Machine Learning Tutorial With Python

🚀 Course Overview This course balances theory and practice. For each algorithm, you will first learn the theory, then apply it using Scikit-Learn, and finally recreate the algorithm entirely from scratch in Python to master the logic.

📚 Syllabus

Module 1: Regression

• Introduction: Setting up the environment and understanding Supervised Learning.

• Linear Regression: using Pandas and Scikit-Learn for stock price prediction.

• Math & Implementation: Understanding "Best Fit Slope" and "R Squared" theory.

• From Scratch: Writing your own Linear Regression algorithm in raw Python without libraries.

Module 2: K-Nearest Neighbors (KNN)

• Concept: Understanding Euclidean Distance and classification logic.

• Application: Using K-Nearest Neighbors on breast cancer datasets.

• From Scratch: Building a custom KNN classifier to understand performance trade-offs.

• Optimization: Comparing your custom code against Scikit-Learn's speed.

Module 3: Support Vector Machines (SVM)

• Theory: Understanding Vectors, Hyperplanes, and the "Kernel Trick".

• Implementation: Applying SVM to classify data effectively.

• Optimization: Mathematical deep dive into Convex Optimization and dot products.

• From Scratch: Building an SVM class in Python (one of the most challenging parts of the course).

Module 4: Clustering Algorithms

• Flat Clustering: Introduction to K-Means and handling unlabeled data (Unsupervised Learning).

• K-Means from Scratch: Implementing the centroid logic manually.

• Hierarchical Clustering: Understanding Mean Shift and dynamic bandwidth.

• Application: applying Mean Shift to the Titanic dataset for grouping passengers.

Module 5: Neural Networks & Deep Learning

• TensorFlow Basics: Introduction to Deep Learning concepts and setting up TensorFlow.

• Neural Network Model: Building a Deep Neural Network (DNN) for sentiment analysis.

• Convolutional Neural Networks (CNN): Processing image data (Cats vs. Dogs).

• Recurrent Neural Networks (RNN): Working with sequential data and LSTM (Long Short-Term Memory) units.