Machine Learning Courses Online

upGrad has partnered with the leading universities listed below to offer some of the most advanced and in-demand AI courses aligned with the latest industry trends.

1. IIIT Bangalore

IIIT Bangalore offers 12 programs across undergraduate and postgraduate levels, giving students a wide range of choices including Artificial Intelligence & Data Science (AI&DS), Computer Science & Engineering (CSE), and Electronics & Communication Engineering (ECE).

For more detailed insights, explore the complete IIIT Bangalore Overview.

2. University of Maryland

University of Maryland offers 264 programs across undergraduate and graduate levels, giving students a wide range of choices including the Bachelor of Science in Computer Science, the Master of Science in Artificial Intelligence (MSAI), the M.Eng. in Engineering Artificial Intelligence (PMAI), and the Master of Science in Applied Machine Learning (SAML).

For more detailed insights, check out the University of Maryland Overview.

3. Golden Gate University

Golden Gate University offers 60+ degrees and certificates, including the Master of Science in Artificial Intelligence (MSAI) and the Doctor of Business Administration (DBA) in Emerging Technologies with a focus on Generative AI. Students can also take specialized courses such as AI Ethics and Governance, Prompt Engineering and Validation, and Agentic AI.

For more detailed insights, check out the Golden Gate University Overview.

4. Liverpool John Moores University

Liverpool John Moores University offers over 250 courses, including the BSc (Hons) Computer Science with Artificial Intelligence, the MSc Artificial Intelligence (Machine Learning), and the LLM Artificial Intelligence and Law. Online options include the MS in Machine Learning & AI with specializations like Generative AI and MLOps.

For more detailed insights, check out the Liverpool John Moores University Overview.

Machine Learning (ML) has become one of the fastest-growing tech domains in 2026, powering applications like recommendation engines, fraud detection, autonomous systems, chatbots, and GenAI tools. With companies across IT, BFSI, healthcare, retail, and manufacturing rapidly adopting ML, skilled professionals are in high demand.

The best part? Machine Learning is open to learners from engineering, computer science, IT, mathematics, and even non-tech backgrounds willing to pick up programming and analytical skills. With the right training, you can learn how models are built, trained, deployed, and used to make real-world decisions.

Eligibility Criteria for the machine learning course

Eligibility for Machine Learning courses in India varies by institute, but most programs follow these common requirements:

Educational Qualification

• A bachelor’s degree in Engineering, Computer Science, Mathematics, Statistics, IT, or any related field.

• Students from non-tech backgrounds can also apply if they complete foundational modules in Python and maths.

Technical Skills

• Basic understanding of programming (Python recommended)

• Familiarity with mathematical concepts like linear algebra, probability, and calculus

• Understanding of SQL or databases is an added advantage

Professional Eligibility

• IT professionals, software developers, data analysts, engineering graduates, QA testers, and tech aspirants looking to upskill for ML, AI, and automation roles.

Machine Learning Course Fees and Duration

When selecting an ML course, learners typically ask: “What’s the duration and fee range?”
Here’s a clear breakdown.

Average Course Fee in India

Specialisations in Machine Learning

1. Deep Learning & Neural Networks

Ideal for learners who want to build advanced ML models used in computer vision, speech recognition, and GenAI tools.

What you’ll learn

• Neural networks, CNNs, RNNs, LSTMs

• Transformers & attention models

• Image, speech, and text processing

• GPU training & model optimization

• Building DL pipelines

2. Applied Machine Learning

Covers core ML algorithms and real-world applications across industries.

What you’ll learn

• Regression, classification, clustering

• Feature engineering

• Model evaluation & tuning

• End-to-end ML model deployment

• MLOps basics (versioning, containers)

3. Generative AI & LLMs

Perfect for aspirants who want to build or fine-tune GenAI models.

What you’ll learn

• LLM fundamentals

• Prompt engineering

• Fine-tuning GPT, LLaMA, Mistral

• RAG pipelines & embeddings

• Building GenAI apps

4. Data Engineering for Machine Learning

Focuses on building data systems that power ML models.

What you’ll learn

• Data pipelines (ETL/ELT)

• SQL & NoSQL

• Big data tools (Spark, Kafka)

• Cloud platforms (AWS, Azure, GCP)

• ML-ready data preparation

5. AI for Business Decision-Making

Designed for managers and non-tech professionals.

What you’ll learn

• Predictive analytics

• Automation workflows

• Forecasting & optimization models

• Business use cases of ML

• Interpreting ML outputs for decisions

Course Syllabus

A comprehensive ML course covers both fundamentals and advanced concepts. Here’s what the curriculum typically includes:

1. Programming Foundations

• Python programming

• NumPy, Pandas, Scikit-learn

• Data wrangling & EDA

• SQL & databases

2. Mathematics for ML

• Linear algebra

• Calculus basics

• Probability & statistics

• Optimization

3. Core Machine Learning Algorithms

• Supervised learning (regression, classification)

• Unsupervised learning (clustering, PCA)

• Ensemble techniques (RF, XGBoost)

4. Deep Learning & AI

• Neural networks

• CNNs & computer vision

• RNNs, LSTMs, Transformers

• NLP & text analytics

5. Model Deployment & MLOps

• Model deployment (Flask, FastAPI)

• Containers & Docker

• ML model monitoring

• CI/CD for ML pipelines

6. Big Data & Cloud ML

• Spark ML

• AWS Sagemaker / GCP Vertex AI

• Distributed data processing

7. Capstone Projects

• Real datasets

• End-to-end ML lifecycle

• Industry problem-solving

8. Soft Skills

• Business communication

• Data storytelling

• Team collaboration

Career Opportunities and Job Roles

Machine Learning opens doors to some of the most impactful tech roles in India and globally.

