This repo contains the early works of my Machine Learning projects.

The datasets have allowed me to experiement with various Machine Learning tools and gain an intuition on how they work.

Across the entire repo I have applied:

1. Fundamentals of Machine Learning Techniques:

• Linear Regression
• Logistic Regression
• Support Vector Machines
• Decision Trees
• Random Forests
• XGBoost
• AdaBoost
• Principal Component Analysis
• T- Stochaistic Neighbor Embedding
• Bayesian Gaussian Mixture Models
• Anomaly and Novelty Detection Techniques
• K Means

2. Neural Networks and Deep Learning using TensorFlow 2 and Keras

• Types of Activation Functions
• Sequential Neural Networks
• Wide and Deep Neural Networks
• Functional Neural Networks
• Callback Methods and Hyperparameter Tuning
• Optimization Functions
• Learning Rate Scheduling
• Regularization in Deep Neural Networks
• Writing Custom TensorFlow Layers, Functions, Metrics, and Losses