Section outline

• Select section Day 1: Introduction and Foundations of Machine Learning

  Day 1: Introduction and Foundations of Machine Learning

  Collapse all Expand all

  Module 1: Introduction to Machine Learning

  • Overview of ML concepts and types (supervised, unsupervised, reinforcement learning)

  • Key terminology: features, labels, training/testing data, overfitting, bias-variance trade-off

  • Real-world applications of ML

  Module 2: Setting Up Python for ML

  • Installing Anaconda and Jupyter Notebook

  • Introduction to Pandas, NumPy, Matplotlib, and Seaborn

  • Basic data import and exploratory data analysis (EDA)

  Module 3: Data Preprocessing and Feature Engineering

  • Handling missing data and categorical features

  • Data normalization and scaling techniques

  • Feature selection and extraction

  • Train-test split and cross-validation
• Select section Day 2: Practical ML Applications and Capstone Project

  Day 2: Practical ML Applications and Capstone Project

  Module 4: Supervised Learning Algorithms

  • Implementing Linear Regression and Logistic Regression

  • Applying k-Nearest Neighbors (k-NN), Decision Trees, Random Forests

  • Evaluating models using accuracy, precision, recall, F1-score, and ROC-AUC metrics

  Module 5: Unsupervised Learning Algorithms

  • Performing K-Means Clustering and Hierarchical Clustering

  • Using Principal Component Analysis (PCA) for dimensionality reduction

  Capstone Project: Build and Present a Real-World ML Solution

  • Select a dataset (e.g., loan prediction, disease detection)

  • Conduct EDA and preprocessing

  • Train and evaluate ML models

  • Present project findings and recommendations

  Outcome: Deliver a complete, documented machine learning solution using Python and Scikit-learn.