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Machine Learning Course Outline Chandigarh:

  1. Introduction to Machine Learning:
  • Definition and basic concepts
  • Types of machine learning: supervised learning, unsupervised learning, reinforcement learning
  • Applications of machine learning
  1. Linear Algebra and Probability:
  • Linear algebra basics (vectors, matrices, eigenvalues)
  • Probability and statistics for machine learning
  1. Supervised Learning:
  • Linear regression
  • Logistic regression
  • Support Vector Machines (SVM)
  • Decision Trees and Random Forests
  • Neural Networks and Deep Learning
  1. Unsupervised Learning:
  • Clustering techniques (K-means, hierarchical clustering)
  • Dimensionality reduction (Principal Component Analysis – PCA)
  • Association rule learning
  1. Model Evaluation and Validation:
  • Cross-validation
  • Bias-variance tradeoff
  • Evaluation metrics (precision, recall, F1-score, ROC curves)
  1. Ensemble Methods:
  • Bagging and boosting
  • Random Forests
  • Gradient Boosting
  1. Neural Networks and Deep Learning:
  • Feedforward neural networks
  • Backpropagation
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Transfer learning
  1. Natural Language Processing (NLP) and Computer Vision:
  • Text processing and sentiment analysis
  • Image processing and object recognition
  1. Reinforcement Learning:
  • Markov Decision Processes
  • Q-learning
  • Deep Reinforcement Learning
  1. Special Topics:
    • Explainable AI
    • Generative Adversarial Networks (GANs)
    • Autoencoders
  2. Practical Applications and Case Studies:
    • Real-world applications of machine learning in various domains
    • Hands-on projects and assignments
  3. Ethical and Social Implications:
    • Bias and fairness in machine learning
    • Ethical considerations in AI

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