This course is your gateway into the powerful world of deep learning and natural language processing, two of the most in-demand areas in modern data science. It begins with an in-depth exploration of Principal Component Analysis (PCA), a key technique in data preprocessing and dimensionality reduction. You’ll learn how PCA works, how to apply it to large datasets like MNIST, and how to evaluate the impact of compression on model performance.

Moving into deep learning, the course provides a strong foundation in neural network architecture, including perceptrons, activation functions, backpropagation, and optimization algorithms. Using TensorFlow and Keras, you will build, train, and evaluate multi-layer deep neural networks on real-world datasets such as customer churn, developing a robust understanding of how deep learning can be used for classification problems. You'll also learn how to save, load, and reuse trained models—essential skills for production-ready machine learning.

The final section dives into natural language processing (NLP), where you’ll work with text data using techniques like Bag of Words and TF-IDF. You’ll process and vectorize textual data for machine learning models, apply feature engineering, and build spam detection systems with Python’s NLP libraries. Real-world use cases and model evaluation strategies are emphasized, giving you the confidence to apply NLP in business or research contexts.