Diploma in Python with Data Science and Machine Learning

The lecture explains Python operators, including arithmetic, assignment, exponentiation, floor division, and modulo, plus comparison and logical operators, with practical examples and syntax demonstrations.

Learn how if-else statements control program flow in Python, using conditional checks, nesting, and proper indentation to execute blocks or raise errors when misaligned.

Master how indentation in Python governs flow control, using if, elif, and else blocks and loop exits; understand errors that occur when indentation is missing.

Practice hands-on coding on your computer to see how it works, emphasizing practical implementation over mere theory. Enhance your skills by tackling the assignment questions through regular practice at home.

Explore iterative statements in Python, including for and while loops, which repeatedly execute a section of code as long as a given condition remains true.

Demonstrate how a while statement uses a condition to repeat printing a message, using the hello example, driven by user input, a counter, and consistent indentation.

Learn to use the for statement to iterate over sequences in Python, executing a block for each item in a list and printing the results with a sample list.

Learn how a tuple works and how to use a for statement to iterate through values one to five, with a guided demonstration.

Explore basic Python concepts in assignment 2 by creating programs that use for to process numbers five to nine and one through nine, and discuss practical knowledge.

Demonstrate how for loops work in Python by initializing a counter, applying a condition, using indentation, and incrementing it to print messages three times, then save with .py and run.

Demonstrates using while and for loops to compute the sum of a list by initializing a sum to zero and updating it as the loop iterates, and prints the result.

Explore how the Python range function iterates numbers in loops, starting from zero, ending before the end, and optionally stepping by a defined increment to produce even numbers.

This bootcamp introduces notebook usage, python basics (data types, operators, conditionals, functions), data analysis and Seabourne visualization, and an introduction to supervised learning and logistic regression.

Explore data science and machine learning, using modern tools to extract useful information from large data volumes and build predictive models, such as weather forecasting or election outcomes.

Refresh your basics with this introductory python crash course, revisiting looping statements and essential topics for data science and machine learning, to help both beginners and others.

Explore Python data types from integers and floats to strings, lists, tuples, and dictionaries. Learn mutability, indexing, converting between types, and appending and deleting items.

Explore dictionaries in Python by learning how key-value pairs work, how to create them with curly braces, and how to access, update, and delete items using keys.

Tackle python list exercises by retrieving items, printing, appending numbers, removing elements, and sorting. Reinforce these with conditional statements or functions for practical data science projects.

Explore Python operators, including arithmetic, comparison, and logical operators, and learn how to apply them within if statements to build conditional logic.

Explore Python iterative statements, including while loops that run when a condition is true and for loops that iterate over sequences or ranges.

Learn Python functions, including defining functions with parameters, blocks, and calls, and explore anonymous lambda functions for single-line expressions in data science and machine learning projects.

learn how to use numpy for fast data analysis, including handling large datasets with 1d and 2d arrays, importing numpy, and converting lists to numpy arrays in notebooks.

Learn foundational NumPy basics: create arrays with arange and zeros/ones, generate identity matrices, inspect shapes, index and slice, generate random values, and compute min/max.

Master numpy indexing and slicing to access elements in multi-dimensional arrays. Learn to retrieve values from 2D, 3D, and the fourth dimension using explicit indices and slices.

Explore numpy data analysis basics: indexing and slicing arrays, selecting elements, handling start and end positions, iterating over items, and sorting arrays, with practical exercises.

Explore the pandas data analysis library to load, clean, transform, and visualize datasets. Apply data cleaning, deduplication, and transformation to build reliable datasets for analysis and plotting.

Learn data analysis with pandas part 2 by cleaning and transforming messy data, extracting relevant information from big data, and predicting trends with coronavirus active cases dataset and machine learning.

Explore data analysis with pandas by building and indexing data structures, creating customized tables, and accessing values via dictionaries and keys, csv concepts and indexing tricks.

Explore loading data with pandas, using city and capital fields, and working with index levels and named indexes to analyze data through functions and avoid rule-based errors.

Explore importing data with pandas, inspecting data frames, and cleaning data by handling file paths, removing duplicates and non-values, and computing mean and median for analysis.

Master Pandas basics to replace missing values with mean, median, or mode, edit column data, handle duplicates, and perform quick data analysis on a dataframe.

Explore how to quantify relationships between dataset columns using correlation, focusing on numeric data and ignoring non-numeric values while visualizing with seaborn scatterplots and histograms for predictive insight.

Explore data visualization with Matplotlib, learning how graphs and plots transform complex data into clear, accurate visuals that reveal trends and patterns for decision makers.

Learn to plot a sine wave with matplotlib in python, using an object-oriented canvas and axis labels, and explore colors and line styles via Seabourne Data Regionalization Library.

Explore seaborn data visualization by importing seaborn, inspecting a flight data dataset, and creating styled plots, including line and joint plots, color palettes, and grid layouts.

Import seaborn to load datasets and explore its high-level, interactive visualization capabilities, including diverse plot types and line plots.

Explore the basics of machine learning, including supervised, unsupervised, and reinforcement learning, how to train models on data, and applications like sentiment analysis, forecasting, and fraud detection.

Explore the machine learning process from data preparation and train-test splits to applying linear regression, evaluating with mean squared error and root mean squared error, and deploying predictive models.