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Best Free Machine Learning Course With Certification in 2025

Best Free Machine Learning Course With Certification in 2025

Q: 1. Can I get a job after completing a free machine learning course?

Yes, but it depends on the depth of the course and how well you apply what you’ve learned. Free courses can teach you the basics, but employers often look for hands-on experience and strong portfolios. Pair your learning with projects to improve your chances.

Q: 2. Can I complete a free ML course without coding experience?

Yes, many beginner-friendly courses introduce Machine Learning concepts without requiring prior coding knowledge. However, learning a programming language like Python alongside the course can be very helpful.

Q: 3. How do free courses compare to paid machine learning certifications?

Free courses provide foundational knowledge but often lack mentorship, advanced topics, and industry-recognized certificates. Paid certifications usually offer in-depth learning, real-world projects, and job placement support.

Q: 4. How important is Python for learning machine learning?

Python is one of the most important languages for Machine Learning due to its simplicity and the availability of libraries like NumPy, Pandas, and TensorFlow. Learning Python is highly recommended.

Q: 5. Can I learn machine learning without a math background?

Yes, but basic knowledge of linear algebra, statistics, and probability is helpful. Some courses simplify the math for beginners, while others include detailed explanations.

Q: 6. What’s the difference between supervised and unsupervised learning?

Supervised Learning: The model learns from labeled data (e.g., predicting house prices).Unsupervised Learning: The model identifies patterns in unlabeled data (e.g., clustering customer segments).

Q: 7. Are free ML courses recognized by employers?

Some free courses offer certificates from reputable platforms which can add value to your resume. However, combining them with projects and self-study makes your skills more credible.

Q: 8. How do I create a portfolio after completing a free ML course?

Work on small projects like creating a recommendation system, sentiment analysis, or image classification. Use platforms like GitHub to showcase your code and Kaggle to participate in competitions.

Q: 9. What’s the best way to practice ML algorithms?

Use datasets from Kaggle, UCI Machine Learning Repository, or Google’s Dataset Search.Experiment with algorithms using libraries like Scikit-learn and TensorFlow.Join online competitions to solve real-world problems.

Q: 10. How do I specialize in deep learning or NLP after a free ML course?

After mastering the basics, explore specialized free courses on platforms that focus on deep learning or Natural Language Processing (NLP). Practice with relevant datasets and tools like PyTorch or Hugging Face.

By Rohan Vats

Updated on Mar 11, 2025 | 12 min read | 8.1k views

Table of Contents

Every time you watch a movie on Netflix, shop on Amazon or get recommendations on YouTube, you’re seeing Machine Learning in action. It’s the technology behind algorithms that learn from data to deliver better results over time.

Machine Learning helps machines mimic human learning by identifying patterns in data and improving with experience. This is why industries like tech, healthcare, and finance use it to enhance customer experiences and streamline operations. Companies like Google, Meta, and Microsoft rely on Machine Learning to power their products and services.

If you’re fascinated by how technology can think, a Machine Learning course is the perfect place to start. The best part? You don’t have to spend a fortune. Several platforms offer free machine learning courses with certificates, allowing you to learn and showcase your skills.

What you’ll learn in a Machine Learning certification course for free:

Foundations of Machine Learning: Algorithms, models, and data processing.
Hands-on Projects: Work on real-world problems to build your portfolio.
Certification: Earn credentials from platforms like upGrad.

With these free courses, you can build your knowledge of Machine Learning and explore how it shapes the technology we use every day.

Beginner-Friendly Free Machine Learning Courses with Certificate

Whether you’re a student, working professional, or someone curious about technology, you can start your ML journey without needing advanced coding or technical expertise. Beginner-friendly courses help you grasp the basics of machine learning, explore real-world applications, and build a solid foundation for advanced learning.

Who Should Opt for Beginner Courses?

Students: Looking to explore ML as a career option.
Professionals: Wanting to upskill and integrate ML into their roles.
Non-Tech Enthusiasts: Interested in understanding how machine learning impacts industries.
Aspiring Developers: Planning to move into AI and ML roles but need a starting point.

1. Machine Learning Crash Course

Overview:
This hands-on course by Google is designed to introduce learners to machine learning concepts in a practical, engaging way. It uses video lessons, interactive visualizations, and exercises to make ML accessible for beginners. Whether you want to explore the theory or apply ML in real-world scenarios, this course provides a comprehensive start.

What You’ll Learn:

Supervised learning techniques such as regression and classification.
How to evaluate and improve machine learning models.
Key Python libraries like NumPy and pandas for data manipulation.
Applications of ML in fields like healthcare, finance, and computer vision.

