AI & Machine Learning – Foundations | AI & ML
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
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
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.
Career Map – Your AI Learning Journey
Beginner Level
Basic Programming Knowledge (Optional)
AI & Machine Learning – Foundations (6 Weeks)
Data Analyst
ML Engineer (Junior)
Business Intelligence Analyst
AI Automation Specialist
Advanced Path
Data Science / Deep Learning / AI Specialization
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.
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 |
Frequently Asked Questions
This course is ideal for students, fresh graduates, working professionals, and career switchers who want to enter the AI, Data Analytics, or Machine Learning domain. No advanced coding experience is required, but basic computer knowledge is helpful.
No prior programming experience is mandatory. The course starts with Python basics and gradually progresses toward machine learning concepts and model building.
You will learn Python, NumPy, Pandas, Scikit-learn, and Jupyter Notebook — all industry-standard tools widely used in AI and machine learning projects.
Yes. In the final week, you will complete a Mini Machine Learning Project involving dataset preparation, model building, evaluation, and result interpretation. This can be added to your professional portfolio.
Yes. The course includes structured placement support such as resume building guidance, interview preparation sessions, portfolio development assistance, and job referral support where applicable.
You can apply for roles such as Junior Data Analyst, ML Intern, AI Associate, Business Intelligence Analyst, or Entry-Level Machine Learning Engineer depending on your skills and project strength.
Freshers in AI/ML-related roles in India typically earn between ₹3 LPA to ₹6 LPA. Strong project portfolios and technical skills can help secure higher packages in startups and product-based companies.
With 1–3 years of experience, salaries generally range between ₹6–10 LPA. Mid-level ML Engineers (3–5 years) earn ₹10–18 LPA, while senior AI professionals (5+ years) can earn ₹18–30+ LPA depending on expertise and company type.
Yes. AI and Data Science are among the fastest-growing technology domains in India across IT services, fintech, healthcare, e-commerce, and startups. Demand for AI-skilled professionals continues to rise rapidly.
Yes. This course builds foundational AI and ML skills required to transition from non-technical or semi-technical roles into entry-level AI, analytics, or data-related positions with consistent practice and project work.
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