| Course Duration | 3 Years |
|---|---|
| Course Level | Undergraduate |
| Course Tuition Fees | Varies by region |
| Mode of Study | Full Time |
| Institute Type | Private |
| Module | Topics |
|---|---|
| Introduction to Artificial Learning & Machine Learning | History of AI, Basics of Machine Learning, Difference between AI, ML, and Deep Learning |
| Python for AI & ML | Python Basics, Libraries for AI & ML (NumPy, Pandas, Matplotlib, Scikit-learn) |
| Data Preprocessing | Data Cleaning, Data Transformation, Handling Missing Values, Feature Scaling |
| 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 |
| Natural Language Processing | Text Preprocessing, Tokenization, Word Embeddings, Sentiment Analysis |
| Reinforcement Learning | Introduction to Reinforcement Learning, Q-Learning, Policy Gradients, Deep Q-Networks |
| AI & ML Project | Project Planning, Dataset Collection, Model Building, Evaluation, and Deployment |
