Introduction to Python: History of Python, An interpreted high-level language, Need of Python Programming, Applications, Importance in Data Science. Introduction to Machine Learning: Definition of Machine Learning; Machine learning and AI, Use/Role of Python in AI, Importance of Python in AI and Machine learning. Applications of Machine Learning, Supervised vs. Unsupervised Learning, Python libraries suitable for Machine Learning; Overview of Python Libraries and Packages: Pillow, Matplotlib, Numpy, NLTK (Natural Language Toolkit), FlashText, Scipy, sklearn, Bokeh, Pandas, Mahotas. Pros & Cons of Machine Learning.
Machine Learning Algorithms in Python: Advantages/Applications of machine learning Algorithms, Regression: Linear Regression, Non-linear Regression; Classification: K-Nearest Neighbour, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machines, Clustering: K-Means Clustering, Hierarchical Clustering, Density-Based Clustering, Recommender Systems: Content-based recommender systems, Collaborative Filtering; Role of Model evaluation.
Installing and working with Python: Data Types, Operators and Operands in Python, Operator precedence; Expressions and Statements (Assignment statement); Input / Output and Comments in Python; Data Structures: Mutable or immutable objects in python; Lists, Tuples, Sets, Dictionaries; Control structures: Conditional Branching, Looping, Exception Handling; User-defined functions: Defining, invoking functions, passing parameters (default parameter values, keyword arguments), Scope of variables- Global and Local Variables, Void functions and Fruitful Functions. File Handling: File handling functions, Object Oriented concepts in Python: Classes in python: Creating a Class, The Self Variable, Constructor, Types of Variables, Namespaces; Inheritance: Types of Inheritance, Encapsulation, Abstraction, Polymorphism.