Machine Learning and AI Fundamentals

Aim

Program Structure

Introduction to Machine Learning and AI

• Overview of Machine Learning and AI.
• Historical Context and Evolution.
• Key Terminologies and Concepts.

Supervised Learning

• Linear Regression and Classification.
• Decision Trees and Random Forests.
• Support Vector Machines (SVM).
• Model Evaluation and Performance Metrics.

Unsupervised Learning

• Clustering Algorithms (K-means, Hierarchical).
• Dimensionality Reduction Techniques (PCA, LDA).
• Anomaly Detection.

Neural Networks and Deep Learning

• Introduction to Neural Networks.
• Deep Learning Fundamentals.
• Convolutional Neural Networks (CNN).
• Recurrent Neural Networks (RNN).
• Transfer Learning.

Natural Language Processing (NLP)

• Text Preprocessing and Tokenization.
• Sentiment Analysis.
• Topic Modeling.
• Sequence Models and LSTM.
• Transformer Models and BERT.

Practical Machine Learning

• Working with Python and Jupyter Notebooks.
• Using TensorFlow and Keras for Model Building.
• Implementing PyTorch for Advanced Deep Learning.
• Utilizing scikit-learn for Machine Learning Algorithms.

Who Should Enrol?

Program Outcomes

Fee Structure

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