Machine Learning Essentials with Python-From Zero to Mastery

K-NN, Linear Regression, SVM, K Means Clustering, Decision Tree, Neural Networks, Deep Learning and Convolutional NNs

This course contains the use of artificial intelligence. Some of the videos in this course were created using AI-assisted tools. These tools were used to professionally produce high-quality visuals and narration in order to make the learning process clearer, more engaging, and more efficient. All learning materials were carefully selected, organized, and updated by the instructor to reflect current knowledge and best practices. AI was used as a supportive technology, not as a substitute for subject-matter expertise, instructional design, or academic responsibility.

Update(02/12/2025): Tens of NEW Lecture Videos and Jupiter Notebooks have been added.

Are you interested in the field of machine learning? Then you have come to the right place, and this course is exactly what you need!

In this course, you will learn the basics of various popular machine learning approaches through several practical examples. Various machine learning algorithms, such as K-NN, Linear Regression, SVM, K-Means Clustering, Decision Trees, Hidden Markov Models and Reinforcement Learning, Bayesian Networks, Neural Networks, Deep Learning and Convolutional Neural Networks, will be explained and implemented in Python. In this course, I aim to share my knowledge and teach you the basics of the theories, algorithms, and programming libraries in a straightforward manner. I will guide you step by step on your journey into the world of machine learning.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. This course will teach you the basic techniques used by real-world industry data scientists. I'll cover the fundamentals of machine learning techniques  that are essential for real-world problems, including:

• Linear Regression

• K-Nearest Neighbor

• Support Vector Machines

• K-Means Clustering

• Decision Tree

• Markov Models and Reinforcement Learning

• Bayesian Networks,

• Neural Networks

• Deep Learning

• Convolutional Neural Networks

  
  

  These are the basic topics any successful technologist absolutely needs to know about, so what are you waiting for? Enrol now!