Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS

Learn problem solving from the ground up, introducing algorithms, flowcharts, and pseudocode before transitioning to Python for beginner data science.

Experience live coding in Python, where concepts are coded and run in the video, with problem solving practice sessions and COVID-19 data analysis from Kaggle using data science packages.

Explore turning problem solving into a universal solution by defining an algorithm. Translate steps into pseudocode and implement them in Python.

Explore expressing algorithms with flowcharts and pseudocode, using a pay calculation example to show inputs, variables, and steps, and discuss when pseudocode is preferable for easy code conversion.

Compare flowcharts and pseudocode to build general solutions, then convert to Python code. Use the make tea problem to illustrate loops, input variations (sugar, milk), and iterative testing of conditions.

Discover a procedural method to find a list’s minimum: initialize min, iterate with a counter, compare elements (L of i), update min when smaller, and return the minimum value.

Extend the min-from-list algorithm to return the minimum value and its position using a while loop, and show how a sorting approach builds a sorted list by removing the minimum.

Convert sorting pseudocode into Python, covering selection sort, minimum value, and zero-based indexing. Learn Python syntax basics, including def, colon, and indentation, while exploring Python’s simplicity and built-in capabilities.

Discover why Python is the default data science language, with readability and open source libraries. Follow a beginner-friendly path from zero to advanced Python use for data science.

Explore why Python and Jupyter notebooks are favored for data science. Learn how Jupyter combines code, testing, and visualization in an interactive, web-based environment with markdown and LaTeX support.

Learn how to install Python using the Anaconda distribution, set up the 64-bit Windows version, launch the Anaconda Prompt, and run Jupyter Notebook to start Python coding.

Learn to write your first Python program—hello world—in the Jupyter notebook interface, using print in code cells and markdown cells, and run code with shift enter.

Explore how to use the IPython shell and the Jupyter notebook within Anaconda, write Python code like a calculator, and introduce variables for storing and reusing results.

Explore how variables store data, assign values, and dynamically determine types in Python, covering integers, floats, strings, and complex numbers, with memory management and multiple assignment.

Master Python arithmetic operators—add, subtract, divide, remainder, multiply, and power—and how they apply to integers, floats, and strings. Learn up casted behavior and result underscore usage in the Jupyter Notebook.

learn Python variable naming rules, including that names cannot start with digits or most special characters. underscores are allowed, and descriptive camel notation improves readability.

Explore the bool data type in Python, its true and false values, and how and, or, not control decision making in programs.

Explore the boolean data type and comparison operators in Python, including and, or, not, and equals and not equals, with hands-on Jupyter exercises demonstrating true/false results.

Learn how Python evaluates comparisons and booleans with operators like ==, !=, <, <=, >, >=, and how to assign results in variables during a Jupyter Notebook demonstration.

Measure how boolean values combine with comparisons using and, or, and not in Python. See how these results drive control flow and if conditions in practical decision making.

Learn how the python round function converts floating point numbers to the nearest integer or a set of decimals. Examples include 4.6 becoming 5 and 4.3 becoming 4.

Learn how Python's divmod returns a two-element tuple with the quotient and remainder for two inputs, with examples and indexing of the resulting tuple in a Jupyter Notebook.

Learn how Python's isinstance checks whether a value belongs to a type, with examples for int, float, and complex, and how pow computes powers, including modulo with three arguments.

Learn how the Python input function captures user input as a string, converts it to numbers with int or float, and handles errors with validation and exception handling.

Use Python if statements to compare two user-provided numbers and print the larger one. Explore the role of boolean comparisons and the basic structure of if blocks.

Explore Python control flow using if, elif, and else to compare two numbers, print the greater value, and build complex if-elif-else sequences with proper indentation.

Explore python conditional logic with if-elif-else and the short form. Compare readable horizontal structures to longer forms and apply these concepts to a grade scoring example in a Jupyter notebook.

Explore nested if conditions in python, where an if body contains another if. Understand how indentation defines blocks and how top-level and nested else parts shape outputs.

Practice Python control flow with if statements and comments, solving a real-number task by extracting the integer portion before the decimal and checking if it is even or odd.

Explore control flow in Python with while loops, printing numbers from 1 to n, updating with i += 1, and using loop conditions to repeat tasks and compute squares.

Learn control flow in Python with the while loop, inner ifs, nested loops, and break and continue statements that control iteration and exit conditions.

Learn how to use the Python for loop to populate lists, iterate with range, and compute squares, while applying indexing and append operations.

Explore how Python's for loop else clause works and when it executes. Demonstrate with sets, dictionaries, and data structures, and practice for and while loops in a Jupyter notebook.

Practice for loops with a selection sort that sorts a list from minimum to maximum, swapping the minimum with the current position and tracking its index in Python.

Learn how Python functions encapsulate tasks with def and descriptive names. Call the function to run its body, and see this workflow demonstrated in a Jupyter Notebook.

Discover how Python docstrings describe a function, keep a description accessible as help without execution, and how three quotes capture multi-line documentation.

Explore how Python functions respond dynamically to input arguments, using a print_message example, type checks, and the document string to guide behavior and reuse across calls.

Explore defining Python functions with multiple input arguments, including a power function, using dynamic typing, docstrings, and type checks (isinstance for int or float) to validate inputs and perform calculations.

Explore defining and calling Python functions with multiple arguments, and learn how argument order matters and how using parameter names makes calls order-free.

Learn how the return statement exposes a function's computed value to the caller and isolates local variables from outside scope, enabling subsequent processing.

Explore Python variable scope, access function outputs with return statements, and distinguish local versus global variables. Learn about None returns and returning multiple values.

Learn to build a universal add function in Python that accepts any number of arguments with *args, treats them as a list, sums them, and returns the result.

Learn how to design Python functions that accept an arbitrary number of keyword arguments using double star syntax, treat them as a dictionary, and process each key-value pair.

Learn how default values in Python functions are assigned at definition time and remain fixed, while mutable types like lists can share memory and influence subsequent calls.

Learn how to create and use Python modules to organize and reuse functions across projects, importing modules and calling functions as if they were in your code.

Learn how to create and use Python modules and packages, import modules with sys.path adjustments, and organize code with files, directories, and reusable functions for data science projects.

Practice building and using Python functions to sort a list, including find minimum and its index, swap values, and composing functions in a module for clearer code.

Explore strings as a data type in Python. Learn to declare with single or double quotes, concatenate strings, and convert non-string types to strings for messages.

Learn how to declare and print multi-line strings in Python using triple quotes, and use them for formatting and comments. Explore indexing characters and spaces for formatting in Jupyter Notebook.

Master Python string indexing and slicing, including zero-based and negative indices, end not included, and steps, while noting strings are immutable and reversing with [::-1].

Explore common Python string methods like strip, lower, upper, replace and split to clean and transform text, using dot notation on string objects and tab-completion for discovery.

Explore Python strings, using in and not in to test substrings. Compare strings, understand equals and ordering, and learn escape sequences, quotes, and raw strings.

Explore core Python data structures: list, tuple, set, and dictionary, highlighting their heterogeneity and use cases, with comparisons on mutability, order, and performance considerations including NumPy.

Explore Python data structures—list, tuple, set, and dictionary—defining them in notebooks, accessing elements, and comparing behaviors like ordering, duplicates, and key-value pairs.

Explore how to index and slice lists and tuples, understand mutability and memory implications, and perform insertion and deletion across lists, tuples, sets, and dictionaries.

Explore Python data structures in practice: list append and plus usage, tuple immutability and concatenation, set add and update, and dictionary insertion and deletion with del in Jupyter.

Plus operator cannot concatenate dictionaries; use the update method instead. Copy behavior: assignment shares memory, while copy yields independent lists, sets, and dictionaries; slicing often copies by default.

