Develop and implement core machine learning algorithms like linear regression, logistic regression, decision trees, KNN, and K-means from scratch using Python Master data preprocessing, feature engineering, and data normalization techniques for machine learning projects Understand and apply mathematical concepts such as gradient descent, loss functions, and model evaluation metrics in Python Build a custom machine learning library by coding algorithms from first principles for practical AI and data science applications Analyze and explain machine learning models' inner workings to enhance transparency and interpretability
Python machine learning from scratch,
in this course we will learn about implementing core machine learning algorithms using Python, without relying on high-level libraries like scikit-learn or TensorFlow. You'll build a solid foundation by coding algorithms such as linear regression, logistic regression, decision trees, k-nearest neighbors, and k-means clustering entirely from scratch. Through this hands-on approach, you'll gain deep insight into how these algorithms work under the hood, including concepts like gradient descent, loss functions, model evaluation, and data preprocessing. The course emphasizes mathematical understanding and Python implementation, helping you strengthen both your coding and analytical skills. By the end of the course, you’ll have developed your own machine learning library and be equipped to create and explain ML models from first principles. Basic Python and math knowledge are required. This course is ideal for anyone looking to truly master machine learning fundamentals. Data School
