Free Online Machine Learning Course with Certificate [2025]
Updated on Aug 19, 2025 | 7 min read | 8.17K+ views
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| Netflix rakes in a whopping $1 billion every year, all thanks to its machine learning-powered recommendation system? This tech boost not only keeps viewers hooked but also highlights why 61% of marketers say AI and ML are game-changers for their growth strategies. It's clear-businesses everywhere are tapping into the power of machine learning to stay ahead of the competition and deliver ultra-personalized experiences! |
Machine Learning is now one of the most popular skills in tech. It’s used in everything from movie recommendations to self-driving cars. If you want to start learning it or improve your skills, a free online course with a certificate is a great way to begin. Plenty of good options don’t cost anything and still offer real value for your career.
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.
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If you're aiming to build a solid foundation in Machine Learning and AI, there are some excellent free courses available that offer certification. These courses are ideal whether you're exploring AI for the first time or looking to sharpen your skills to meet industry demands. Before diving into the details, here’s a quick overview of the top free ML & AI certification courses and where you can take 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 |
Let’s explore what each course offers, who it’s best suited for, and how it can help you build strong AI and machine learning skills, whether you’re a beginner or looking to specialize.
1. Introduction to Generative AI – Free Certificate by upGrad
This beginner-friendly course introduces you to generative AI, covering AI-driven creativity and real-world use cases. Over just 2 hours, you’ll learn about Large Language Models (LLMs), tips & tricks for using generative AI, and how businesses adopt this technology.
AI Skills You’ll Learn:
- Basics of generative AI and LLMs
- Real-world applications of AI-generated content
- Practical insights into enterprise adoption of AI
- Hands-on tips for working with AI tools
Who Should Opt for This Course?
- Beginners curious about AI creativity
- Professionals wanting to apply generative AI in projects
- Students exploring AI technologies
2. Artificial Intelligence in the Real World – Free Certificate by upGrad
Explore AI applications across industries like healthcare and finance in this 7-hour course. You’ll learn foundational AI concepts, random variables in AI, and the differences between machine learning and deep learning.
AI Skills You’ll Learn:
- Different types of AI and their applications
- Machine learning vs. deep learning explained
- How AI impacts business operations and decision-making
- Strategies for integrating AI into real-world projects
Who Should Opt for This Course?
- Anyone interested in practical AI use cases
- Students and professionals aiming to understand AI’s business impact
- Beginners wanting a broad introduction to AI
3. Fundamentals of Deep Learning and Neural Networks – Free Certificate by upGrad
This 28-hour course gives you a solid foundation in deep learning and neural networks. You’ll study neural network architecture, perceptrons, and training methods, understanding how AI models mimic brain function.
AI Skills You’ll Learn:
- Deep learning fundamentals and network structures
- Perceptron design and training techniques
- How artificial neurons process information
- Application of neural networks in AI systems
Who Should Opt for This Course?
- Beginners ready to dive deep into AI models
- Developers interested in neural network architectures
- Students preparing for advanced AI topics
4. Unsupervised Learning: Clustering – Free Certificate by upGrad
Master the core clustering algorithms like K-Means and Hierarchical clustering in this 11-hour course. You’ll apply Python libraries for clustering, explore real-world business cases, and learn how to segment data effectively.
AI Skills You’ll Learn:
- Key clustering methods and their differences
- Python implementation with Scikit-learn and Seaborn
- Practical applications in marketing, fraud detection, and customer profiling
- Data preprocessing for unsupervised learning
Who Should Opt for This Course?
- Beginners learning unsupervised ML
- Data enthusiasts wanting a hands-on Python experience
- Marketers and analysts interested in customer segmentation
Also read: Machine Learning Datasets Project Ideas for Beginners: Real-World Projects to Build Your Portfolio
5. Microsoft Azure AI Fundamentals – Free Certificate by Microsoft Learn
This free course introduces you to AI concepts using Microsoft Azure tools. Over several hours, you’ll get hands-on experience with AI workloads, Azure services, and cognitive APIs, making it ideal if you want to build AI solutions on cloud platforms.
AI Skills You’ll Learn:
- Core AI workloads and concepts on Azure
- Using Azure Cognitive Services for vision, speech, and language
- Building AI models with cloud infrastructure
- Basics of responsible AI and ethical considerations
Who Should Opt for This Course?
- Beginners interested in AI cloud platforms
- IT professionals wanting to learn AI integration on Azure
- Developers seeking Azure certification prep
Must Read: Top 48 Machine Learning Projects [2025 Edition] with Source Code
6. Machine Learning Crash Course with TensorFlow APIs – Free Certificate by Google AI
This practical crash course uses TensorFlow APIs to teach you key machine learning concepts and techniques. You’ll get hands-on experience with training models, working with datasets, and understanding ML workflow.
AI Skills You’ll Learn:
- Fundamentals of machine learning and TensorFlow
- Data pipeline creation and feature engineering
- Model training, evaluation, and deployment
- Real-world ML problem solving with Google tools
Who Should Opt for This Course?
- Developers wanting to use TensorFlow for ML projects
- Beginners ready for a coding-first approach to ML
- Students and professionals looking to solidify ML basics
Also Read: Machine Learning Applications in Healthcare: What Should We Expect?
Career Opportunities in Machine Learning
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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
To start a career in machine learning, you must first master the relevant skills and information. Furthermore, having hands-on experience through projects and internships can considerably increase your chances of landing a machine-learning position.
As the need for machine learning specialists grows, so will the income opportunities. Machine learning professionals fetch lucrative wages because of their specialized expertise and the value they provide to organizations. Furthermore, the profession provides several prospects for advancement and continual learning, making it an intriguing career choice.
Also Read: Top 30 Machine Learning Skills for ML Engineers in 2024
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!
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
Discover in-demand Machine Learning skills to expand your expertise. Explore the programs below to find the perfect fit for your goals.
In-demand Machine Learning Skills
Discover popular AI and ML blogs and free courses to deepen your expertise. Explore the programs below to find your perfect fit.
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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
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?
Math is essential for understanding how algorithms work under the hood. Focus on linear algebra (for data representation), probability and statistics (for model evaluation), and calculus basics (for optimization). You don’t need to be a math wizard, but a good grasp will help you debug models and develop custom solutions rather than relying solely on libraries.
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.
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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