Applied Machine Learning

This course is designed for anyone looking to step into the world of Machine Learning or aspiring to build a career as a Data Scientist. The course balances conceptual ML and applied ML, preparing you to tackle real-world ML problems.

Modules Overview

• 📘 Introduction to ML+
  • What is Machine Learning?
  • Real-world Applications
  • Types of Learning (Supervised, Unsupervised, Reinforcement)
  • How Machines Learn from Data
• 📊 Core ML Concepts +
  • Machine Learning Pipeline
  • Models
  • Loss Functions
  • Weights optimization
• 📈 Linear Models +
  • Introduction to Linear Models
  • Basis Functions
  • Perceptron and Logistic Regression
  • Multiclass Classification
• ⚙️ Data Preprocessing & Feature Engineering +
  • Data Cleaning and Data Exploration
  • Feature Selection and Transformation
  • Dimensionality Reduction
• 🌀 Non-linear Models +
  • Support Vector Machines (SVM)
  • Decision Trees
  • Clustering
• 🎯 Model Tuning +
  • Overfitting vs Underfitting
  • Regularization
  • Bias-Variance Tradeoff
  • Cross-Validation
  • Hyperparameter Tuning
• 🧠 Advanced ML +
  • Ensembling Techniques
  • Neural Networks
  • Responsible AI