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20+ Best Courses to Learn Machine Learning from Scratch

Last Updated on January 30th, 2026, 49

Best Machine Learning Courses Online (2026 Complete Guide)

Machine Learning (ML) is one of the most powerful technologies shaping the world today. From recommendation systems and fraud detection to chatbots, automation, and self-driving cars, ML powers thousands of real-world applications. If you want to build a career in Data Science, AI, or Analytics, learning Machine Learning is the first and most important step.

Machine learning courses help you understand the mathematics behind models, core algorithms, and how these are applied in real-world AI systems. Whether you aim to become an AI engineer, data scientist, or ML professional, structured learning is essential to build strong foundations.

Why Learn Machine Learning?

  • High-demand skill across industries like IT, finance, healthcare, e-commerce, robotics, and more

  • Great career growth in roles such as ML Engineer, Data Scientist, AI Engineer, and Analyst

  • Helps you build intelligent systems that can predict, automate, and optimize tasks

  • The core foundation for advanced topics like Deep Learning, NLP, Computer Vision, and AI

  • Boosts problem-solving and analytical skills

  • Enables you to work on real-world applications like recommendation engines, forecasting, detection systems, and automation

  • ML professionals earn some of the highest salaries in the tech industry

  • Python + ML is one of the most beginner-friendly pathways into AI

Mathematics Included in Machine Learning Courses

Good machine learning courses include essential math concepts such as:

  • Linear algebra (vectors, matrices)

  • Probability and statistics

  • Mean, variance, and distributions

  • Gradient descent and optimization

  • Cost and loss functions

  • Basic calculus for model training

These topics are explained in a practical and beginner-friendly way.

Algorithms Covered in Machine Learning Training

Most professional ML courses cover key algorithms like:

  • Linear and logistic regression

  • k-Nearest Neighbors (KNN)

  • Support Vector Machines (SVM)

  • Naive Bayes

  • Decision Trees and Random Forests

  • K-Means clustering

  • Principal Component Analysis (PCA)

Understanding how and when to use these algorithms is a core focus.

Professional Machine Learning & AI Certifications

Many machine learning courses also prepare you for professional certifications, such as:

  • Machine Learning certifications

  • AI and data science professional certificates

  • Industry-recognized online certifications

  • University-backed AI programs

These certifications help validate your ML skills for jobs and research roles.

Best Machine Learning Online Courses with Certification

1

Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2026] (Udemy)

This bestselling Machine Learning course teaches you how to build real-world machine learning models using both Python and R. Designed by data science experts, it helps you understand the intuition behind algorithms while applying them to practical business and personal use cases.

Course Details:
Rating: 4.5/5 | Learners: 1,174,301+ | Duration: 42.5 hrs | Level: Beginner to Advanced | Certificate: Yes | Access: Full Lifetime Access |

Learn machine learning from scratch using Python and R. Build powerful predictive models, perform data analysis, and apply techniques such as regression, classification, clustering, dimensionality reduction, reinforcement learning, NLP, and deep learning. Understand how to choose the right model for each problem and create production-ready ML solutions.

2

Machine Learning, Data Science & AI Engineering with Python (Udemy)

This comprehensive machine learning and AI engineering course focuses on building real-world data science, deep learning, and generative AI solutions using Python. It combines theoretical foundations with hands-on projects to prepare learners for modern AI roles.

Course Details:
Rating: 4.6/5 | Learners: 240,616+ | Duration: 21 hrs | Level: Beginner to Advanced | Certificate: Yes | Access: Full Lifetime Access |

Learn machine learning, data science, and AI engineering with Python. Build generative AI systems using OpenAI, RAG, and LLM agents, develop neural networks with TensorFlow and Keras, work with Spark MLlib, apply regression and classification algorithms, perform clustering and PCA, create recommender systems, and understand reinforcement learning and A/B testing.

3

Machine Learning Specialization – Andrew Ng (Coursera)

This world-class Machine Learning Specialization is a beginner-friendly program created by DeepLearning.AI and Stanford University, taught by AI pioneer Andrew Ng. It provides a strong foundation in modern machine learning concepts with hands-on practice using Python.

Course Details:
Rating: 4.9/5 | Learners: 744,065+ | Duration: ~2 months (10 hrs/week) | Level: Beginner | Certificate: Yes | Access: Flexible Schedule |

Learn machine learning fundamentals with Python using NumPy, scikit-learn, and TensorFlow. Build supervised models such as linear and logistic regression, neural networks, and decision trees. Apply unsupervised learning techniques like clustering and anomaly detection, create recommender systems, and build deep reinforcement learning models while following industry best practices used in Silicon Valley.