1. Popular Job Roles

Source: Glassdoor

2. Industries Hiring ML Professionals

• IT & Tech (AI products, automation tools)

• E-Commerce (recommendation systems)

• Healthcare (medical imaging, diagnosis models)

• BFSI (fraud detection, credit scoring)

• Telecom (network optimization)

• Manufacturing (predictive maintenance)

3. Growth Path

• Entry Level: ML Intern, Data Analyst

• Mid Level: ML Engineer, AI Specialist

• Senior Level: Senior ML Engineer, Data Science Manager

• Leadership: Head of AI, Chief AI Officer (CAIO)

Key Factors for Choosing a Machine Learning Course

1. Curriculum Quality

Ensure the course covers:

• Core ML algorithms

• Deep Learning

• MLOps & deployment

• Capstone projects

2. Learning Format

Choose based on:

• Mentor-led vs. self-paced

• Live classes vs. recorded lectures

• Industry mentorship

3. Certification Value

Look for:

• University-backed certificates

• Industry recognition

• Strong alumni outcomes

4. Placement Support

Check for:

• Resume & portfolio building

• Mock interviews

• Job assistance

5. Reviews & Testimonials

Evaluate:

• Mentor expertise

• Quality of assignments

• Real-world relevance

Top Reasons to Learn Machine Learning in 2026

1. High Demand & Future-Proof Career

ML roles are growing at over 35% annually in India, driven by automation, AI adoption, and GenAI tools.

2. Attractive Salaries

Experienced ML professionals earn between ₹12–35 LPA, with senior roles crossing ₹40 LPA.

3. Work Across Industries

ML skills are applicable in finance, healthcare, e-commerce, marketing, cybersecurity, and more.

4. Skill Shortage

Companies struggle to find skilled ML engineers — creating strong job security.

5. Real-World Impact

ML solves problems like fraud detection, disease prediction, personalisation, and automation.

Real-World Use Cases of Machine Learning

Machine learning is powering innovation across industries. Here’s how companies apply ML — and the kind of work you’ll learn to do in an ML course.

1. Finance & Banking

Applications:

• Credit scoring

• Fraud detection

• Algorithmic trading

• Customer risk evaluation

Impact:
Faster approvals, lower fraud, stronger financial decisions.

2. Retail & E-Commerce

Applications:

• Recommendation systems

• Customer segmentation

• Demand forecasting

• Dynamic pricing

Impact:
Higher conversions, better customer experience.

3. Healthcare

Applications:

• Disease prediction

• Medical imaging (tumor detection)

• Drug discovery

• Personalized treatment models

Impact:
Better diagnosis, faster treatment decisions.

4. Marketing & Advertising

Applications:

• Campaign optimization

• Sentiment analysis

• Customer lifetime value prediction

• Behavioural modelling

Impact:
Higher ROI, targeted marketing.

5. Manufacturing & Supply Chain

Applications:

• Predictive maintenance

• Quality control using computer vision

• Supply chain optimization

Impact:
Reduced operational costs, improved efficiency.

6. Telecom & Technology

Applications:

• Churn prediction

• Network optimization

• Personalized content recommendations

Impact:
Higher retention, better digital experiences.

Machine Learning is revolutionizing the way businesses operate by enabling systems to learn from data and make intelligent predictions. As industries adopt ML-powered solutions, career opportunities in this field are rapidly expanding. To help you strengthen your ML foundation and grow your expertise, we’ve curated a complete set of resources that cover ML fundamentals, techniques, projects, courses, and career insights.

• Machine Learning, Deep Learning, and Artificial Intelligence are closely connected but serve different purposes in solving real-world problems. Learn how they differ and where each technology is used here: Artificial Intelligence vs Machine Learning: ML vs Deep Learning
• A typical Machine Learning course includes Python programming, data preprocessing, statistical learning, supervised & unsupervised algorithms, model optimization, and ML deployment techniques. Explore the full breakdown of topics and learning outcomes here: Machine Learning Course Syllabus
• Machine Learning is shaping industries like healthcare, finance, e-commerce, and logistics through predictive analytics, automation, fraud detection, and personalized recommendations. Explore real-world ML use cases here: Machine Learning Applications
• Building ML projects such as churn prediction models, spam classifiers, demand forecasting systems, and recommendation engines helps learners develop hands-on expertise. Check out the most impactful beginner-friendly projects here: Top Machine Learning Project Ideas & Topics
• Careers in Machine Learning span roles such as ML Engineer, Data Scientist, MLOps Engineer, and AI/ML Researcher — all offering excellent growth potential. Discover the most in-demand ML career paths here: Career Opportunities in Machine Learning
• The average salary for ML engineers in India starts at ₹7 LPA for entry-level roles, with experienced professionals earning around 50 LPA or more based on expertise and industry. Explore more insights here: Machine Learning Engineer Salary in India: Beginners & Experienced