Machine Learning & Data Science Masterclass with Python

Master Python control flow to manage decision making and iteration in machine learning and data science workflows.

Delve into functions in Python within the machine learning and data science masterclass, building foundational skills for data-driven programming.

Explore core data structures essential for data science and machine learning tasks using Python and learn how to store, access, and manage data efficiently.

Learn how to handle exceptions in Python by using try and except, with optional else and finally blocks, manage resources using the with statement, and raise custom errors.

Explore Python classes part 1 within the machine learning and data science masterclass, focusing on introductory concepts for building data-driven applications.

Explore Python classes in depth, continuing with object-oriented concepts and practical examples for data science and machine learning workflows.

Organize Python projects with modules and packages, practice import strategies, and understand script versus module execution using ecommerce.shopping.sales and __name__ == '__main__'.

Master the Python standard library as part of the machine learning and data science masterclass with Python.

Explain the Python package index and its role in supporting machine learning and data science projects with Python.

Identify popular Python packages essential for machine learning and data science. Apply these tools to streamline data handling, modeling, and analysis in Python projects.

Build web applications using Django and Python in a practical, beginner-friendly module. Learn the core concepts and project structure in Part 1.

Build web applications with Django, part 2, leveraging Python for machine learning and data science workflows.

master machine learning with python for data science by introducing core concepts and applying practical techniques through hands-on coding.

Build a beginner Python dice rolling game while learning to think like a programmer: plan, implement input loops, generate two random dice numbers, and handle invalid choices with clear outputs.

Build practical Python projects by creating a currency converter and a quiz game, mastering core coding skills for machine learning and data science.

Develop a text-based tic-tac-toe game in python using a 3 by 3 list of lists board, with input validation, turn-based moves, winner and draw detection, and optional colorized output.

Build a pig dice game and a cows and bulls guessing game in Python. Implement turn-based play, dice rolling, score tracking to 100, input validation, and cows and bulls logic.

This Python masterclass builds a password strength checker and a password generator, using regex for validation and a 0–5 strength scale with robust error handling.

Explore databases, relational models, and the sql language using MySQL and MySQL Workbench, and learn to install, connect, and run queries to manage tables, relationships, and data.

Learn to retrieve data from a single table for machine learning and data science workflows in Python, focusing on concise, efficient data access for analysis and modeling.

Retrieve data from multiple tables using Python and apply it within a machine learning and data science workflow. Master cross-table data access to support integrated analysis and modeling.

Explore inserting, updating, and deleting data with Python in machine learning and data science workflows.

Learn to write complex queries for data analysis using Python in a machine learning and data science context, turning diverse data into actionable insights.

Master SQL views to simplify complex queries, reuse saved subqueries, and build updatable views for invoices and balances, with check option for data security and abstraction.

Explore stored procedures and their role in Python-driven machine learning and data science workflows, clarifying how database operations integrate with data analysis.

Explore how MySQL triggers enforce data consistency after insert or delete. Learn to manage events, including using the event scheduler and creating, viewing, and altering tasks.

Explore transactions and concurrency in a python-based machine learning and data science masterclass, and gain concise, practical insights for implementing these concepts.

Discover MySQL data types across string, numeric, date and time, blob, and spatial categories, learn effective typing practices, and explore json as a flexible column for product properties.

Explore the fundamentals of designing databases in part 1 of this course, introducing core concepts for structuring and organizing data in machine learning and data science projects.

Explore designing databases part 2 for machine learning and data science with Python, introducing practical database design concepts for data-driven projects.

Improve algorithm efficiency by mastering indexing techniques to boost query performance for high-performance data pipelines in data science using Python, enabling faster machine learning data processing.

Learn to secure databases in MySQL by creating restricted user accounts, grant only the necessary privileges for apps to read and write data, and revoke privileges when necessary.

Explore how data science works by following a practical eight-step workflow—from data collection and cleaning to feature engineering, model building, evaluation, deployment, and monitoring—powered by Python, statistics, and machine learning.

Explore core data structures and algorithms, from arrays and linked lists to trees and graphs, and master big O time and space complexity through problem-solving drills and interview prep.

Explore linked lists as a core data structure within Python-driven machine learning and data science workflows. Discover practical implementation basics and how linked lists support efficient data management in applications.

Learn about stacks in Python-driven machine learning and data science, focusing on how stacks organize data and support practical tasks.

Explore the role of queues in machine learning workflows using Python, including data processing, task scheduling, and efficient workflow management.

Explore hash tables and their use in Python for machine learning and data science in this masterclass.

Explore binary trees within a Python-driven machine learning and data science masterclass. Learn core concepts of binary trees and their role in data processing and algorithms.

Study AVL trees, a data structure topic covered in this machine learning and data science masterclass with Python.

Explore the heap data structure and its use in efficient priority-based operations, implemented in Python for machine learning and data science workflows.

Explore tree-based learning methods in machine learning with Python, building and evaluating tree models to analyze data and improve predictive insights.

Explore graphs in machine learning and data science with Python to understand their role in data-driven workflows.

Learn about undirected graphs in the context of machine learning and data science with Python.

Learn sorting algorithms and their use in machine learning and data science with Python to enhance data organization and analysis.

Explore string manipulation algorithms in Python to support machine learning and data science workflows across diverse datasets.

This lesson explains how Python programs are written in .py files, checked by the interpreter, converted to bytecode, and executed by Python's virtual machine, with data types and dynamic typing.

Install and configure Git, create snapshots, and explore history, branching, and merging. Collaborate with others via GitHub and learn to use the command line and GUI tools.

Learn to create snapshots in Python for machine learning and data science in this masterclass.

Explore how to analyze browsing history data with Python for machine learning and data science applications.

Explore branching in machine learning and data science with Python, highlighting how branching decisions influence workflows and model development.

Learn to collaborate with Git through centralized and distributed workflows, push, fetch, and pull from remotes, and use GitHub concepts like pull requests, issues, and milestones.

Explore rewriting history through machine learning and data science with Python, applying practical techniques to analyze historical data and uncover new insights.