Key Features:

Duration: 15 hours of content, spread over 12 modules.
Interactive Exercises: 100+ coding exercises in Google Colab (no setup required).
Real-World Focus: Learn how ML solves practical problems.
Visual Aids: Use interactive visualizations to grasp complex concepts easily.
Prerequisites: Basic programming skills in Python and an understanding of linear equations and graphs.

Who It’s For:

Beginners curious about how machine learning works.
Developers looking to explore ML workflows with Python.
Students seeking a hands-on introduction to machine learning concepts.

2. Understanding Machine Learning

Overview:
This course simplifies machine learning for absolute beginners. With no coding required, it focuses on key concepts, definitions, and workflows. Using relatable examples, it shows how machine learning powers technologies like recommendation systems, chatbots, and self-driving cars.

What You’ll Learn:

How machine learning differs from AI and data science.
An overview of the machine learning lifecycle, from data preparation to evaluation.
Deep learning basics and applications in NLP and computer vision.
The ethical implications and limitations of machine learning.

Key Features:

Duration: Flexible, self-paced lessons.
Practical Examples: Covers relatable use cases like e-commerce and healthcare.
Interactive Exercises: Learn through guided activities and real-world scenarios.
Non-Technical Focus: Perfect for learners without coding experience.

Who It’s For:

Non-programmers exploring the world of machine learning.
Beginners seeking a conceptual understanding of ML.
Professionals curious about how ML impacts their industry.

Intermediate Free Machine Learning Courses and Certification

If you’ve already started learning machine learning and have some experience with Python or data analysis, intermediate courses are the next step. These programs are designed to deepen your understanding of concepts like deep learning, natural language processing (NLP), and reinforcement learning. They help you gain practical knowledge and prepare for challenging roles in the field.

Who Should Opt for Intermediate Courses?

Data Analysts: Wanting to explore more advanced ML techniques.
Developers: Interested in applying machine learning in real-world projects.
Working Professionals: Looking to specialize in AI-driven solutions.
Students: Ready to take on more technical and hands-on ML topics.

3. Machine Learning with Python

Overview:
Part of the MITx MicroMasters program, this course offers an in-depth introduction to machine learning. It covers advanced topics like linear models, deep learning, and reinforcement learning, with hands-on Python projects to reinforce learning. Ideal for learners with a background in math, programming, and probability.

What You’ll Learn:

Fundamentals of machine learning tasks like classification, regression, and clustering.
Building and analyzing models such as neural networks, kernel machines, and graphical models.
Reinforcement learning concepts and their applications.
How to select suitable models for different use cases.

Skills Gained:

Advanced Python programming for ML.
Deep learning and artificial neural networks.
Linear and non-linear model implementation.
Understanding reinforcement learning workflows.

Key Features:

Duration: 15 weeks (10–14 hours per week).
Projects: Hands-on projects using Python for practical learning.
Prerequisites: Proficiency in Python, probability theory, and college-level calculus.
Certifications: Part of MITx MicroMasters in Statistics and Data Science.

Who It’s For:

Data analysts looking to advance their ML knowledge.
Programmers interested in deep learning and reinforcement learning.
Professionals aiming to specialize in machine learning applications.

4. Post Graduate Certificate in Machine Learning & NLP (Executive)

Source: upGrad (IIIT Bangalore)
URL: PGC in Machine Learning & NLP

Overview:
This course, designed for working professionals, focuses on machine learning and natural language processing (NLP). It includes over 240 hours of learning, 5+ industry projects, and 360° career support. The program helps learners master cutting-edge tools and techniques in NLP, including machine translation and text analytics.

What You’ll Learn:

Core ML and NLP concepts like sentiment analysis, text processing, and machine translation.
Practical implementation of algorithms for NLP workflows.
Advanced machine learning models and their applications.

Skills Gained:

Text analytics and machine translation.
Practical understanding of Git and other collaborative tools.
Mastery of ML and NLP libraries like TensorFlow and PyTorch.

Key Features:

Duration: 8 months (240+ hours of learning).
Projects: 5+ industry-focused projects and case studies.
Certifications: Certification from IIIT Bangalore.
Career Support: 1:1 mentorship, job portal access, and mock interviews.

Who It’s For:

Professionals transitioning into AI and NLP roles.
Developers aiming to specialize in natural language processing.
Analysts seeking to integrate NLP into their work.

IIIT Bangalore

Executive Diploma in Machine Learning and AI

Placement Assistance

Executive PG Program13 Months

View Program

Liverpool John Moores University

Master of Science in Machine Learning & AI

Dual Credentials

Master's Degree19 Months

View Program

Advanced Machine Learning Programs with Certification

For experienced professionals aiming to deepen their expertise, advanced machine learning programs provide the perfect pathway to specialize in cutting-edge technologies. These courses are designed for those who already have a strong foundation in machine learning and want to explore areas like deep learning, predictive analytics, and advanced algorithms.

