Course Duration | Course Level | Course Tuition Fees | Mode of Study | Institute Type |
---|---|---|---|---|
3 Years | Undergraduate | Varies by Program | Full Time | Private |
Module | Topics |
---|---|
Introduction to Machine Learning | History of ML, Types of ML, Applications |
Python for Machine Learning | Python Basics, Libraries for ML (NumPy, Pandas, Matplotlib, Scikit-learn) |
Supervised Learning | Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines |
Unsupervised Learning | K-Means Clustering, Hierarchical Clustering, Principal Component Analysis |
Neural Networks and Deep Learning | Basics of Neural Networks, Deep Learning Frameworks, Convolutional Neural Networks, Recurrent Neural Networks |
Reinforcement Learning | Introduction to RL, Q-Learning, Policy Gradients |
Natural Language Processing | Text Preprocessing, Word Embeddings, Sentiment Analysis, Machine Translation |
Special Topics in ML | Advanced Algorithms, Big Data and ML, Ethical Implications of ML |