This repo contains my Machine Learning 💻 Journey.🚶🏽‍♂️ Each notebook contains a specific Machine learning model with a clear explanation of how it works.

SUPERVISED LEARNING

• 1. K Nearest Neighbors KNN A classification Supervised Machine Learning
• 2. Linear Regression Introductory theory of lines of best fit
     • R squared, Root Mean Square Error
     • Cross Validation
     • Regularization (Ridge, Lasso)
• 3. Model Fine Tuning
     • Confusion Matrix, Classification Report
     • Logistic Regression, ROC Curve
     • Hyperparameter tuning : (GridSearchCV, RandomizedSearchCV)
• 4. Data Preprocessing
     • Handling Missing Data
     • Imputation, data pipelines
     • Centering and scaling
     • Evaluating Multiple Models

UNSUPERVISED LEARNING

• 1. Clustering for Dataset Exploration
     • Introduction to unsupervised learning
     • Evaluating a clustering
     • Transforming features for better clustering
• 2. Visualization with Hierarchical clustering and t-SNE
     • Visualizing Hierarchies
     • Hierarchical clustering with scipy
     • t-SNE for 2-dimensional maps
• 3. Decorrelating Your Data and Dimension Reduction
     • Visualizing the PCA Transformation
     • Intrinsic dimension
     • Dimension reduction with PCA
• 4. Discovering Interpretable Features
     • Non Negative Matrix Factorization (NMF)
     • NMF learns interpretable parts
     • Building Recommender systems with NMF