Course Overview – AI & Machine Learning – Foundations
The AI & Machine Learning – Foundations course is a 6-week practical program designed to introduce students and working professionals to the world of Artificial Intelligence and data-driven decision making.
Using industry-relevant tools like Python, NumPy, Pandas, Scikit-learn, and Jupyter Notebook, this course builds a strong base in data handling, model building, and applied machine learning techniques.
Whether you are a beginner exploring AI or a professional looking to upgrade your skills, this program gives you a clear, structured entry into the AI ecosystem at an affordable investment of ₹ 12,999.
What Is This Course All About?
This course focuses on understanding how machines learn from data. You will learn how to clean datasets, build simple machine learning models, evaluate performance, and apply AI techniques to real-world business problems.
You’ll learn to:
- Write Python programs for AI applications
- Handle structured datasets using Pandas
- Understand supervised vs unsupervised learning
- Train and test machine learning models
- Apply ML concepts to practical business use cases
"AI is no longer optional. This course helps you build the technical foundation required to enter one of the fastest-growing career domains in India."
Tools & Technologies Covered
| Tool |
Purpose |
| Python |
Core programming language for AI/ML |
| NumPy |
Numerical computations & arrays |
| Pandas |
Data manipulation & cleaning |
| Scikit-learn |
Machine learning model building |
| Jupyter Notebook |
Interactive coding & experimentation |
Weekly Learning Breakdown
| Week |
Focus Area |
| Week 1 |
Introduction to AI/ML & Python Basics |
| Week 2 |
Data Handling & Dataset Preparation |
| Week 3 |
Machine Learning Basics & Types |
| Week 4 |
Model Building & Training |
| Week 5 |
Applied AI Use Cases & Evaluation Metrics |
| Week 6 |
Mini Machine Learning Project |
Real-World Applications
- Customer churn prediction
- Sales forecasting
- Fraud detection basics
- Recommendation systems fundamentals
- Business analytics automation
AI careers in India are among the fastest-growing tech domains, with strong salary acceleration compared to traditional IT roles.
Why Choose AI & Machine Learning – Foundations?
The AI & Machine Learning – Foundations program is your entry point into one of the highest-growth technology domains in India. It equips you with strong fundamentals in Python, data handling, and model building — preparing you for real-world AI, analytics, and automation roles.
AI is reshaping every industry — this course gives you the foundation to participate in that transformation confidently.
Career Map – Your AI Learning Journey
Beginner Level
Basic Programming Knowledge (Optional)
AI & Machine Learning – Foundations (6 Weeks)
Business Intelligence Analyst
Advanced Path
Data Science / Deep Learning / AI Specialization
AI & ML skills are among the most future-proof and salary-accelerating capabilities in today’s job market.
Skill Wheel – What This Course Trains You In
| Skill |
Application |
| Python Programming |
Data manipulation & AI scripting |
| Data Handling |
Cleaning, filtering, preparing datasets |
| Machine Learning Basics |
Supervised & Unsupervised models |
| Model Evaluation |
Accuracy, precision, performance metrics |
| Practical Implementation |
Jupyter-based experimentation |
| Mini Project Execution |
Real-world ML application |
Placement Assistance & Career Support
Students completing this course receive structured career support including:
- Resume building for AI/ML roles
- LinkedIn profile optimization
- Interview preparation sessions
- Mini project portfolio guidance
- Referral & hiring partner opportunities (where applicable)
India’s AI and analytics industry is growing rapidly across IT services, fintech, e-commerce, healthcare, and startups. Companies are actively hiring candidates with strong Python and ML fundamentals.
With practical skills and project exposure, learners can confidently apply for entry-level AI, Data Analyst, and ML Associate roles.