Explore lists, tuples, sets, and dictionaries via tab completion and help, and learn list methods like append, clear, pop, reverse, plus set and dictionary operations such as update and union.

Explore the abstract nature of data structures—lists, tuples, sets, and dictionaries—through nesting, indexing, and accessing inner elements, and practice building and iterating complex structures with loops.

Build a data entry workflow using lists and dictionaries to compute each student's average marks. Input ids and comma-separated marks, avoid duplicates, convert to integers, and display the averages.

Explore numpy, a fast numerical Python package, and learn how arrays store homogeneous numeric data efficiently. Create arrays from lists or tuples, inspect dtype, and use numeric types.

Explore numpy array properties like dtype and ndim, and how dimensions govern element access in 1d, 2d, and 3d arrays and arrays of arrays.

Explore how numpy shape property and ndim describe dimensions using a 3d array example, and learn about size, nbytes, and universal (vectorized) functions.

Explore core NumPy array creation and manipulation, including zeros and ones, arange and range behaviors, random permutation and rand int, and the reshape operation, with hands-on examples.

Explore NumPy's random package to generate uniform and Gaussian numbers, reshape with arange into 2 by 5 matrices, and test algorithms with histograms via Matplotlib.

Discover how numpy slicing accesses a memory view versus creating a copy. Explore indexing in 1d and 2d arrays, and learn practical examples of reversing and extracting subarrays.

Locate elements with argwhere, index two dimensional arrays, and slice submatrices; access rows, columns, and transpose matrices. Apply numpy linear algebra for inverses, determinants, eigenvalues, and axis-based sorting.

Learn NumPy masking and advanced indexing, including index arrays and boolean masks to select elements, with a copy-versus-view distinction and conditional filtering.

Master numpy broadcasting to align array shapes in arithmetic, and use horizontal and vertical stacking, concatenate, and vectorized universal functions for fast data processing.

Explore the speed of NumPy universal functions (ufuncs) for numerical data processing, demonstrating how vectorized operations outperform Python loops on large arrays.

Explore how the pandas data science package enables powerful data manipulation, cleaning, and pre-processing, built on numpy. Create and use series and dataframe objects with custom indices, values, and slicing.

Explore Pandas series by creating a series from dictionaries to map grades and marks, using explicit and implicit indices and slicing, then introduce data frames as multi-dimensional extensions.

Explore building and manipulating Pandas data frame from series and dictionaries, including indexing, transposing, adding or deleting columns, masking with conditions, and handling missing values.

Learn how to handle missing values in pandas data frames, including nan and none types, with fillna or dropna, and compare fixed-value fills to mean or regression imputation in scikit-learn.

Master pandas indexing by using .loc for explicit indices and .iloc for implicit indices to slice series and dataframes, aligning with numpy-like indexing.

Explore pandas data manipulation on a covid-19 dataset by loading csv, cleaning columns, converting dates, and handling missing values with simple imputer, while inspecting data with describe and info.

Learn to manipulate covid-19 data in pandas by grouping by country and date, summing confirmed, deaths, and recovered, and filtering records.

Explore matplotlib basics with pyplot for creating line and scatter plots, using numpy linspace, customizing colors and labels, and generating multiple curves in a single figure.

Visualize covid-19 trends with matplotlib, pandas, and numpy, plotting deaths, confirmed, and recoveries by country and the world, using groupby, imputer, and scatter plots.

Compare matplotlib and seaborn for data visualization, showing seaborn's high level interface with pandas data frames and its ability to produce stylish, easy-to-read plots.

Explore how Seaborn simplifies visualizing distributions with histograms and kernel density estimates, and compare its concise plots to Matplotlib’s code-heavy approaches for data frames.

Explore seaborn's joint plot and pair plot to visualize two-dimensional distributions and pairwise relationships, using kde options, axis styles, and color-coded classifications for multi-attribute data.

Visualize iris data with Seaborn pairplot to compare distributions of sepal and petal attributes across setosa, versicolor, and virginica. Preview interactive plots via a visualization library in the next video.

Discover bokeh for interactive, browser-based data visualizations saved as HTML, with zoom, pan, grid plots, and multiple styles, contrasting with static seaborn and matplotlib outputs.

Learn to build a grid of interactive subplots in bokeh using gridplot from bokeh.layouts, combining multiple figures on a single html document with configurable widths.

Explore scikit-learn, a popular Python library for machine learning, and learn to generate data, split training and test sets, and apply algorithms like linear regression, SVM, and random forest.

Explore scikit-learn's linear regression for fitting a best line to synthetic regression data, including data preparation, model fitting, prediction, and visualization with matplotlib and seaborn.

Master scikit-learn workflows by generating synthetic data with makeblobs, training SVM and random forest classifiers, and evaluating accuracy to compare model performance.

Explore scikit-learn workflows for covid-19 trend analysis using matplotlib, pandas, and numpy; impute missing values, plot country and global death, confirmed, and recovered trends, and discuss basic prediction approaches.

Celebrate your successful completion of this data science and machine learning course with a thank you bonus video on scikit-learn, and explore more beginner friendly courses while leaving a review.

The course uses engaging real world datasets and synthetic data to teach data manipulation, visualization, and function explanations in Python, including image data, Kaggle challenges, and coverage of Python packages.

Explore strings, dictionaries, and core data structures; master NumPy, pandas, matplotlib, Seabourne, Bukit, likely in flood, and Volume for interactive and geographic visualizations.

Explore how the Udemy review system works, preview upcoming topics and real-world concepts, and learn how your honest feedback helps shorten the learning curve and improve course content.

Learn how numpy universal functions, including add, sum, and plus operators, enable fast numerical data processing, handle nan values, and manage shapes with new axis and squeeze.

Explore NumPy universal functions and operators for efficient, element-wise data processing, including subtract, multiply, power, and remainder, and understand how aggregates like mean and std reduce dimensions.

Explore universal functions in NumPy, including shape changes with axis, missing values, trigonometric functions, comparison operators, and boolean logic, plus nan-safe methods for robust data processing.

Practice numpy ufuncs, comparisons, and logical operators by building a 50-element array multiplied by 10, removing decimals, filtering values greater than five, and sorting ascending and descending.

Explore NumPy for numerical data processing by creating arrays with np.random.rand, removing decimals via floor, filtering values greater than five, and sorting arrays in ascending and descending order.

Learn how NumPy ufuncs use the out parameter to write results in place, avoiding temporary storage and speeding operations on large arrays.

Explore NumPy-based image processing by treating images as RGB arrays, manipulate red, green, blue channels, read and display images with Matplotlib, and flip images using array indexing.

Learn numpy image processing by drawing a blue box on a bird image, with blue 200 and red and green zero, at coordinates 1000–2000 x and 100–500 y.

Learn how to use NumPy for numerical data processing by creating and displaying a blue box, setting the blue channel to 200 within specified x and y axes.

Learn to build a k-nearest neighbors classifier from scratch in numpy, compute Euclidean distances with broadcasting, and evaluate on iris data using a custom train-test split.

Explore numpy's ability to store heterogeneous data using structured arrays, defining fields like name, id, marks, and GPA; see how numpy structures underpin pandas and enable field-based indexing.

Learn to build a NumPy structured array with four fields: employee type (permanent or non-permanent), age (integer), birth year (string), and salary (integer), and populate three entries.

Explore creating NumPy structured arrays with four fields: employee type, age, birth year, and salary, and learn to define dtypes, populate entries, and access each field for data processing.

Explore Pandas for data manipulation and cleaning, built on NumPy, and learn to work with Series and DataFrames, creating them from lists or dictionaries with custom indices.

