Machine Learning Free Online Course with Certificate
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Machine Learning has become one of the most in-demand skills in the tech world, powering everything from personalized movie recommendations to autonomous vehicles. If you’re looking to start your ML journey or sharpen your existing knowledge, enrolling in a free online course with a certificate is an excellent choice. These courses not only come at zero cost but also provide meaningful career value through practical learning and recognized certification.
In this blog, you’ll discover the best free machine learning courses that offer certificates. You’ll learn what each course covers, where to access them, and how they can help you take the next step in your learning or career journey.
Best Machine Learning and AI Courses Online
Popular AI Programs
If your goal is to establish a strong foundation in Machine Learning and AI, several high-quality free courses with certification are available. These programs are perfect whether you’re just starting out in AI or seeking to enhance your skills to stay competitive in the industry. Before we explore each course in detail, here’s a brief overview of the top free ML and AI certification courses and the platforms where you can access them.
| Platform / Provider | Course Name |
| upGrad | Introduction to Generative AI – Free Certificate |
| upGrad | Artificial Intelligence in the Real World – Free Certificate |
| upGrad | Fundamentals of Deep Learning and Neural Networks – Free Certificate |
| upGrad | Unsupervised Learning: Clustering – Free Certificate |
| Microsoft Learn | Microsoft Azure AI Fundamentals – Free Certificate |
| Google AI | Machine Learning Crash Course with TensorFlow APIs – Free Certificate |
To build a strong foundation in machine learning and apply it across real business problems, explore these top upGrad programs:
1. Introduction to Generative AI – Free Certificate by upGrad
This beginner-friendly course introduces the fundamentals of generative AI, highlighting AI-driven creativity and real-world applications. In just 2 hours, you’ll explore Large Language Models (LLMs), practical tips for working with generative AI, and how businesses leverage this technology.
AI Skills You’ll Gain:
- Understanding the basics of generative AI and LLMs
- Real-world applications of AI-generated content
- Practical insights into enterprise AI adoption
- Hands-on tips for interacting with AI tools
Who Should Enroll:
- Beginners curious about AI-powered creativity
- Professionals looking to apply generative AI in projects
- Students exploring emerging AI technologies
2. Artificial Intelligence in the Real World – Free Certificate by upGrad
This 7-hour course explores AI applications across industries such as healthcare and finance. You’ll gain foundational knowledge of AI concepts, understand random variables in AI, and learn the distinctions between machine learning and deep learning.
AI Skills You’ll Gain:
- Understanding different types of AI and their applications
- Clear differentiation between machine learning and deep learning
- Insight into AI’s impact on business operations and decision-making
- Strategies for applying AI in real-world projects
Who Should Enroll:
- Anyone interested in practical AI use cases
- Students and professionals aiming to understand AI’s business impact
- Beginners seeking a comprehensive AI introduction
3. Fundamentals of Deep Learning and Neural Networks – Free Certificate by upGrad
This 28-hour course offers a deep dive into neural networks and deep learning. You’ll study network architectures, perceptrons, and training methods to understand how AI models simulate brain function.
AI Skills You’ll Gain:
- Core concepts of deep learning and network structures
- Design and training of perceptrons
- How artificial neurons process information
- Applying neural networks in AI systems
Who Should Enroll:
- Beginners eager to explore advanced AI models
- Developers interested in neural network architecture
- Students preparing for advanced AI topics
4. Unsupervised Learning: Clustering – Free Certificate by upGrad
Master key clustering techniques such as K-Means and Hierarchical Clustering in this 11-hour course. Learn to implement clustering algorithms in Python, analyze real-world business cases, and segment data effectively.
AI Skills You’ll Gain:
- Understanding core clustering methods and their differences
- Python implementation using Scikit-learn and Seaborn
- Practical applications in marketing, fraud detection, and customer profiling
- Data preprocessing for unsupervised learning
Who Should Enroll:
- Beginners interested in unsupervised machine learning
- Data enthusiasts seeking hands-on Python experience
- Marketers and analysts aiming to apply clustering for customer segmentation
If you want to strengthen your foundation in algorithm design and model building, upGrad’s free Data Structures & Algorithms course is a great starting point. Learn sorting techniques, runtime analysis, and practical tools like arrays and stacks, all through expert-led lessons with certification.
