Unit – I

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.

Unit – II

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.

Unit - III

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.

Unit – IV

Data Science Using Python: Downloading and reading data files in Python, Data Frame (Creating Data Frame from an Excel Spreadsheet, Creating Data Frame from .csv Files, Creating Data from Python List of Tuples, Operations on Data Frames);Data Exploration: head(), tail(), describe(),value_counts(), GroupBy(); Data Wrangling: Check missing values in the dataset, Fill missing values, Binning in Python; Data Visualization: Bar Graph, Histogram, Creating a Pie Chart, Creating Line Graph.