Who Should Opt for Advanced Programs?

AI Enthusiasts: Professionals looking to specialize in areas like deep learning and predictive modeling.
Senior Data Scientists: Seeking mastery in advanced tools and techniques.
Working Professionals: Looking to future-proof their careers with certifications from top institutions.
Graduates: Interested in pursuing global opportunities or dual degrees in AI and ML.

5. Post Graduate Certificate in Machine Learning and Deep Learning (Executive)

Source: upGrad (IIIT Bangalore)
URL: PGC in Machine Learning & Deep Learning

Overview:
This program focuses on mastering machine learning and deep learning concepts with hands-on projects. It offers 240+ hours of learning, live sessions, and personalized career support to help learners gain advanced knowledge and transition into AI-driven roles.

What You’ll Learn:

Advanced deep learning concepts like neural networks and convolutional networks.
Machine learning algorithms and model evaluation.
Real-world applications using industry-standard tools and libraries.

Skills Gained:

Proficiency in TensorFlow, PyTorch, and other deep learning frameworks.
Advanced data processing and model building.
Expertise in designing and implementing ML workflows.

Key Features:

Duration: 8 months (240+ hours of learning).
Projects: 5+ industry projects and case studies.
Certifications: PG Certificate from IIIT Bangalore.
Career Support: AI-powered profile builder, 1:1 mentorship, and mock interviews.

Who It’s For:

Working professionals transitioning into AI and deep learning roles.
Data scientists aiming to upgrade their ML and DL expertise.
Developers interested in AI-driven projects.

6. MS in Machine Learning Engineering

Source: upGrad (Drexel University)
URL: MS in Machine Learning Engineering

Overview:
This dual-degree program allows learners to earn a master’s degree from Drexel University while gaining an advanced certificate from IIIT Bangalore. It focuses on advanced machine learning techniques, including predictive analytics and statistical modeling, with the added benefit of international exposure.

What You’ll Learn:

Advanced machine learning algorithms and deep learning techniques.
Predictive analytics and data visualization.
Statistical modeling and SQL-based data management.

Skills Gained:

Advanced statistical and algorithmic skills for ML.
Hands-on experience with tools like Python, TensorFlow, and SQL.
Expertise in building scalable machine learning solutions.

Key Features:

Duration: 23 months (online and on-campus hybrid).
Certifications: Master’s degree from Drexel University and a certificate from IIIT Bangalore.
Global Exposure: International campus experience with networking opportunities.
Career Support: End-to-end application and visa assistance, STEM OPT eligibility, and career mentoring.

Who It’s For:

Professionals aiming for global career opportunities in AI and ML.
Developers transitioning into specialized ML roles.
Students with strong technical backgrounds in data science.

7. Master of Science in Machine Learning & AI

Source: upGrad (LJMU & IIIT Bangalore)
URL: Master of Science in Machine Learning & AI

Overview:
This Master’s program, delivered by Liverpool John Moores University (LJMU) and IIIT Bangalore, is designed for professionals aiming to become leaders in AI and ML. With over 750 hours of learning, industry-driven projects, and integrated modules on Generative AI, this program equips learners with the latest tools and techniques to excel in the field.

What You’ll Learn:

Deep learning techniques and their applications.
Generative AI models and tools.
Natural Language Processing (NLP) and computer vision.
Core ML concepts like predictive analytics, neural networks, and advanced algorithms.

Skills Gained:

Expertise in Generative AI development.
Mastery of advanced ML and deep learning techniques.
Proficiency in industry-relevant tools like Python, TensorFlow, and PyTorch.
Practical problem-solving skills through real-world projects.

Key Features:

Duration: 19 months (750+ hours of learning).
Projects: 15+ industry projects and case studies.
Certifications: Master’s Degree from LJMU and advanced certifications from IIIT Bangalore.
Career Support:
- 1:1 mentorship sessions.
- Mock interviews and AI-powered profile building.
- Access to a job portal with exclusive opportunities.
Learning Experience: Live sessions with faculty from LJMU and IIIT-B, combined with pre-recorded content.

Who It’s For:

Professionals with 1+ years of experience in a technical domain.
Learners with a background in mathematics, statistics, or programming.
AI enthusiasts aiming for leadership roles in ML and AI.

