| Feature | Detail |
|---|---|
| Course Duration | 3 Years |
| Course Level | Undergraduate |
| Course Tuition Fees | Varies |
| Mode of Study | Full-time |
| Institute Type | Private |
| Module | Topics |
|---|---|
| Introduction to AI & ML | History of AI, Basics of Artificial Intelligence and Machine Learning, Difference between AI, ML, and Deep Learning, Applications |
| Python for AI & ML | Python Basics, Libraries (NumPy, Pandas, Matplotlib, Scikit-learn), Data Handling and Visualization |
| Statistics for AI & ML | Descriptive Statistics, Inferential Statistics, Probability, Distributions, Hypothesis Testing |
| Machine Learning Algorithms | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Algorithms like Linear Regression, Logistic Regression, Decision Trees, SVM, KNN, Clustering, Dimensionality Reduction |
| Deep Learning | Neural Networks, CNN, RNN, LSTM, Transfer Learning, TensorFlow and Keras |
| Natural Language Processing (NLP) | Text Preprocessing, Tokenization, Stemming, Lemmatization, Word Embeddings, Sentiment Analysis, Chatbots |
| AI & ML Projects | Project Lifecycle, Data Collection, Data Preprocessing, Model Building, Evaluation, Deployment |