5. Microsoft Azure AI Fundamentals – Free Certificate by Microsoft Learn
This course introduces AI concepts using Microsoft Azure tools. Over several hours, you’ll gain hands-on experience with AI workloads, Azure services, and cognitive APIs—perfect for building AI solutions on cloud platforms.
AI Skills You’ll Gain:
- Core AI workloads and concepts on Azure
- Utilizing Azure Cognitive Services for vision, speech, and language tasks
- Building AI models in cloud environments
- Basics of responsible AI and ethical considerations
Who Should Enroll:
- Beginners interested in AI cloud platforms
- IT professionals looking to integrate AI on Azure
- Developers preparing for Azure AI certification
6. Machine Learning Crash Course with TensorFlow APIs – Free Certificate by Google AI
This practical crash course uses TensorFlow APIs to teach fundamental machine learning concepts. You’ll gain hands-on experience with model training, dataset management, and the end-to-end ML workflow.
AI Skills You’ll Gain:
- Fundamentals of machine learning and TensorFlow
- Building data pipelines and performing feature engineering
- Training, evaluating, and deploying models
- Solving real-world ML problems using Google tools
Who Should Enroll:
- Developers looking to apply TensorFlow for ML projects
- Beginners ready for a coding-focused approach to ML
- Students and professionals seeking to solidify core ML skills
Also read: Machine Learning Datasets Project Ideas for Beginners: Real-World Projects to Build Your Portfolio
How to Select the Right Free Machine Learning Course
Choosing the right free machine learning course is essential to ensure you gain the knowledge and skills that match your career goals. Not all free courses are created equal—some are ideal for beginners, while others are more suitable for learners with prior coding or AI experience. Here’s a detailed guide to help you make the best choice:
Define Your Learning Goals (Beginner vs Advanced)
Before enrolling, clearly identify your learning objectives:
- For Beginners: Look for courses that introduce fundamental concepts of AI and machine learning without requiring prior programming knowledge. Beginner-friendly courses often include practical examples, simplified explanations, and shorter durations.
- For Intermediate or Advanced Learners: If you already have basic ML knowledge, choose courses that focus on neural networks, deep learning, or specialized topics like clustering and TensorFlow implementation. These courses provide hands-on coding experience and deeper technical insights.
Check Course Duration and Flexibility
Time commitment and flexibility are important factors:
- Duration: Free courses vary from a couple of hours (e.g., Introduction to Generative AI) to 28+ hours (Fundamentals of Deep Learning). Consider your availability and learning pace before enrolling.
- Self-Paced vs Scheduled: Self-paced courses allow you to learn at your own speed, ideal for working professionals. Scheduled courses with fixed timelines may offer interactive sessions or cohort-based learning, which can be helpful for accountability and networking.
- Workload Balance: Ensure the course structure fits your daily routine. Short, focused courses are better for quick skill acquisition, while longer courses are suited for comprehensive mastery.
Must Read: Top 48 Machine Learning Projects [2025 Edition] with Source Code
Verify Certificate Recognition
A key benefit of these courses is the certificate you receive upon completion. Before choosing a course:
- Check Credibility: Verify whether the certificate is issued by a recognized platform or institution, such as upGrad, Microsoft, or Google AI.
- Employer Recognition: Some certificates carry more weight in industry hiring. Certificates from well-known organizations can add credibility to your resume and LinkedIn profile.
- Digital Verification: Many platforms provide verifiable digital certificates, which can be shared online or attached to portfolios.
Evaluate Course Provider Reputation
The platform offering the free course plays a big role in course quality:
- Platform Credibility: UpGrad, Microsoft Learn, and Google AI are widely recognized and trusted in the AI and ML community.
- Instructor Expertise: Check whether courses are taught by industry experts or academics. Expert-led courses often provide practical insights, case studies, and real-world examples.
- Reviews & Ratings: Read learner reviews and ratings to understand the course experience, content depth, and engagement quality.
- Support & Resources: Good platforms provide additional resources such as forums, Q&A sessions, downloadable materials, and project-based learning opportunities.
Also Read: Machine Learning Applications in Healthcare: What Should We Expect?
