This repository contains all the slides and additional notes from my journey through the Machine Learning with Python course on freeCodeCamp.
The course covers a range of machine learning concepts and techniques using Python and Tensorflow, including supervised and unsupervised learning, model evaluation, and data preprocessing. This repository is a collection of the materials I created throughout the course to reinforce my learning and serve as a personal reference.
Slides: These slides cover key topics and algorithms introduced in the course. Topic 1 - Introduction to Machine Learning Topic 2 - Core Learning Algorithms Topic 3 - Neural Networks Topic 4 - Convolutional Neural Networks Topic 5 - Natural Language Processing with RNNs
Data Preprocessing: Handling missing values, normalization, encoding categorical variables. Supervised Learning: Linear Regression, Decision Trees, Random Forests, K-Nearest Neighbors (KNN) Support Vector Machines (SVM), Logistic Regression Unsupervised Learning: K-Means Clustering, Hierarchical Clustering, PCA Model Evaluation: Cross-validation, Confusion Matrix, ROC Curve, Precision-Recall