Create a pandas series from a grades dictionary to map GPA values and marks. Explore explicit versus implicit indices and slicing, and preview data frames as multi-dimensional structures.

Learn to create and manipulate pandas data frames from series or dictionaries, access and modify columns, transpose, and apply masking while preparing for missing value handling.

Build a two-column pandas data frame with name (string) and vaccine (boolean), populate five entries, then create a new data frame with names where the vaccine status is true.

Create a pandas DataFrame from dictionaries with name and vaccinated fields, then filter to fetch names with true vaccination status.

Explore how pandas represents missing values as NaN or None in a dataframe and how to fill or drop them to manage gaps.

Explore how pandas loc and iloc differentiate explicit versus implicit indexing in series and dataframes, with practical examples for selecting, slicing, and reversing rows using numpy-like indexing.

Learn to load and preprocess a mixed data file with pandas: read_csv, header handling, mapping labels to numeric, and one-hot encoding for a binary income classification.

Learn how pandas group by splits data into groups by product or type. Apply aggregates such as sum and mean, and analyze multiple attributes within a data frame.

Extend the previous pandas data frame by adding a country column with entries Pakistan and China. Group the data by country and calculate the sum to practice grouping in pandas.

Explore data manipulation with pandas by creating a country field and grouping data by country to summarize vaccination results using the groupby function.

Learn to manipulate data with pandas using hierarchical indexing and multi-index structures to group by product and type, aggregate energy consumption, and read csv data.

Master pandas rolling windows to perform windowed aggregates on dataframe columns, using window size, minimum period, and options like mean or sum, including Gaussian windows to smooth data.

Demonstrates creating a pandas data frame and applying a three-value rolling window, summing each window, stepping by three, and excluding NaN values.

Learn to create a pandas data frame and apply rolling with a three-value window to sum values, while excluding NaN entries and validating the results.

Demonstrate how pandas where keeps original values where a condition is true and applies an action to all other locations, with indexing alternatives and practical examples.

Use the clip function to constrain Pandas data frame values within a lower and upper threshold. Compare this with indexing and understand when to use universal functions.

Practice using pandas clip to bound a data frame between 10 and 30, replacing values below 10 with 10 and above 30 with 30 using the where method.

Learn how to use pandas clip to constrain data in a data frame by replacing values below ten with ten and values above thirty with thirty.

Learn how to use pandas merge to join data frames by common keys, explore inner and outer joins, and handle many-to-many relationships with practical examples.

Learn to merge multiple data frames in pandas to consolidate student records using the pandas merge function, with practice through a merge quiz.

Learn how to use pandas merge to combine two data frames into a single data frame, preserving records from both and creating a unified dataset.

Learn to use pandas pivot tables to create multi-dimensional summaries that extend group by, aggregating survival by gender, class, and age or fare partitions in the Titanic dataset.

Explore pandas string methods to manipulate data frames with vectorized operations, including lower, capitalize, cat, split, length, and null handling, demonstrated on the Titanic dataset.

Convert mixed date formats in pandas data using pd.to_datetime to standardize a date column, then perform date arithmetic with day, period, and business-day frequencies, differences in seconds or nanoseconds.

Apply pandas to a covid-19 csv dataset, clean and reformat columns, handle missing values, and compute country- and date-level totals for confirmed, deaths, and recovered cases.

Fix a datetime import bug by distinguishing the datetime module from the datetime function, enabling clean data handling for covid19 data ahead of visualisations.

Master the basics of data visualization with Matplotlib as the foundation. Explore Seaborn and Bokeh, import pyplot as plt, and try styles like classic and Seaborn White Grid in notebooks.

Generate data with NumPy and visualize it with Matplotlib, plotting sine and cosine curves and multiple plots on a figure. Learn to switch styles, including Seaborn, using plt and ax.

Explore coloring and styling Matplotlib plots using color names, shorthand letters, and RGB tuples. Adjust figure size and line styles, and apply Seaborn styles to enhance graph readability.

Explore Matplotlib capabilities through a colors and styles quiz by plotting y versus x with 1500 points, orange dashed line, and specified figure size and style.

Visualize data with matplotlib using the seabourne style, set a six-by-four figure, define x and y data, and plot an orange dashed line while exploring colors and styles.

Use concise Matplotlib shortcuts to set colors and line styles, like '-' for solid and '--' for dash, then adjust line width, markers, and consult the documentation for more properties.

Master axis limits and visualization controls in matplotlib, including setting xlim and ylim, using plt.axis and axes objects, and reversing or tightening axes for precise, high resolution plots.

Set x axis limits from 1 to 14 and y axis limits from -1 to 1.2 on a seaborn plot, then reverse the y axis.

Master how to set matplotlib axis limits using x from 1 to 14 and y from -1 to 1.2, then reverse the y axis by swapping y limits.

Annotate figures with labels, titles, and axis descriptions in Matplotlib to make plots descriptive; learn legends for color-coded curves, and place them precisely for clearer data visualization.

Explore using the axes object (ax) versus pyplot (plt) to annotate and label plots in matplotlib. Use ax.set to configure x and y labels, limits, and the title efficiently.

Explore matplotlib set functions to customize a plot and add x and y labels, reinforcing how these functions work in data visualization.

Trace the Matplotlib workflow by defining x and y values, applying xlim and ylim, labeling axes, setting a title, and reversing the y-axis by swapping y limits.

Explore the wide range of Matplotlib markers for data visualization in the plot function, including plus, triangles, squares, diamonds, and dots, and learn how marker size and color affect readability.

Explore how matplotlib markers behave under seaborn styles and how switching to a classic style fixes missing markers, then test multiple markers with random data and line styles.

Create and customize scatter plots with matplotlib using plt.scatter, adjusting colors, sizes, alpha blending, and color maps with a color bar to distinguish iris species.

Visualize two-variable functions with contour plots by using numpy meshgrid to generate an x-y grid and compute z, then tailor color maps, intervals, and filled versus unfilled contours.

Generate 50 values for X and 80 values for Y from 5 to 10, compute Z with function, and render a contour plot using Furneaux color map with 40 intervals.

Learn to create a matplotlib contour plot by defining x and y grids, computing z with a function, and applying a 40-interval inferno color map.

Explore how Matplotlib's hist function visualizes data distributions through histograms and density plots, comparing multiple datasets with customizable bins, alpha blending, and step-filled histograms, using iris data as examples.

Learn to create a figure with multiple subplots arranged in a grid using Matplotlib, sharing axes and visualizing images and faces.

practice creating an eight-subplot grid in matplotlib with two rows and four columns. use cool and warm color maps to reinforce subplots concepts through a hands-on quiz.

Learn to create matplotlib subplots in a 2-by-4 grid with a 4-by-3 figure size, iterating with nested loops and experimenting with color maps like cool warm to enhance visualization.

Learn to create three dimensional plots in matplotlib, including parametric curves, scatter plots, surface plots, and contour plots, using mpl_toolkits.mplot3d and a 3d projection.

Explore 3d data visualization with matplotlib by setting up a 3d scatter plot, generating random X, Y, Z data with numpy, and customizing color, markers, and alpha.

Practice creating a 3d scatter plot using Matplotlib, with specific x, y, z data, green dot marker for the first plot and yellow a-plus marker for the second, following guidelines.

Explore creating three-dimensional scatter plots in matplotlib, adjusting the projection to three-dimensional, setting X, Y and Z coordinates, colors, and markers, and reviewing a step-by-step solution to a quiz.

Explore creating and customizing 3d surface plots with matplotlib's mplot3d, using plot_surface on X, Y, Z data, color maps, alpha, and meshgrid examples; and preview seaborn for high level plots.

Explore seaborn, a high-level visualization library built on matplotlib, and learn quick, expressive plots, from histograms and kernel density estimates to joint plots and pair plots using iris data.