Real-World Job Examples (India Market)
| Role |
Industry |
Avg Salary (INR) |
| Junior Data Analyst |
IT / Analytics Firms |
₹3 – 6 LPA |
| ML Engineer (Entry Level) |
Tech / Startups |
₹6 – 10 LPA |
| AI Associate |
Fintech / E-commerce |
₹7 – 12 LPA |
| Business Intelligence Analyst |
BFSI / Retail |
₹6 – 11 LPA |
| Senior ML Engineer (5+ yrs) |
Product Companies |
₹18 – 30+ LPA |
Why This Course Is a Smart Investment
| Feature |
Value |
| Beginner Friendly |
No advanced math or coding required |
| Project-Based Learning |
Build portfolio-ready ML mini project |
| Industry-Relevant Tools |
Python, Pandas, Scikit-learn |
| Affordable Pricing |
₹12,999 for job-ready foundation skills |
| Career Acceleration |
Entry into high-growth AI ecosystem |
Course Curriculum: AI & Machine Learning – Foundations
The AI & Machine Learning – Foundations program is a structured 6-week practical training course designed to build strong fundamentals in Python programming, data handling, and machine learning model development.
This curriculum focuses on hands-on implementation using Python, NumPy, Pandas, Scikit-learn, and Jupyter Notebook, ensuring learners gain both conceptual clarity and practical exposure.
Week-Wise Curriculum Breakdown
Week 1 – Introduction to AI & Python Basics
Build foundational understanding of Artificial Intelligence and start coding with Python.
| Topic |
Focus Area |
| Introduction to AI & ML |
Applications, industry relevance, AI vs ML |
| Python Fundamentals |
Variables, data types, operators |
| Control Structures |
Loops, conditionals |
| Functions & Modules |
Reusable code blocks |
| Jupyter Notebook Setup |
Interactive coding environment |
Week 2 – Data Handling & Dataset Preparation
Learn how to collect, clean, and prepare structured datasets for machine learning models.
| Topic |
Focus Area |
| NumPy Basics |
Arrays, numerical operations |
| Pandas Introduction |
DataFrames, Series |
| Data Cleaning |
Handling missing values, duplicates |
| Data Transformation |
Filtering, grouping, sorting |
| Exploratory Data Analysis |
Understanding patterns in data |
Week 3 – Machine Learning Basics & Types
Understand core ML concepts and different learning approaches.
| Topic |
Focus Area |
| What is Machine Learning? |
Supervised vs Unsupervised Learning |
| Regression Models |
Linear Regression basics |
| Classification Models |
Logistic Regression, KNN overview |
| Train-Test Split |
Model validation approach |
| Overfitting & Underfitting |
Bias-variance tradeoff basics |
Week 4 – Model Building & Training
Apply Scikit-learn to build and train simple machine learning models.
| Topic |
Focus Area |
| Scikit-learn Introduction |
Library structure & workflow |
| Model Training |
Fitting models using training data |
| Prediction |
Generating outputs |
| Evaluation Metrics |
Accuracy, confusion matrix |
| Improving Model Performance |
Basic tuning concepts |
Week 5 – Applied AI Use Cases & Evaluation
Explore practical business use cases and evaluate real-world ML applications.
| Use Case |
Application Area |
| Customer Churn Prediction |
Telecom / SaaS |
| Sales Forecasting |
Retail / E-commerce |
| Basic Fraud Detection |
Fintech |
| Performance Evaluation |
Precision, recall, F1-score |
| Model Interpretation |
Understanding outputs |
Week 6 – Mini Machine Learning Project
Build a hands-on project to apply your learning and create a portfolio-ready asset.
| Project Component |
Deliverable |
| Problem Statement Selection |
Business use case definition |
| Dataset Preparation |
Cleaned & structured dataset |
| Model Development |
Trained ML model |
| Evaluation & Results |
Performance metrics & insights |
| Final Submission |
Project report + Jupyter notebook |
Outcome: By the end of 6 weeks, learners will confidently build, train, and evaluate basic machine learning models using Python and industry-standard tools.