1. Programme Overview
The Machine Learning Using Python course is a comprehensive 3-month skill programme designed to build strong foundations in Python programming and practical expertise in machine learning. The course consists of 17 modules, divided into two major components: Python Programming and Machine Learning.
The first phase focuses on Python fundamentals, covering core concepts such as data types, control structures, functions, object-oriented programming, exception handling, GUI development, and database operations. It further extends to data analysis using NumPy and Pandas, along with data visualization and a mini project to strengthen practical skills.
The second phase introduces key machine learning concepts, including supervised and unsupervised learning techniques. Learners will gain hands-on experience in data preprocessing, regression and classification algorithms, clustering, and dimensionality reduction. The course also covers model evaluation and tuning using industry-standard methods. Through real-world case studies and projects in domains such as healthcare, banking, and retail, learners will develop the ability to build and evaluate machine learning models.
By the end of the programme, learners will have a solid foundation in Python and practical exposure to end-to-end machine learning workflows.
2. Programme at a Glance
3. Programme Objectives
4. Syllabus
The programme is structured into 17 modules across two subjects — Python Programming (9 modules) and Machine Learning (8 modules).
Subject 1: Python Programming (9 Modules)
Module 1: Python Fundamentals
- Python installation & IDEs
- Variables, data types
- Operators & expressions, Input/Output
Module 2: Control Flow
- Conditional statements
- Loops (for, while)
- Break, continue, pass, Nested loops
Module 3: Data Structures
- Lists, tuples, sets, Dictionaries
- String operations & slicing
- Comprehensions
Module 4: Functions & Modules
- Function definition & scope, Arguments
- Lambda functions
- Creating & importing modules
Module 5: Object-Oriented Programming
- Classes & objects, Constructors
- Inheritance
- Polymorphism & encapsulation
Module 6: Exception Handling & I/O
- Try-except-finally
- Custom exceptions
- File handling
Module 7: GUI with tkinter & SQLite
- Tkinter basics, widgets, containers
- Menus, events & forms (Login Form)
- Connection, cursor, CRUD operations
Module 8: Python for Data Analysis
- NumPy (arrays, operations)
- Pandas (DataFrames, filtering)
- Data cleaning & aggregation
Module 9: Visualization & Project
- Matplotlib & Seaborn
- Exploratory Data Analysis
- Mini project (API / dataset-based)
Subject 2: Machine Learning (8 Modules)
Module 1: Intro to Machine Learning
- ML concepts & applications
- Types of ML
- ML pipeline
Module 2: Data Pre-processing
- Missing values, Encoding variable
- Feature scaling
- Train-test split
Module 3: Regression Algorithms
- Simple & multiple linear regression
- Polynomial, Lasso and Ridge regression
- Model assumptions
Module 4: Classification Algorithms
- Logistic regression
- SVM
- KNN, Naive Bayes
Module 5: Tree-Based Models
- Decision trees
- Random forest
- Feature importance
Module 6: Unsupervised Learning
- K-means clustering
- Hierarchical clustering
- PCA & dimensionality reduction
Module 7: Model Eval & Tuning
- Confusion matrix, Precision, Recall
- ROC-AUC
- GridSearchCV / RandomizedSearchCV
Module 8: ML Projects & Case Studies
- Healthcare prediction
- Banking risk analysis
- Retail demand forecasting
5. Mode of Assessment & Certification
Examination: Internal Assessment 1 (15 Marks), Internal Assessment 2 (15 Marks), Project Eval (70 Marks).
Project Breakdown: Development (20 Marks), Execution (20 Marks), Documentation (15 Marks), Viva-Voce (15 Marks). Minimum 28 marks required in project.
Certification: A University Certificate will be awarded upon successful completion of the online project evaluation. The certificate is issued by Dr. B.R. Ambedkar Open University, Hyderabad.
6. Job & Career Prospects
Completing this course opens entry-level and intermediate opportunities in the data and AI domain:
- Data Analyst (Entry Level) — Analyzing and visualizing data using Python tools.
- Machine Learning Engineer (Beginner Level) — Building and testing ML models.
- Data Science Trainee — Assisting in data-driven projects and analysis.
- AI/ML Intern — Supporting development of machine learning solutions.
- Business Analyst (Technical) — Using data insights for decision-making.
7. Further Opportunities