Explore data visualization with seaborn by building scatterplots and pair plots using the iris dataset, customizing with hue, style, and markers to distinguish species.

Explore seaborn relplot fundamentals through a hands-on iris dataset quiz, creating a relationship plot with x and y variables and species as the style.

Create a Seaborn relationship plot using relplot with x and y from a dataset, style by species, and enable markers to visualize data relationships.

Explore seaborn's relation plots with relplot to create line plots of iris data, showing sepal length versus sepal width with hue by species and style variations.

Learn to create Seaborn relplot visualizations, plotting total bill vs tip by smoker and time with hue and style, then apply to fmri data showing time vs signal by subject.

Explore seaborn relplot facets with a practical quiz, mapping x to size and y to total bill, and faceting by smoker category to compare groups.

Explore Seaborn relplot facets to visualize the relationship between size and total bill, dividing by smoker status and using hue and style for gender.

Explore Seaborn catplot to visualize tips data by day, revealing how to tune jitter, hue by gender, and compare box, violin, swarm, and bar styles.

Explore Seaborn heat maps to visualize data matrices and confusion matrices, adding annotations and sizing controls to reveal value intensity and distribution in machine learning datasets.

Explore Bokeh for interactive plotting, contrasting it with static Matplotlib and Seabourne plots, and learn to create interactive figures with browser-based output, tools like zoom and save.

Embed all plots in the same notebook for browser-based interactivity with bokeh, and plot multiple series with circle and triangle markers and legends.

Explore Bokeh's grid plot for arranging multiple interactive subplots, enabling interaction across whole grids and individual charts, and compare with limitations for 3D plots and alternatives like Plotly.

Practice building four Bokeh plots in a grid using given x and y coordinates, with specific markers and labels, and render the output in a notebook.

Learn how to create four interactive plots in a Bokeh grid plot, using line, circle, triangle, and square markers with colors and titles to compare x and y functions.

Explore 3d interactive plotting with Plotly, building a 3d scatter plot in Jupyter notebook, and customize traces, layout, markers, and color scales.

Create a 3d interactive scatter plot quiz in plotly, using z values from -10 to 20 (70 points); set x as the sign of z and apply solar color scale.

create a 3d interactive scatter plot using Plotly by generating 70 values from -10 to 20 and mapping x, y, z with color scales.

Create an interactive 3d surface plot with Plotly by building a numpy mesh grid and a cos(x^2+y^2) function to generate X, Y, Z.

Learn to build a Plotly 3D interactive surface plot using x values from 20 to 60 with 40 samples, paired with y and z, and the ice fire color scale.

Explore building a three-dimensional interactive surface plot with Plotly by generating x and y grids, computing z values, and applying a color scale to enhance visualization.

Explore geographic data visualization with folium by mapping covid-19 data on a globe using latitude and longitude, with circle markers scaled to confirmed cases.

Create geographic maps with folium using covid-19 data, compare maximum and minimum dates, and adjust the radius by the number of deaths to analyze outcomes.

Explore geographic maps with Folium to visualize COVID-19 data by selecting the minimum date and mapping the number of deaths with radius and color settings.

Discover how Pandas' built-in plotting module, built on Matplotlib, lets you create histograms, area plots, and more directly from data frames with df.plot, alongside seaborn, bokeh, and plotly.

Master pandas for plotting through a concise bonus video, reinforcing practical data skills and encouraging thoughtful reviews to help future learners.

Explore the fundamentals of probability and statistics with Python, linking theory to practical machine learning models. Build from sets and experiments to random variables and data-driven model construction.

Understand how the Udemy review system works and how your honest feedback after exploring the course can drive updates and help future learners.

Learn the difference between probability and statistics: statistics analyzes past data to derive rules, while probability uses these laws to predict future events and likelihood.

This lecture defines a set as an unordered collection of distinct, well-defined objects, without duplicates or ordering, often written with curly braces, and highlights its role in probability and statistics.

Examine the definition of a set and determine if a set can include heterogeneous objects, using integers and strings as examples to analyze set theory basics.

Define the concept of a set as a collection with distinct, unordered elements, capable of containing heterogeneous objects in theory, though practical usage favors separate sets for different types.

Define the concept of a set and explain the difference between a set and a multi set, encouraging online research to deepen understanding.

Explore data science concepts of set, multisite (multiset), and topal, showing distinctness and unorderedness: sets have no duplicates and are unordered; multisite allows duplicates but is unordered; topal has neither.

Explore sets and elements, membership notation, and the empty set; distinguish finite and infinite sets by cardinality, and contrast countable versus uncountable sets with examples.

Explore subsets, including the empty set and self-contained sets, then define the power set and universal set and how all subsets relate to a parent set.

Practice sets in python by defining sets A and B, checking membership and subset relations, implementing a custom issubset function, and exploring power sets for deeper understanding.

Generate all subsets of a set with a Python function that uses binary indexing and NumPy boolean arrays. Demonstrate that the power set has 2^n elements using {1,2,3}.

Explore set operations—union, intersection, difference, and complement—along with De Morgan's laws, partitions, the universal set, and Python demonstrations using Venn diagrams.

Explore union and intersection of a set with an empty set, and understand how to denote the empty set in set operations.

Investigate set operations by applying union with the empty set, which returns the original set without duplicates, and intersection with the empty set, which yields an empty result.

Explore set difference and the empty set through hands-on practice, computing expressions like A minus five and fi minus a using A = {1, 9, 7, 13}.

Apply the set difference to keep elements from the first set not found in the second. If the second set is empty, all first set elements appear.

Explore two-set partitions of a 10-element set into nonempty disjoint subsets B and C whose union is the whole set, and compute how many distinct partitions exist.

Solve a counting exercise by partitioning 10 elements into two sets of varying sizes, using combinations (10 choose k). The lecture clarifies counting methods and connects to probability theory.

Explore python set operations with numpy to perform union, intersection, difference, and complements, verify de Morgan's laws, check isdisjoint, issubset, issuperset on A and B within a universal set.

Explore how Venn diagrams visualize set operations—union, intersection, difference, and complement—within a universal set, and see links to probability, subsets, supersets, and partitions.

Practice problems show sets are unordered and distinct, contrast them with ordered collections, and guide you through partitions, the Python is_partition function, and complement identities with randomized trials.

Define a random experiment as a process with given conditions that produces one of several outcomes, where the exact result is uncertain and may vary on repetition.

Define outcomes as results of an experiment and the sample space as all possible outcomes. Use coin toss examples to illustrate finite, infinite, and countable sets and events.

Explore the sample space of a three-roll four-sided die and a single coin toss, identifying all possible outcomes and the structure of the experiment.

Learn to construct and enumerate the sample space for three rolls of a four-sided die and a coin toss, and determine that the sample space contains 128 elements.

Define an event as a subset of the sample space and identify events of interest using outcomes, with examples from two dice and temperature thresholds.

Explore how a 16-element sample space yields as many events as there are subsets, including the empty set. Understand that the total number of events equals 2^16.

Explore how a set's powerset includes all subsets, each a potential event in a sample space; for 16 elements, there are 2^16 events, with the empty set deemed impossible.

Explore how events are modeled as sets and identify disjoint sets, using renaming as a hint to distinguish overlapping and non-overlapping events.

Rename the event as a set to identify disjoint sets, where the intersection is empty, illustrated by event one with {1,3} and event two with {2,4}.

Explore the terminology of experiment, outcomes, sample space, sample point, and event, then attempt homework on a four-sided die, finite versus infinite sample spaces, and even-sum events.

Design sample spaces and assign non-negative probability laws to events, defining a probability model that quantifies likelihoods and enables unambiguous predictions.