Machine Learning Applications You See Every Day

Machine Learning is a part of our daily lives, often unnoticed. Here are some common ways it’s used across different industries:

Industry	Application	Example
Healthcare	Diagnoses diseases and predicts health risks.	Identifying early signs of diabetes.
Retail	Provides product recommendations and dynamic pricing.	Suggesting items based on shopping history.
Finance	Detects fraud and assesses loan eligibility.	Flagging unusual banking transactions.
Entertainment	Recommends movies, shows, or music.	Suggesting a playlist based on recent songs.
Transportation	Allocates rides and powers self-driving cars.	Optimizing routes for rideshare drivers.
Manufacturing	Detects defects and predicts equipment issues.	Alerting for maintenance before breakdowns.
Education	Adapts lessons and detects plagiarism.	Customizing lessons for a student’s pace.
Customer Service	Chatbots handle queries and analyze feedback.	Resolving order-related issues in real time.

What Jobs Can You Get After Learning Machine Learning?

Machine Learning offers a variety of career opportunities in India. Here are some roles you can pursue, their key responsibilities, and the typical salary range:

Job Role	Responsibilities	Average Salary (INR)
Machine Learning Engineer	Develop and deploy ML models; work with large datasets to create predictive systems.	₹11,75,000 per year
Data Scientist	Analyze data to extract insights; apply ML algorithms to solve business challenges.	₹8,00,000 to ₹12,00,000 per year
AI Engineer	Design AI models; integrate AI solutions into existing systems.	₹9,00,000 to ₹14,00,000 per year
Data Analyst	Interpret complex data sets; assist in decision-making processes.	₹4,00,000 to ₹8,00,000 per year
Business Intelligence Analyst	Create data visualizations; report on business performance metrics.	₹5,00,000 to ₹9,00,000 per year
NLP Specialist	Work on language processing tasks like translation and sentiment analysis.	₹7,00,000 to ₹12,00,000 per year
Computer Vision Engineer	Develop systems for image and video analysis; implement object detection algorithms.	₹8,00,000 to ₹13,00,000 per year
Automation Engineer	Design automated systems; apply ML for process improvements.	₹6,00,000 to ₹10,00,000 per year
Robotics Engineer	Integrate ML into robotics; work on navigation and control systems.	₹7,00,000 to ₹12,00,000 per year

Note: Salaries can vary based on experience, location, and company size.

How to Transition from Free to Advanced Machine Learning Courses

Completing free machine learning course with certificate is a great start, but advancing your skills can open up even better opportunities. Here’s how you can make the leap to more specialized and advanced areas in machine learning:

Steps to Transition to Advanced Machine Learning

Choose a Specialization:
Focus on specific areas like Natural Language Processing (NLP), deep learning, or computer vision. These fields are in high demand and offer great career prospects.
Enroll in Paid Certifications:
Invest in certifications like TensorFlow Developer, Advanced Machine Learning, or AI for Everyone. These programs offer in-depth learning and hands-on experience.
Work on Real-World Projects:
Build a portfolio by solving real-world problems. Use platforms like Kaggle to participate in competitions or collaborate on open-source projects to showcase your skills.
Learn Advanced Tools and Frameworks:
Gain expertise in tools like PyTorch, TensorFlow, or Keras, which are essential for advanced machine learning tasks.
Seek Mentorship or Join Communities:
Connect with industry experts or join machine learning communities to learn from others’ experiences and get guidance.

How upGrad Helps You Build a Career in Machine Learning

Machine Learning is one of the most exciting and in-demand fields today, and upGrad makes it easier than ever to start or advance your career in this space. With top-notch programs, real-world projects, and unmatched career support, here’s how upGrad helps you succeed:

Why Choose upGrad for Machine Learning?

Learn from Global Experts: Programs designed with top universities ensure you learn from the best.
Real-World Projects: Work on practical challenges that enhance your portfolio.
Flexible Learning: Study online, at your own pace, without disrupting your schedule.
Comprehensive Career Support:
- One-on-one mentorship from industry professionals.
- Mock interviews and resume-building workshops.
- Exclusive job portal with 1,000+ hiring partners.

Career Outcomes with upGrad

Salary Growth: Graduates see an average salary hike of 51%.
Top Employers: Opportunities at leading companies like Google, Amazon, and Microsoft.

What Makes upGrad Stand Out?

Globally Recognized Certifications: Add credibility to your resume with certifications from top institutions.
Hands-On Learning: Build your skills by solving real-world problems.
Tailored Career Services: Personalized support to help you land your dream role.

With upGrad, you gain the skills, experience, and support needed to excel in Machine Learning. Start your career today!

Expand your expertise with the best resources available. Browse the programs below to find your ideal fit in Best Machine Learning and AI Courses Online.

Best Machine Learning and AI Courses Online

Master of Science in Machine Learning & AI from LJMU	Executive Post Graduate Programme in Machine Learning & AI from IIITB	Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland
Advanced Certificate Programme in Machine Learning & NLP from IIITB	Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB	View all Machine Learning Courses

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