Tips to Maximize Learning from Free ML Courses
Taking a free machine learning course is just the first step. To truly benefit, you need to actively apply your learning and build practical skills. Here are some strategies to get the most out of your free ML courses:
Build a Consistent Study Plan
Consistency is key when learning complex topics like ML and AI:
- Set a Schedule: Allocate dedicated time each day or week for course modules, exercises, and hands-on practice. Even 1–2 hours daily can lead to steady progress.
- Break Down Content: Divide the course into smaller, manageable segments. Focus on completing one concept or module at a time.
- Track Progress: Maintain a learning log to record completed lessons, notes, and questions for review.
- Reinforce Learning: Revisit concepts regularly to retain knowledge, especially foundational topics like linear regression, classification, and neural networks.
Participate in ML Competitions (Kaggle, Hackathons)
Practical experience is crucial for mastering machine learning:
- Kaggle Competitions: Platforms like Kaggle provide real datasets and competitions where you can apply ML algorithms, experiment with models, and learn from community solutions.
- Hackathons: Participate in AI/ML hackathons to solve real-world problems under time constraints. This builds problem-solving skills and coding efficiency.
- Learning Through Challenges: Competitions expose you to advanced techniques, feature engineering strategies, and model optimization practices that are rarely covered in free courses.
- Resume & Portfolio: Showcasing competition results can significantly boost your credibility with employers.
Also Read: Top 30 Machine Learning Skills for ML Engineers in 2024
Network with ML Communities
Engaging with peers and professionals accelerates learning:
- Online Communities: Join forums and platforms like Reddit’s r/MachineLearning, LinkedIn ML groups, or AI Discord channels.
- Discussion & Collaboration: Ask questions, share project updates, and participate in discussions to deepen your understanding.
- Mentorship Opportunities: Networking may lead to mentorship from experienced ML practitioners, which can provide guidance on projects and career paths.
- Stay Updated: Communities often share the latest research papers, free resources, and workshops that enhance your learning journey.
Create a Portfolio with Projects
A strong portfolio demonstrates practical skills and reinforces learning:
- Start Small: Begin with simple ML projects like predicting house prices or analyzing customer churn.
- Use Real Datasets: Platforms like Kaggle, UCI Machine Learning Repository, and Google Dataset Search provide datasets for practical experimentation.
- Document Your Work: Include project descriptions, methodologies, code snippets, results, and visualizations.
- Showcase Online: Share your projects on GitHub, LinkedIn, or personal websites to attract potential employers or collaborators.
- Focus on Variety: Include projects from multiple areas—supervised learning, unsupervised learning, deep learning—to display a well-rounded skill set.
Benefits of Online Courses in Machine Learning
These courses help learners build and expand their AI and Machine Learning skills through a structured approach:
Machine Learning Courses to upskill
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IIIT Bangalore
Executive Diploma in Machine Learning and AI360° Career Support
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- Foundational Concepts – Covers basic principles and theories.
- Beginner Level – Introduces newcomers to the field.
- Valuable Insights – Provides key understandings and perspectives.
- Practical Experience – Offers hands-on application and practice.
- Advanced Techniques – Explores sophisticated methods and tools.
- Expertise Deepening – Helps experienced learners expand their knowledge.
Career Opportunities in Machine Learning
Aspiring professionals now have many job options because of the increased need for machine learning expertise. Let’s look at some of the most fascinating jobs in machine learning:
Below is a comparison of some of the most in-demand roles in India, along with their average salary ranges in 2025.
| Role | Key Responsibilities | Average Salary (2025) |
| Machine Learning Engineer | Design, build, and deploy machine learning models and systems. Expert in programming, algorithms, and scaling ML solutions. | ₹9.5 – ₹11.5 LPA |
| Data Scientist | Analyze large datasets, derive insights, and apply statistical and machine learning techniques to solve business problems. | ₹10 – ₹37 LPA |
| AI Researcher | Conduct innovative research to develop new AI/ML models and algorithms. Push the boundaries of AI capabilities. | ₹26 – ₹30 LPA |
| Data Engineer | Build and maintain data infrastructure. Ensure reliable data pipelines for data analysis and machine learning workflows. | ₹10 – ₹11 LPA |
| Business Intelligence Analyst | Use machine learning and analytics to interpret business data and provide insights to stakeholders for strategic decision-making. | ₹7.5 – ₹8.2 LPA |
| Machine Learning Consultant | Help organizations adopt machine learning by identifying use cases, designing solutions, and aligning them with business goals. | ₹14 – ₹15.4 LPA |
Source: Scaler, Ambitionbox
Why Choose upGrad’s Machine Learning Free Online Course?