Explore the probability axioms, including non-negativity with zero allowed, additivity for disjoint events, and that the sample space has probability one, with a fair die example.

Derive the probability axioms from the sample space and events, showing that P(A complement) = 1 − P(A) and P(A ∪ B) = P(A) + P(B) − P(A ∩ B), with disjoint and subset implications.

Explore whether an empty seat, as an event, can have non-zero probability by applying the probability axioms and laws. Solve exercise 01 through axioms-focused reasoning.

Show that an empty event cannot have non-zero probability, based on the probability axioms. Use P(sample space)=1 and P(A∪B)=P(A)+P(B) for disjoint events, and decompose S as S∪∅ to conclude P(∅)=0.

Explore discrete probability models with two dice, define sample spaces and probability laws, and compute events such as even sums and at least one die showing four.

Apply probability axioms and set theory to compute the probability of neither malaria nor typhoid using complements, union and intersection, yielding 0.1 and introducing a continuous probability model.

Compare discrete and continuous probability models by showing countable versus uncountable sample spaces. Explain why single-element probabilities fail in continuous models and why intervals carry probability.

Explore conditional probability, a powerful method for assessing outcomes given partial information, with two dice and medical test examples, and see how information changes likelihood.

Explore conditional probability through a loaded die example, deriving P(A) and P(A|B) for events A and B, and show that A and B are independent when P(A|B)=P(A).

Explore conditional probability using a six-face die example. Derive the rule P(A|B)=P(A∩B)/P(B) and note independence when A's likelihood does not change with B, normalizing by P(B) as the sample space.

Explore how conditional probability underpins machine learning, modeling distributions of random variables for tasks like face recognition, activity recognition, and text-to-speech, linking statistics to classifiers and regressors.

Explore the law of total probability, linking joint and marginal distributions, and see how disjoint partitions of the sample space enable reliable probability decomposition for real data and random variables.

Explain the concept of independence in probability, including statistical independence, conditional probability, and A given B, A intersection B, and independence across multiple events and subsets.

Analyze how independence and conditional independence shape probability models, proving that A not depending on B implies B not depending on A, and introducing Bayes rule and naive Bayes classifiers.

Explore probability models and conditional independence through an exercise that shows A and B are dependent, then become conditionally independent given C, using three events A, B, and C.

This lecture uses a coin-box example to show that events A and B are dependent overall but become independent when conditioned on C, illustrating conditional independence in probability models.

Master Bayes rule and Bayes theorem as the foundation of probabilistic classification. Learn how class conditional distribution, prior, and marginal integrate with generative and discriminative modeling in supervised learning.

Explore how real data is represented as random variables and use probability theory to model distributions, apply Bayes' rule, and build data-driven classifiers from training data.

Build a probability model with a four-sided die and two coins, assuming all outcomes are equally likely, then compute the probability of an even roll and two heads.

A random variable is a function of an experiment's outcome that maps outcomes to numbers, illustrated by dice sum, maximum, and prime number, with probabilities defined for the variable values.

Define discrete random variables from a two-roll experiment, such as the sum and the maximum, map values to events, and introduce the indicator for prime sums and probability mass function.

Explore how a zero probability for a specific value of a random variable relates to empty or impossible events, using practical examples.

Explore how random variables handle events X = a, distinguishing discrete and continuous cases where continuous variables have zero probability yet may not imply an empty event.

Explain discrete random variables and the probability mass function that assigns probabilities to each value. Introduce Bernoulli random variables with two outcomes using fair and biased coin tosses.

Implement a Bernoulli trial in Python using NumPy to simulate coin tosses, estimating the probability of success by counting heads and tails across many trials.

Assess whether the next ball outcome can be modeled as a Bernoulli trial, a type of random variable, with six as the success leading to team A's win.

Model experiments with random variables by assigning binary outcomes to events, such as X for a win and Y for a six, with flexible probabilities.

Explore independent Bernoulli trials and build a geometric random variable by counting tosses until the first head, deriving its pmf from a biased coin with p=0.7.

Explore the geometric random variable from independent Bernoulli trials, modeling the number of tosses until the first head, and verify its normalization for infinite, discrete outcomes.

Build and simulate geometric random variables in Python using Bernoulli trials, exploring the distribution by generating many trials, adjusting probability of success, and visualizing with histograms.

Explore binomial random variables from independent Bernoulli trials, counting heads in n coin tosses and the binomial pmf with p. Preview a Python exploration of binomial trials.

Learn to implement a binomial trial in Python with a vectorized numpy approach. Plot histograms to visualize how n and p shape the Bernoulli/binomial distribution and its mean.

Explore real data as random variables, linking discrete and continuous types. Model joint and conditional distributions, pmf, and Bayes rule for classification and regression.

Explore discrete random variables beyond binomial and geometric, encouraging students to identify and analyze other famous discrete distributions in real datasets.

Explore the Poisson distribution as a famous discrete random variable with a probability mass function driven by lambda, its relationships to binomial models, and Gaussian approximations for real data.

Analyze a four-sided die rolled three times to study the maximum as a discrete random variable. Compute the uniform pmf and the probability that the maximum is even.

Explore continuous random variables on the interval zero to one, where outcomes are uncountable and individual values have zero probability; learn interval probabilities and introduce probability density functions.

Determine if x, the midpoint of a randomly selected interval from a subdivided line segment, is continuous, given six disjoint intervals with known lengths and a six sided die.

Demonstrates that the midpoint of randomly chosen six disjoint intervals yields a discrete random variable, not continuous, due to a finite set of possible values.

Learn how probability density functions model continuous random variables by linking interval probabilities to areas under the curve, distinguishing density from probability mass and ensuring total area equals one.

Explore the properties of a probability density function for a continuous random variable X and identify all criteria that define a valid probability density function.

Identify the key properties of a valid continuous pdf: non-negativity, normalization with total area under the curve equal to one, and allowance for X to be negative or positive.

Explore continuous random variables with the uniform distribution on [10,30], derive the density 1/20, and compute probabilities as areas; learn to generate uniform numbers in numpy using np.random.rand.

Explore how a fair six-sided die illustrates a uniform random variable, with each outcome equally likely, and analyze whether X from rolling the die is uniform.

The lecture shows that the dice roll variable x is a discrete uniform random variable with six outcomes and probability 1/6, not a continuous uniform.

Generate and visualize independently generated uniform random numbers with numpy to illustrate a 0 to 1 distribution that scales to 0–100 and shifts to 20–120.

Explore the exponential distribution as a continuous random variable with density lambda e to the minus lambda x for x greater than zero, where lambda is a parameter shaping curve.

Analyze how the exponential distribution changes with lambda, the arrival rate (lambda > 0), comparing values like 0.5, 0.7, and 5.9 and its density behavior.

Explore how varying lambda changes the exponential distribution, showing that higher lambda yields faster decay and a larger peak, while smaller lambda yields slower decay.

Practice the exponential distribution by plotting its density function for varying lambda; observe that larger lambda sharpens decay while the area under the curve remains one.

Explore the Gaussian (normal) random variable, its real-valued range, and the Gaussian density with mu and sigma; see how mu shifts the peak and sigma controls the decay.

Explore how the Gaussian distribution reacts to changes in sigma, comparing large versus small sigma, in this exercise on continuous random variables for data science and machine learning.

Explore how sigma and variance shape Gaussian distribution: large sigma flattens the bell curve, while small variance sharpens it. Relate this to exponential distribution and other distributions' parameter impacts.

Learn to generate data from Gaussian in Python, visualize the resulting distributions with histograms, and see how varying mu and sigma shifts and sharpens the normal curve.

Learn how continuous and discrete random variables transform into new features to improve a probability model and a classification model, with examples like height, weight, dice, and dimensionality reduction.

