Course Summary

Introduction to Machine Learning course. This course teaches you how to implement machine learning algorithms from scratch, step by step. In this hands-on course, you’ll not only learn the theory, but also write the code behind popular machine learning techniques yourself. From linear regression and decision trees to advanced neural networks, every part of each algorithm is built from scratch. Then, we compare our implementations to industry-standard libraries like Scikit-learn and prove that our custom algorithms can match or even outperform them in terms of performance. What you will learn in the Introduction to Machine Learning course: Implement machine learning algorithms from scratch: Create each algorithm from scratch by coding. Comparing custom implementations with Sklearn: Deep understanding of optimization, speed, and accuracy with direct comparison. Understanding the mathematics and logic behind machine learning: A thorough understanding of the principles and fundamentals. Increase confidence in machine learning fundamentals: Master the basic concepts. Building and training neural networks: Deep understanding of each layer, activation function, and backpropagation steps. Developing practical coding skills: Becoming a skilled machine learning programmer. This course is suitable for people who: Future Data Scientists: To enhance practical skills and deep understanding. Developers moving into machine learning: To learn the principles and techniques. Students and researchers: To carry out research projects and develop new algorithms. Applied professionals: To solve real-world challenges with machine learning. Consultants and entrepreneurs: to develop AI-based products and services. Machine learning enthusiasts: To acquire the necessary knowledge and skills.