This AI and Machine Learning course offers a structured, hands-on introduction to the core principles and tools used to build intelligent systems. Starting from foundational concepts, you’ll explore the differences between AI, machine learning, and deep learning, along with real-world applications across industries. The curriculum includes essential math and statistics—such as linear algebra, calculus, probability, and hypothesis testing—ensuring a solid theoretical base.

Through practical exercises, learners will gain proficiency in Python programming and key libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. The course covers supervised and unsupervised learning techniques, deep learning architectures (CNNs, RNNs, LSTMs), and NLP with modern models such as BERT. You’ll also work with big data tools (Hadoop, Spark), model deployment techniques, and cloud platforms like AWS and Azure.

Capstone projects—including image classification, sentiment analysis, and predictive maintenance—allow learners to apply their skills in realistic scenarios. The course concludes with modules on AI ethics, model fairness, and soft skills like communication, resume building, and interview preparation, helping you become job-ready for AI/ML roles across industries.