4

Supervised Machine Learning: Regression and Classification (Coursera)

This beginner-friendly course is the first part of the Machine Learning Specialization by Andrew Ng. It focuses on core supervised learning techniques and helps learners build a strong foundation in machine learning using Python and industry-standard libraries.

Course Details:
Rating: 4.9/5 | Learners: 1,131,148+ | Duration: ~3 weeks (10 hrs/week) | Level: Beginner | Certificate: Yes | Access: Flexible Schedule |

Learn supervised machine learning using Python with NumPy and scikit-learn. Build and train models for prediction and binary classification, including linear regression and logistic regression. Understand data preprocessing, model evaluation, and best practices to develop machine learning models that perform well on real-world data.

5

[2026] Machine Learning: Natural Language Processing (V2) (Udemy)

This highest-rated NLP course teaches you how to build real-world Natural Language Processing systems using Python. It covers both classical machine learning and modern deep learning approaches, helping you understand the foundations behind today’s generative AI models.

Course Details:
Rating: 4.8/5 | Learners: 27,022+ | Duration: 23.5 hrs | Level: Intermediate to Advanced | Certificate: Yes | Access: Full Lifetime Access |

Learn NLP from scratch using Python with NLTK, scikit-learn, and TensorFlow. Convert text into vectors using TF-IDF, word2vec, and GloVe, build search and similarity systems, implement sentiment analysis, spam detection, topic modeling, summarization, and language models. Understand key foundations behind modern AI systems like ChatGPT, GPT-4, and transformer-based models.

6

Machine Learning Bootcamp: Python, Projects & Deployment (Udemy)

This hands-on machine learning bootcamp is designed to take you from fundamentals to deployment by focusing on real-world projects and end-to-end ML workflows. It is ideal for learners who want practical experience building, serving, and deploying machine learning applications.

Course Details:
Rating: 4.8/5 | Learners: 110+ | Duration: 66.5 hrs | Level: Beginner to Advanced | Certificate: Yes | Access: Full Lifetime Access |

Learn machine learning with Python by building real-world projects. Cover core ML concepts including classification, regression, and unsupervised learning, along with the math behind ML. Perform data preprocessing, feature engineering, cross-validation, and hyperparameter tuning. Convert notebooks into production-ready apps, serve models using FastAPI and Streamlit, and deploy full ML applications on AWS EC2.

7

Mathematical Foundations of Machine Learning (Udemy)

This bestselling course focuses on the core mathematics behind machine learning, helping learners deeply understand how ML algorithms work under the hood. It is ideal for anyone who wants to strengthen their intuition in linear algebra and calculus for machine learning and deep learning.

Course Details:
Rating: 4.6/5 | Learners: 139,980+ | Duration: 16.5 hrs | Level: Beginner to Intermediate | Certificate: Yes | Access: Full Lifetime Access |

Master the mathematical foundations of machine learning, including linear algebra and calculus, with hands-on Python examples. Work with tensors using NumPy, TensorFlow, and PyTorch, apply matrix operations, eigenvectors, SVD, and PCA, understand gradients and gradient descent, compute derivatives of cost functions, and gain deeper insight into how modern machine learning and deep learning algorithms actually work.

8

Mathematics for Machine Learning Specialization (Coursera)

This beginner-friendly specialization builds the essential mathematical foundations required for machine learning and data science. It is designed to help learners develop strong intuition in linear algebra, calculus, and dimensionality reduction, and clearly connect these concepts to real-world machine learning applications.

Course Details:
Rating: 4.6/5 | Learners: 260,625+ | Duration: ~4 weeks (10 hrs/week) | Level: Beginner | Certificate: Yes | Access: Flexible Schedule |

Learn the core mathematics behind machine learning, including linear algebra, multivariate calculus, and dimensionality reduction with PCA. Work with vectors, matrices, derivatives, and optimization techniques, and apply concepts through hands-on Python projects using NumPy and Jupyter notebooks. Build the mathematical confidence needed to progress into advanced machine learning and data science courses.

9

Machine Learning Specialization – University of Washington (Coursera)

This intermediate-level Machine Learning Specialization is designed to help learners build intelligent, data-driven applications through hands-on case studies. Created by leading researchers from the University of Washington, it focuses on applying machine learning techniques to real-world problems using practical workflows.

Course Details:
Rating: 4.7/5 | Learners: 221,728+ | Duration: ~2 months (10 hrs/week) | Level: Intermediate | Certificate: Yes | Access: Flexible Schedule |

Gain applied machine learning experience through real-world case studies. Learn prediction, classification, clustering, and information retrieval techniques, analyze large datasets, evaluate models, and build intelligent systems that improve over time. Develop strong applied ML and Python skills suitable for production-ready applications.

