This course provides a comprehensive introduction to Fundamentals of Programming using Python and the concepts of AI, ML and DL for various industrial applications. This is a basic to advanced course hence prior programming experience is not required. This course will help candidates to develop the basic knowledge and basic skills required for a programmer to use the Python programming environment effectively in terms of data structures, control structures, classes, functions and important libraries such as NumPy, Pandas, Matplotlib and Seaborn etc. Then the mathematics for AI ML and DL will be covered including Probability, Statistics Linear Algebra and Calculus. Towards the end of the course we cover Machine learning and Deep learning theory concepts and take up applications to AI such as NLP, Speech, Image etc.

1. Definition:

   • Machine Learning is a subset of AI that focuses on the development of algorithms and models that allow computers to learn from data and make predictions or decisions.

2. Types of Machine Learning:

   • Supervised Learning: The algorithm is trained on a labeled dataset, where the input data and corresponding output are provided.
   • Unsupervised Learning: The algorithm is given unlabeled data and must find patterns and relationships without explicit guidance.
   • Reinforcement Learning: The algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties.

3. Applications:

   • ML is applied in various domains, including image and speech recognition, natural language processing, recommendation systems, and predictive analytics.

4. Algorithms:

   • Common ML algorithms include linear regression, decision trees, support vector machines, k-nearest neighbors, and neural networks.