Explore the cumulative distribution function (cdf), its usefulness, and how it relates to both discrete and continuous variables. This homework guides you to research its definition and applications.

Explore the definition of expectation as the mean of a random variable, covering discrete and continuous cases, pmf and pdf, with Bernoulli examples and Python demonstrations.

Generate data from Bernoulli, geometric, and binomial distributions and compute the sample mean to reveal its connection to distribution parameters. Learn about expected value and law of large numbers.

Explore the law of large numbers, linking the sample mean to the expected value for iid independent trials, with examples like Bernoulli, binomial, and geometric distributions; see Python demos.

Explore the law of large numbers with iid data, showing how the sample mean converges to the population mean as sample size grows, and review key distributions and their means.

Explore the law of large numbers with iid samples from Bernoulli, geometric, binomial, and normal distributions, showing the sample mean converges to the expected value as sample size grows.

learn how transforming a random variable affects its expected value and how to compute moments and variance using the original probability mass or density function.

Solve for the expected value, variance, and the fourth moment of the maximum of three rolls of a four-sided die, assuming a uniform pmf for the random variable.

Build a from-scratch bayes classifier on iris data, using petal length as the feature, with gaussian class-conditional estimates and a train-test split.

Explore how to model data with multiple random variables using joint distributions, including joint pmf and density, marginal distributions, the law of total probability, and expectations.

Derive the expectation formula for two discrete random variables X and Y from their joint pmf, and extend the approach to continuous distributions by replacing sums with integrals.

Compute the expected value of z = x + y using the joint pmf, showing how linearity distributes over the sum to yield the marginal distributions of x and y.

Compute the expected value of a binomial random variable X with parameters n and p, where n is the number of trials and p is the probability of success.

View the binomial random variable X as a sum of independent Bernoulli trials and apply linearity of expectation to obtain its expected value, E[X] = n p.

Compute the expectation of the product for two independent discrete random variables X and Y using their joint pmf, showing that E[XY] = E[X] E[Y].

Explore how independent random variables have a joint distribution equal to the product of their marginals, and show that E[XY] = E[X] E[Y] for independent variables.

Explore the multivariate gaussian as a key joint distribution, using the random vector to describe multiple variables and its density with mu and the covariance matrix C.

Explore conditioning in random variables, including discrete and continuous cases, joint and marginal distributions, and independence vs conditional independence, with applications to Naive Bayes classification.

Explore the general classification problem, predicting the finite discrete class Y from data X by a probability model, and compare generative modeling with discriminative modeling.

Build a Naive Bayes classifier by applying the conditional independence assumption to model y given x1 and x2, estimating joint density as the product of x1|y and x2|y, typically Gaussian.

Explore how regression predicts a continuous target y from multiple random variables X using conditional densities and expected values, and examine the curse of dimensionality and potential workarounds.

Understand how the curse of dimensionality makes joint probability estimation difficult as the number of random variables grows, demanding more data and tools like principal component analysis for dimensionality reduction.

Implement a from-scratch naive bayes classifier in python using iris data from seaborn, modeling each feature independently given the class and multiplying to form the joint distribution, with train/test split.

Learn how parametric distributions are described by parameters like mu, sigma, and lambda, estimate them from data, and compare with non parametric approaches using kernel density estimates.

Estimate distribution parameters with maximum likelihood from iid samples by maximizing the joint probability, yielding the maximum likelihood estimates, and note that ML minimizes KL divergence.

Learn maximum likelihood estimation with an exponential distribution to estimate lambda from iid samples; maximize the log-likelihood, n log lambda minus lambda times the sum s, giving lambda = n/s.

Apply maximum a posteriori estimation by treating parameters as random with priors, yielding a MAP posterior for lambda and linking regularization to better generalization.

Explore logistic regression as a powerful binary classifier grounded in maximum likelihood on Bernoulli variables, using the logistic function to model Y given X and minimize binary cross-entropy loss.

Learn ridge regression, a powerful regression model that adds a ridge regularizer to ordinary least squares, improving generalization and connecting regression to probabilistic methods.

Explore how deep neural networks model probability distributions by learning layered parameters to estimate the probability of y given x for binary classification.

Delve into counting principles in combinatorics and probability by exploring permutations of distinct objects, showing how n factorial yields all possible rearrangements.

Explore permutations and combinations with and without repetition, derive the npk and n choose k formulas from factorials, and apply to problems like bit strings and teams.

Derive the binomial random variable's pmf from Bernoulli trials and permutations and combinations, showing how k successes in n independent trials form the binomial distribution.

drive logistic regression with maximum likelihood on bernoulli data, modeling success probability via sigmoid, forming the likelihood and log-likelihood to optimize w.

Derive logistic regression through maximum likelihood with Bernoulli outcomes, using a sigmoid probability and cross-entropy loss, and apply gradient descent to find optimal weights.

Explore data science and machine learning concepts through approachable techniques and methods, with emphasis on practical learning for new learners and workplace applications.

Explore machine learning fundamentals without heavy math, using intuitive explanations, Python code, and visualizations to understand driving forces behind models and apply to real-world projects like face recognition.

Learn the fundamentals of machine learning through theory and extensive Python practicals, with live coding on real and synthetic datasets. Explore features, regression, classification, clustering, and from-scratch models.

Explore the Udemy review system, skim remaining sections to see how concepts are made simple, and provide honest feedback to improve the course and help future learners.

Discover how machine learning powers applications like automatic language translation and image recognition, learning translation rules from data and enabling traffic prediction and object localization.

Explore machine learning applications such as speech recognition, traffic prediction, self-driving cars, and fraud detection driven by data. Understand how data availability enables predicting the future and guiding actions.

Machine learning trends rise as algorithms gain high accuracy with vast training data from sensors and devices, enabled by powerful hardware, cloud computing, and software stacks like scikit-learn and PyTorch.

Explore how data drives three learning techniques: supervised, unsupervised, and reinforcement learning, where labels guide the model and unseen data tests its insight.

Explore unsupervised learning by clustering similar objects without labels, and reinforcement learning where an agent learns actions from delayed rewards in an environment to reach a goal, with self-driving cars.

Explore features as the driving force of machine learning, covering feature extraction, transformation, engineering, scaling, and how raw features become training data and datasets for supervised learning.

Generate synthetic data with make blobs to demonstrate features and their structure. Use iris and UCI regression data to show features, class labels, and targets.

Learn regression, a supervised learning task that maps features to continuous targets like house prices. Explore single and multiple targets with synthetic data and a Jupiter notebook.

Visualize regression with one feature, fit and predict with a linear model using scikit-learn, and extend to two-degree polynomial features to capture non-linear mappings.

Classification is a supervised learning task with a finite set of categories as the target, unlike regression's continuous outputs, illustrated by face recognition and vehicle or pedestrian labels.

Explore classification with Python by applying a built-in scikit-learn support vector machine to the iris dataset, loading data, separating features and labels, training, and evaluating predictions.

Group data into similar clusters using unsupervised learning by feature vectors, label the groups, and compare to classification in a practical scikit-learn Jupyter notebook example.

Demonstrate clustering of synthetic data with make blobs, using k means on two features to form five centers and plot unsupervised learning alongside classification and regression.

Learn how image data becomes numeric feature vectors by flattening grayscale pixels or using hog and lbp features, and compare hand-engineered features with convolution and neural networks for learned representations.

Convert video frames into a fixed-length feature vector and use audio feature vectors for machine learning, then assemble a data matrix X with labels y to support supervised tasks.

Convert text attributes to numeric features with one hot encoding and drop uninformative fields. Prepare data for machine learning pipelines with text and numeric data, managing the curse of dimensionality.