An excellent first step is to enroll in a free online machine learning course with certification. Enroll in a trusted course that covers the fundamentals, such as algorithms, data processing, model training, and evaluation. The skills you gain will open doors in various fields, such as AI development, data analysis, and automation.
You can choose from plenty of platforms in 2025 to start your journey with a trusted machine learning course. Here's why you should consider one such platform like upGrad:
- 1 on 1 Industry Mentorship: You get to study exclusively from a leading Machine Learning expert. It would allow you to remove any doubts regarding the subject matter.
- Industry-Expert Curriculum: All the courses are the product of seasoned industry experts, who ensure that you understand all the concepts as quickly as possible.
- Weekly Live Lectures: Your instructor will give live lectures every week. Live interactions help streamline the learning experience.
- Free Certificate: After you complete the machine learning course for free, you’ll get a certificate to enhance your resume.
If you're ready to deepen your AI expertise and start building robust algorithms, here are some additional upGrad courses that can help you upskill and put these techniques into practice.
If you're ready to take the next step in your career, connect with upGrad’s career counseling for personalized guidance. You can also visit a nearby upGrad center for hands-on training to enhance your generative AI skills and open up new career opportunities!
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Frequently Asked Questions (FAQs)
1. What’s the best way to start learning machine learning if I have no prior experience in AI?
If you’re new to AI, start with foundational courses that cover basic statistics, Python programming, and core ML concepts like supervised and unsupervised learning. Focus on hands-on projects and gradually explore libraries like scikit-learn. Avoid jumping into deep learning too soon; build a solid base first.
2. Can I learn machine learning effectively without a degree in computer science?
Absolutely. Many successful AI practitioners come from diverse backgrounds. Online courses, bootcamps, and open-source projects can teach you what you need. However, dedication to learning programming and math basics is crucial since those are foundational skills in AI.
3. What are the three main types of machine learning?
The three main paradigms are Supervised Learning, where the model learns from labeled data (e.g., classifying emails as spam or not spam); Unsupervised Learning, where the model finds patterns in unlabeled data (e.g., grouping customers into different segments); and Reinforcement Learning, where an agent learns to make decisions by performing actions and receiving rewards or penalties (e.g., training a bot to play a game).
4. How necessary is math knowledge in AI, and which topics should I focus on?
A free ML course provides foundational knowledge and a certificate, but securing a job also requires hands-on projects, coding proficiency, advanced learning, and networking within the machine learning community to gain practical experience.
5. Which programming languages and tools should I focus on to build a career in AI?
Python is the most popular language for AI due to its rich ecosystem of libraries (scikit-learn, TensorFlow, PyTorch). R is useful for statistics-heavy projects. Familiarize yourself with Jupyter notebooks, version control (Git), and cloud platforms like AWS or Google Cloud for scalable AI solutions.
6. What is the difference between TensorFlow and PyTorch?
Both are leading deep learning frameworks, but they have different philosophies. TensorFlow is known for its robust production deployment capabilities and its comprehensive ecosystem (TensorFlow Extended). PyTorch is often praised for its flexibility, ease of use, and intuitive "Pythonic" feel, making it a favorite in the research community. The choice often comes down to project needs and personal preference.
7. What is data preprocessing, and why is it so important?
Data preprocessing is the crucial step of cleaning and preparing raw data to make it suitable for a machine learning model. This includes tasks like handling missing values, scaling numerical features, and encoding categorical variables. It's often said that 80% of a machine learning project is data preprocessing, as the quality of the data directly determines the performance and accuracy of the model.
8. How do I handle imbalanced datasets in machine learning projects?
Common strategies include resampling techniques like oversampling the minority class or under sampling the majority class, using synthetic data generation (SMOTE), and choosing evaluation metrics such as precision-recall or F1 score over accuracy to measure model performance better.
