Machine Learning Curriculum


Module 1: Introduction to Machine Learning

  • What is Machine Learning?
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
  • Applications of Machine Learning in real-world scenarios
  • Basic concepts: Features, Labels, Training, Testing, Validation
  • Tools and libraries: Introduction to Python, Anaconda, Jupyter Notebooks

Module 2: Data Preprocessing and Exploration

  • Data collection and acquisition using Kaggle and other repositories
  • Data cleaning and handling missing values
  • Data transformation and normalization
  • Exploratory Data Analysis (EDA)
  • Visualization techniques using libraries like Matplotlib and Seaborn

Module 3: Supervised Learning Algorithms

  • Linear Regression
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines
  • Model evaluation and metrics: Accuracy, Precision, Recall, F1-score, ROC curve

Module 4: Unsupervised Learning Algorithms

  • Clustering: K-Means, Hierarchical Clustering
  • Dimensionality Reduction: PCA (Principal Component Analysis)
  • Anomaly detection
  • Applications of unsupervised learning

Module 5: Model Evaluation and Hyperparameter Tuning

  • Cross-validation techniques
  • Bias-Variance trade-off
  • Grid Search and Random Search for hyperparameter tuning
  • Overfitting and underfitting mitigation

Module 6: Neural Networks and Deep Learning

  • Introduction to neural networks
  • Activation functions and architecture
  • Feedforward Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Transfer learning and pre-trained models

Module 7: Natural Language Processing (NLP)

  • Text preprocessing
  • Bag of Words
  • Sentiment analysis
  • Word embeddings: Word2Vec, GloVe
  • Introduction to Recurrent Neural Networks for NLP

Module 8: Time Series Analysis

  • Introduction to time series data
  • Moving averages and exponential smoothing
  • ARIMA (AutoRegressive Integrated Moving Average) models
  • LSTM (Long Short-Term Memory) for time series forecasting

Module 9: Capstone Project

  • Participants work on a real-world Machine Learning project
  • Applying concepts from previous modules
  • Data exploration, preprocessing, model selection, evaluation, and interpretation
  • Project presentation and peer review