Convert text data to numeric with one-hot encoding on the adult dataset using pandas get_dummies for a binary classification task predicting over 50k.

Discover why data standardization improves convergence and stability, and learn scaling techniques like zero-to-one ranges and the standard scalar with scikit-learn examples.

Learn how data from images, videos, audio, and text are encoded into feature vectors, standardized, and analyzed in feature spaces to train classification, regression, or clustering models.

Explore how a model functions as a map from feature space to outcomes, define dimensions, and understand parameters, hyperparameters, and training from data.

Examine how parameters and hyperparameters guide model selection and learning in classification, from linear models to non-linear polynomial forms, and how training estimates parameters within a chosen model class.

Learn how the training process estimates model parameters for a linear model with x1 and x2, mapping inputs to class labels. Discover how error, cost, loss, and optimization guide parameter learning.

Discover how to minimize total error via optimization by estimating parameters a, b, and c using training data and the mean squared error as the guiding loss.

Build a linear regression model from scratch with one feature, estimating a and b via least squares, using numpy on synthetic data.

Build a linear regression model from scratch with one feature in a Jupiter notebook. Generate data, construct X with a column of ones, fit with numpy lstsq, and compare predictions.

Build a minimum-to-mean distance classifier from scratch using two features and Euclidean distance to assign binary labels by proximity to class means, demonstrated in a Jupyter notebook.

Learn to build a minimum distance classifier from scratch with NumPy, using synthetic two-class data, computing class means, and predicting by Euclidean distance. Explore from scratch setup and visualization approaches.

Explore k-means clustering, an unsupervised learning method that groups data into k clusters by assigning points to the nearest mean and re-estimating means through iterations, starting from random initial means.

The lecture demonstrates implementing k-means clustering from scratch by generating a 500-point, two-feature synthetic dataset with three clusters, computing distances to three means, and assigning points to the nearest center.

Explore how higher-degree polynomials increase model flexibility to fit training data exactly. Observe how this creates training error zero and overfitting, harming generalization.

Explore how model flexibility drives overfitting and harms generalization in regression, using polynomial features on noisy data to contrast training versus unseen data.

Explore how model flexibility drives overfitting with small data, define generalization, and introduce regularization—restricting parameter magnitudes to improve generalization in machine learning models.

Measure generalization by holding out a validation set from training data, using an 80/20 split, and evaluating unseen data performance to detect overfitting.

Understand how proper data splits affect generalization, avoid data snooping, and use a three-way split (training, validation, test) to reliably evaluate unseen data and hyperparameters.

Learn cross-validation as a practical validation method to estimate generalization by keeping test data untouched and using training data in 80/20 or k-fold splits to tune hyperparameters and regularization.

Define accuracy and mean squared error as performance measures, explain their limitations, and illustrate why some errors are costlier in medical predictions; preview confusion matrix, precision, and recall.

Study how the confusion matrix reveals misclassifications and true positives in a multi-class setting, and derive precision and recall from it to guide metric choice.

Explore Bayes theorem and probability distributions to connect classification, regression, and generative versus discriminative modeling, then tackle the curse of dimensionality and learn dimensionality reduction with PCA.

Learn how principal component analysis reduces dimensionality from D features to K, easing the curse of dimensionality, stabilizing probabilistic measures, and lowering computation in an unsupervised setting.

Explore deep learning and neural networks, highlighting how deep models use large data to improve accuracy. Review the neural network structure: input, hidden, and output layers with neurons and weights.

Explore how convolutional neural networks enable learning for image data by automatically extracting salient features. Observe how convolutional and pooling layers build features fed to a classifier, unlike hand-engineered features.

Explore recurrent neural networks, the go-to for time series and sequential data, handling varied input lengths in videos, language translation, and image captioning.

Explore principal component analysis for dimensionality reduction in a scikit-learn pipeline, using explained variance and energy thresholds to adaptively select components and apply fit and transform to data.

Explore scikit-learn pipelines and principal component analysis for dimensionality reduction, implement PCA with energy-based component selection, and apply fit and transform on a sample dataset.

Explore building a polynomial regression pipeline in scikit-learn by combining polynomial features with linear regression, using make_pipeline, defining a default degree, and testing with synthetic data.

Build a face recognition pipeline with scikit-learn using PCA and SVM on the LFW faces dataset, with grid search for C and gamma and evaluation via confusion matrix.

Explore the mathematics behind supervised learning, including loss functions, regularization, hyperparameters, and cross-validation, and compare linear, polynomial, and kernel methods like SVM and logistic regression.

Celebrate completing the data science and machine learning course and apply practical, hands-on insights from ai, ml, statistics, and data science to the workplace, including scikit-learn projects.

Explore dimensionality reduction and feature engineering through real datasets and engaging Python-based exercises, with hands-on use of NumPy and Scikit-learn, plus Python code for principal component analysis and pipelines.

Learn how Udemy's review system works and how your honest feedback helps improve data science and machine learning course content, updates, and clarity for real world concepts.

Explore what a feature is in data science and machine learning, including synonyms like attribute and random variable, and see how informative facial landmarks influence model performance.

Mark and visualize facial features on an image by plotting landmarks with coordinates. Flatten these into a 20-number feature vector and explore how the feature space supports machine learning models.

Explore how feature space represents data as points across dimensions, from single features on a line to multi-dimensional spaces, with numeric conversion for non-numeric features.

Explore the dimensionality of the feature space, defined as the total number of features, and how high-dimensional data complicates visualization beyond three dimensions, with dimensionality reduction helping visualize structure.

Explore the UCI machine learning repository to compare datasets by instances, features, dimensionality, and attribute types (categorical or real), across tasks such as classification, clustering, and regression, including ImageNet.

Dimensionality reduction addresses the curse of dimensionality by reducing feature space while preserving information relevant to the task, improving function estimation from sparse high-dimensional data.

Explore what overfitting means in data science and machine learning, and examine how dimensionality reduction relates to overfitting, including whether the two are linked or independent.

Explore dimensionality reduction by comparing feature selection and feature extraction, learn criteria like correlation scores and L1 regularization, and understand how to construct new features from existing ones.

Explain why feature selection lowers dimensionality, improves model performance and generalization, and preserves original feature identities for better interpretation and data acquisition.

discover the three main feature selection methods—filter, wrapper, and embedded—along with evaluation criteria and search strategies for selecting and discarding features.

Learn how filter methods generate feature subsets, evaluate each subset with independent scoring criteria, and select top features as a fast, model-free preprocessing step for machine learning and dimensionality reduction.

Wrapper methods use a machine learning model to guide feature selection, training on subsets and evaluating on a holdout set to identify the best features, though they are slow.

Explore feature selection methods—filter, wrapper, and embedded—highlighting how embedded methods train once on all data, weight features to reveal importance for fast, model-specific selection.

Explore how feature selection searches the subset space to pick the best features, using filter and wrapper methods, and greedy strategies like forward selection and backward elimination.

Examine why exhaustive subset search fails for wrapper feature selection and how greedy search offers a general approach, and relate simulated annealing to subset selection and filter methods.

Explore statistical-based filter criteria for feature selection, including low variance, t-score, chi-square, and HSIC, while contrasting unsupervised and supervised approaches and addressing feature redundancy.

Explore information theoretic criteria for feature selection, such as information gain and MRMR, to maximize relevance to the class label while minimizing redundancy, with CIFS addressing unselected features.

Explore similarity-based filter methods for feature selection by building an affinity matrix to preserve pairwise similarity, using distance metrics, k-nearest neighbor graphs, and geodesic distance via the Eisel Map.

Explore similarity-based feature selection using the unsupervised Laplacian score and spec to preserve data manifold and local neighborhoods, compare supervised options like Fisher score and ReliefF, with Python focus ahead.