9. What is overfitting, and how can I prevent it?
Overfitting happens when a model learns the training data too well, including its noise, and as a result, it performs poorly on new, unseen data. You can prevent it by using more training data, simplifying the model, using techniques like cross-validation, or applying regularization methods (like L1/L2) that penalize model complexity.
10. How important is deep learning compared to classical machine learning techniques?
Deep learning is powerful for tasks involving unstructured data like images, audio, and text, but classical ML methods are still very effective for tabular data and smaller datasets. Understanding both allows you to choose the best approach for a problem rather than defaulting to deep learning.
11. What are hyperparameters, and why is tuning them important?
Hyperparameters are the configuration settings that are set before the training process begins, such as the learning rate in a neural network or the number of trees in a random forest. Tuning them is crucial because the right set of hyperparameters can significantly improve a model's performance. Techniques like Grid Search or Randomized Search are used to find the optimal combination.
12. What are the best resources for learning ML, and can I find a free machine learning course?
There are many excellent resources available. For a structured path, platforms like upGrad offer comprehensive programs. For self-starters, many universities put their materials online, and yes, you can find a high-quality free machine learning course from institutions like Stanford or from tech giants like Google and Microsoft. The key is to find a Machine Learning Course that combines theory with hands-on coding projects.
13. What are some of the most important evaluation metrics for a model?
The choice of metric depends on the problem. For classification, Accuracy is a common start, but Precision, Recall, and the F1-Score are crucial for imbalanced datasets. The AUC-ROC curve is also used to measure a model's performance across different thresholds. For regression tasks, common metrics include Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
14. How do I choose the right machine learning specialization for my career goals?
Identify whether you want to work in data science, computer vision, NLP, or reinforcement learning. Each has its tools and techniques, so look for courses and projects aligned with your interests. Consider industry demand, your background, and your desired job roles when picking a specialization.
15. What are the must-have projects to include in my AI portfolio to impress recruiters?
Recruiters look for projects that solve real-world problems with clear objectives and outcomes. Examples include image classification, sentiment analysis, recommendation systems, or time-series forecasting. Make sure your code is clean and documented and that you can confidently explain the model choices and performance metrics.
16. What career paths are available for machine learning engineers versus data scientists?
Machine learning engineers focus on building and deploying models into production, requiring strong software engineering skills. Data scientists emphasize data analysis, feature engineering, and deriving insights from data. Both roles overlap but require different skill sets and day-to-day tasks.
17. What’s the best way to transition from traditional software development to AI and machine learning roles?
Leverage your programming skills by starting with ML libraries like TensorFlow or PyTorch. Take project-based courses focused on AI workflows. Also, on your resume and in interviews, emphasize your understanding of data preprocessing, feature engineering, and model evaluation.
18. How do I stay updated with the latest AI and machine learning advancements?
Follow top conferences like NeurIPS, ICML, and CVPR, and read papers from arXiv. Subscribe to newsletters from sources like MIT Technology Review or DeepLearning.AI, and join AI communities on Reddit, LinkedIn, or Stack Overflow. Practical experimentation with new models and frameworks also helps you stay current.
19. What is the difference between a model's parameters and hyperparameters?
Parameters are the internal variables that the model learns on its own from the training data, such as the weights and biases in a neural network. Hyperparameters are the external, high-level settings that you, the developer, configure before training, such as the learning rate or the number of layers in the network.
20. What is transfer learning, and why is it useful?
Transfer learning is a powerful technique where a model that was pre-trained on a large dataset for one task (like identifying millions of images from the internet) is repurposed and fine-tuned for a second, related task (like identifying specific types of medical images). This is incredibly useful because it allows you to achieve high performance on your specific task without needing a massive dataset or extensive training time.
References:
https://www.businessinsider.com/netflix-recommendation-engine-worth-1-billion-per-year-2016-6
https://www.scaler.com/blog/data-scientist-salary-in-india/
https://www.ambitionbox.com/profile/machine-learning-engineer-salary
900 articles published
Pavan Vadapalli is the Director of Engineering , bringing over 18 years of experience in software engineering, technology leadership, and startup innovation. Holding a B.Tech and an MBA from the India...
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