Machine Learning and AI Fundamentals

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

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

• 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.

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