Python-Introduction to Data Science and Machine learning A-Z

Explore the concept of data, its types, and how to collect, measure, analyze, and visualize it, including qualitative and quantitative, nominal and ordinal, discrete and continuous data.

Explore the fundamentals of statistics, including descriptive and inferential types, and see how measures like mean, median, and standard deviation fuel data science applications.

Explore the difference between population and sample, and compare probability and non-probability sampling methods, including random, systematic, stratified, cluster, convenience, voluntary, purposive, and snowball.

Learn the basics of statistics by exploring median, mode, and mean with clear definitions and practical examples. See how these formulas reveal central tendency in datasets.

Explore how to compute range, quartiles (Q1, Q2, Q3) and percentiles, and grasp standard deviation and variance to measure data dispersion in practical datasets.

Explore the basics of decision trees in data science and machine learning, including entropy and information gain, with simple examples. Relate them to brain-like decisions that guide data splits.

Explore the basics of probabilities, including marginal, conditional, and joint probabilities, with practical examples from gambling, weather, and finance, plus an intro to probability distributions.

Explore NumPy, a Python library for multidimensional arrays that enables fast linear algebra, random number generation, and shape manipulation, offering Matlab-like efficiency over Python lists for scientific computing.

Set up numpy in PyCharm by installing the community version, creating a project, configuring the Python interpreter, and installing the numpy package to begin writing numpy-enabled Python code.

Import numbers as a feature and define one dimensional areas and two dimensional areas using Python, then print and add these areas to perform basic linear algebra.

Learn basic array operations in Python: create areas, inspect item size and data type, determine dimensions and shape, compute size, and reshape arrays to different column-row configurations.

Learn to perform basic calculations in Python by using linspace to generate evenly spaced numbers, slice arrays, and compute min, max, and sum across one- and two-dimensional arrays.

Explore basic matrix operations in Python with arrays, including addition, subtraction, multiplication, and division, reshape arrays into a single column, sum across axes, and compute square roots and standard deviation.

Learn to apply exponential and logarithmic functions to a NumPy array in Python, printing results and preparing for visualization with Matplotlib.

Explore the basics of data analysis with Python and Pandas, including the data lifecycle, transforming raw data, and drawing insights using Pandas' powerful features.

Set up pandas in your text editor by configuring the project interpreter, installing the pandas package, and importing pandas to start data analysis, time series, and statistics.

Create a simple employee database in Python with pandas, building a data frame from numbers, names, and hourly salaries, and display a clean table.

Explore pandas slicing with head and tail to select the first or last rows from a simple database. Practice retrieving the three first rows or the last row with examples.

Learn how to merge multiple data frames with pandas to create one data frame, handle matching and nonmatching columns, and preserve selected columns using the merge operation.

Explore joining two Pandas data frames to merge food data, including names, prices, colors, weights, and quantities, and print the merged table.

Learn to use pandas to read a CSV file, specify its location, and convert it to an HTML file, with practical steps and real-world naming examples.

Discover the basics of SciPy, an open-source Python library that extends NumPy with modules for data analysis, including linear algebra, integration, interpolation, clustering, and differential equation solving.

Learn basic SciPy operations in Python by importing packages, using from-import and import statements, and exploring help, info, and source to inspect package content.

Explore SciPy's special functions package in Python, including elliptic functions, integrals, Bessel and Kelvin functions, and cover convenience functions like log, exp, and basic trigonometry for advanced math applications.

Explore SciPy's integration tools, including quad and dblquad, to compute single-variable and double integrals in Python, with practical examples and limits.

Apply a SciPy fftpack transformation to a 2d array in Python by importing NumPy and SciPy, creating the array, and printing the transformed results.

Explore SciPy's linear algebra tools to solve linear equations and compute the inverse of a matrix using two square arrays, from the linear algebra subpackage.

Learn to visualize data with matplotlib in Python, using bar charts and scatter plots to reveal patterns in data. Grasp how matplotlib provides Matlab-like plotting library for data science visuals.

install and set up matplotlib in your text editor by configuring the Python interpreter and adding the matplotlib package, enabling you to run plotting in your data science workflow.

Import matplotlib, create basic graphs, and plot multiple lines with titles and axis labels, then apply styles like classic and dark background for improved visuals.

Explore how to visualize data with matplotlib by building a three-series bar graph, including data setup, axis labeling, and styling options.

learn to create a histogram in matplotlib with Python, including generating data and bins, setting a title and axis labels, and exploring bar and step histogram styles.

Create a pie chart using matplotlib to visualize food sales by category, assigning labels and colors, and customize the style to compare pizza, ice cream, and burgers.

Create a scatter plot in matplotlib by supplying x and y data, adding a title and axis labels, and explore styling and multi-graph options.

Learn how Seaborn, a higher-level visualization library built on matplotlib, creates attractive statistical graphs with multiple variables, grids, colors, distributions, and automatic linear regression plots from the web.

Set up Seabourne in your text editor and install the Seabourne package to begin plotting. Import numpy, pandas, and Seabourne to create plots and graphs in your data science workflow.

learn to create a seaborn histogram with an overlaid line by loading datasets from github, such as diamonds or tips, and adjust bins and plot display with matplotlib.

Learn to create a Seaborn joint plot to explore the relationship between tips and total bill using the tips dataset, showing how higher bills relate to higher tips.

Visualize flight passenger data with seaborn to identify the most popular month, July, using a categorical plot of months vs passengers and exploring strip, violin, and box styles.

Explore seaborn's multi-plot visualizations by creating a face grid of scatter plots from the tips dataset, showing total bill, tip, sex, and smoker status.

This lecture introduces machine learning with Python, explaining how data drives learning and covering supervised, unsupervised, and reinforcement methods with examples like image and speech recognition.

Explore core machine learning algorithms with a theoretical introduction to linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means, and random forests, using Python.

Explore building a linear regression model in Python using NumPy and Matplotlib, generating X and Y arrays, computing coefficients by minimizing squared error, and visualizing data with a regression line.

Learn logistic regression using a beginner-friendly library, build a data frame with finance, management, and logistics features, and evaluate with train-test split, confusion matrix, and accuracy score.

Explore the k-means algorithm by generating 800 random points with five centers, clustering them, and visualizing centers with a scatter plot in this Python data science lesson.

Wraps up the course by reviewing statistical concepts used in data science and the data science tools explored in Python, including machine learning and algorithms.