Explore Python feature selection by coding filter, wrapper, and embedded methods in a Jupyter notebook, using selectKBest and recursive feature elimination on high-dimensional swarm behavior data.

Join an activity to determine whether forward selection can be learned using recursive feature elimination, backward elimination, and recursive feature elimination with cross-validation.

Explore the mathematical foundations of feature extraction for dimensionality reduction, including vector spaces, eigen decomposition, positive semidefinite matrices, singular value decomposition, principal component analysis, and constrained optimization with Lagrangian methods.

Explains the closure of a set under an operation, using examples like multiplication and addition, and shows how results stay within the set, a prerequisite for vector spaces.

Explore linear combinations of objects, especially vectors and matrices, by scaling each object with real numbers and summing results to form new objects.

Explore linear independence by checking whether a vector can be formed from linear combinations of other vectors. Learn about scalars, as well as linearly independent sets in vector spaces.

Explore how a vector space is defined by closure under scalar multiplication and addition. Learn that linear combinations stay in the set, with real vector spaces and pictorial representations.

Define the span of a set and show how a basis, as an independent set, spans a vector space and defines its dimensions.

Explore how a basis uses independent vectors to span a vector space, bases are not unique, differentiate coordinates from dimension, and show dimension equals number of basis vectors in subspace.

Define subspace as a vector-space subset closed under linear combinations, enabling dimensionality reduction via a k-dimensional basis and showing data in d dimensions can be represented by k coordinates.

Discover orthonormal basis in vector spaces, where vectors are orthogonal and normalized with unit norm for efficient computation. Learn how Gram Smith orthogonalization converts any basis into an orthonormal basis.

Explore matrix multiplication and matrix product, including outer product, by pairing row vectors with columns via dot products and adopting a blockwise view in Euclidean spaces R^n.

Determine if B lies in the column space of A by solving Ax = B, and use linear least squares to minimize the Euclidean distance when no exact solution exists.

Learn how to compute the rank of a matrix, identify independent columns, and link the column and row spaces to the data subspace, with PCA as a dimensionality reduction concept.

Explore eigenvalues, eigenvectors, and eigen space, including how eigen decomposition preserves direction, forms spaces for each eigenvalue, and their role in optimization and data science.

Explore the properties of symmetric and positive semidefinite matrices, including real eigenvalues, orthonormal eigenvectors, and orthogonal diagonalization, with applications to principal component analysis and singular value decomposition.

Discover singular value decomposition (SVD): decompose any real matrix into orthogonal U and V and diagonal D, using eigenvectors of A A transpose and A transpose A for PCA applications.

Explore the role of Lagrange multipliers and the Lagrangian dual in constrained optimization, especially for dimensionality reduction, including inequality and equality constraints and normalization conditions.

Maximize the trace of W^T S W with unit-norm columns to find the dominant eigenvectors of S, revealing how dimensionality reduction drives feature extraction.

Explore the numpy linear algebra library in Python, compute determinants, inverses, svd, least-squares solutions, and pseudo inverses, and analyze eigenvalues, eigenvectors, and orthogonality.

This activity provides a mathematical treatment of linear algebra for data science, linking Strang’s book, MIT OCW videos, and Ali Ghodsi lectures on dimensionality reduction.

Explore feature extraction as a dimensionality reduction technique, focusing on PCA and Kernel PCA, and discuss links to MDS, Isomap, LLE, Laplacian eigenmaps, and MVU, with Python implementations.

Master principal component analysis to perform unsupervised dimensionality reduction and feature extraction by identifying the best subspace and projecting data orthogonally to preserve information.

PCA identifies a subspace that best represents the data, balancing minimum reconstruction error and maximum variance after projection, to retain information.

Explore PCA properties, including linear projection, linear transformation via matrix multiplication, subspace reconstruction, and maximum variance preservation while preserving Euclidean geometry.

Derive the PCA max-variance solution via a linear transform W that maps D-dimensional data X to K-dimensional Y, maximizing variance and minimizing reconstruction error.

Derive principal component analysis by maximizing the Frobenius norm of centered data projections, equivalent to maximizing the trace W^T S W with S covariance matrix; select eigenvectors to maximize variance.

Implement PCA from scratch in numpy to extract eigenfaces from high-dimensional face data, including computing the mean, centering, covariance, and selecting top components by energy for reconstruction.

Use dual pca to perform feature extraction when d > n, compute eigenvalues from an n-by-n matrix, and preview kernel pca as a future extension.

Explore how PCA connects to SVD by centering the data matrix Xc, performing SVD Xc = U D V^T, and truncating to top eigenvalues for dimensionality reduction.

Kernel PCA extends PCA with SVD-based insights to enable nonlinear dimensionality reduction on centered data using the eigenvectors of x^t x and the d and v matrices.

Learn kernel pca and isomap as nonlinear dimensionality reduction techniques that use kernel matrices to preserve pairwise similarities and geodesic geometry, via eigen-decomposition and graph-based distances.

Explore kernel PCA as a unifying framework for nonlinear dimensionality reduction, comparing Isomap, MDS, LLE, Laplacian eigenmaps, and MVU, all built from kernel similarities and eigen-decomposition.

Explore encoder-decoder networks for dimensionality reduction and compare them to PCA and kernel PCA. Reveal how autoencoders address reconstruction and out-of-sample projection, and discuss linear versus non-linear activation.

Explore supervised feature extraction techniques, including supervised principal component analysis and Fisher linear discriminant analysis, that use label information to enhance dimensionality reduction and class discrimination.

Explore fisher's linear discriminant analysis as a supervised dimensionality reduction technique and implement fld using the psychic learned library to observe how reduced dimensions relate to the number of classes.

Explore feature extraction and dimensionality reduction pipelines in scikit-learn, applying PCA, kernel PCA, MDS, Isomap, LLE, spectral embedding, Laplacian eigenmaps, and t-SNE with supervised feature selection.

Learn how to prepare a data matrix for machine learning by handling categorical features with one-hot encoding, avoiding ordinal coding pitfalls, and improving model performance.

Explore how one hot encoding converts categorical features, like neighborhoods, into a numeric matrix with sklearn's dict vectorizer. It expands features and yields sparse, memory-efficient matrices.

Explore text features for information retrieval by counting term frequencies and applying tf-idf weighting to rank documents, converting text to feature vectors with count vectorizer and tf-idf.

Explore how raw image features arise from camera sensors with RGB and grayscale values 0–255. Flatten into vectors; CNNs then extract features and outperform traditional and hand-engineered ones.

Learn how derived features transform raw data into new features, enabling linear models to capture nonlinear patterns through polynomial features and feature space transformations.

Explore hand engineered image features like histogram of oriented gradients and local binary patterns, forming feature vectors used with SVMs for detection; note how deep networks reduce this need.

Learn feature scaling and normalization by centering and scaling each feature, improving optimization convergence and enabling batch normalization, PCA-related whitening, and robust performance across neural networks.

Explore the scalability limits of traditional feature selection and extraction for big data, and how activity-feature scaling motivates new, more efficient dimensionality reduction algorithms.

Celebrate completing the data science and machine learning course with this feature engineering bonus video. It invites learners to explore more courses and leave an honest review.

Explore why deep neural networks excel with big data, and learn Python with TensorFlow through live notebook coding to predict coronavirus trends from a real 2020 Kaggle dataset.

Explore how the Udemy review system works, discover course topics and real-world concepts, and learn how feedback helps improve the material and shorten the learning curve.

Learn the fundamentals of machine learning and the common concepts across subfields, including neural networks, framed as predictive modeling that builds models to predict future outcomes.

Learn how classification uses features to distinguish categories, such as dogs versus cats, by analyzing images represented as number matrices and predicting labels with a machine learning classifier.