10

Recommender Systems and Deep Learning in Python (Udemy)

This bestselling course provides an in-depth understanding of recommender systems using machine learning and deep learning techniques. It is ideal for learners who want to design accurate, scalable recommendation engines used in real-world applications.

Course Details:
Rating: 4.7/5 | Learners: 35,410+ | Duration: 13 hrs | Level: Intermediate to Advanced | Certificate: Yes | Access: Full Lifetime Access |

Learn how to build powerful recommender systems using Python. Implement matrix factorization and SVD with NumPy, deep learning models with Keras and TensorFlow, autoencoders, residual networks, and Restricted Boltzmann Machines. Work with big data recommendation systems using Spark on AWS EC2 to build scalable, production-ready solutions.

11

Microsoft Azure Machine Learning (Coursera)

This beginner-friendly Microsoft Azure Machine Learning course introduces core machine learning concepts and shows how to build and publish ML models using Azure Machine Learning Studio. It is ideal for learners who want to understand no-code and low-code ML solutions on the Azure platform.

Course Details:
Rating: 4.4/5 | Learners: 26,982+ | Duration: ~1 week (10 hrs/week) | Level: Beginner | Certificate: Yes | Access: Flexible Schedule |

Learn the fundamentals of machine learning using Microsoft Azure. Explore core ML concepts, common machine learning types, and predictive modeling. Use Azure Machine Learning Studio to build no-code ML solutions, understand MLOps basics, and prepare for the Microsoft Azure AI-900 (AI Fundamentals) certification exam.

12

Python for Data Science and Machine Learning Bootcamp (Udemy)

This bestselling Python bootcamp is a comprehensive introduction to data science and machine learning using Python. It focuses on practical skills, popular libraries, and real-world machine learning techniques widely used in industry.

Course Details:
Rating: 4.6/5 | Learners: 800,135+ | Duration: 25 hrs | Level: Beginner to Intermediate | Certificate: Yes | Access: Full Lifetime Access |

Learn data science and machine learning with Python using NumPy, Pandas, Matplotlib, Seaborn, Plotly, and scikit-learn. Implement core machine learning algorithms including linear and logistic regression, decision trees, random forests, SVMs, K-means clustering, neural networks, NLP, and spam filters. Gain hands-on experience with data analysis, visualization, and big data concepts using Spark.

13

TensorFlow for Deep Learning Bootcamp (Udemy)

This bestselling TensorFlow bootcamp teaches you how to build powerful deep learning models using TensorFlow 2. It is designed for learners who want to master deep learning techniques and apply them to real-world computer vision, NLP, and time-series problems.

Course Details:
Rating: 4.6/5 | Learners: 86,908+ | Duration: 62.5 hrs | Level: Beginner to Advanced | Certificate: Yes | Access: Full Lifetime Access |

Learn deep learning with TensorFlow 2 by building real-world models for computer vision, CNNs, NLP, and time-series forecasting. Work with image and text recognition, visualize neural network performance, integrate machine learning into applications, and gain hands-on experience through interactive notebooks to become a job-ready TensorFlow developer.

14

Machine Learning with Python (Coursera)

This intermediate-level course focuses on applying machine learning techniques using Python and scikit-learn. It emphasizes hands-on labs and real-world datasets to help learners build, evaluate, and optimize end-to-end machine learning solutions.

Course Details:
Rating: 4.7/5 | Learners: 645,569+ | Duration: ~2 weeks (10 hrs/week) | Level: Intermediate | Certificate: Yes | Access: Flexible Schedule |

Learn machine learning with Python using scikit-learn and Jupyter Notebooks. Apply regression, classification, clustering, and dimensionality reduction techniques, including PCA, t-SNE, and UMAP. Practice model evaluation, cross-validation, regularization, and pipeline optimization through hands-on labs and a real-world final project.

15

AWS Certified Machine Learning Engineer Associate: Hands On! (Udemy)

This bestselling hands-on course is designed to help you prepare confidently for the AWS Certified Machine Learning Engineer – Associate exam. It focuses on building, training, deploying, and managing machine learning solutions on AWS using real-world workflows.

Course Details:
Rating: 4.6/5 | Learners: 43,536+ | Duration: 23.5 hrs | Level: Intermediate | Certificate: Yes | Access: Full Lifetime Access |

Prepare for the AWS Certified Machine Learning Engineer Associate exam by mastering AWS machine learning services such as SageMaker and Bedrock. Learn data preparation, feature engineering, hyperparameter tuning, model training, deployment strategies, CI/CD automation, and best practices for securing, monitoring, and optimizing ML infrastructure on AWS.

16

Machine Learning for Absolute Beginners – Level 1 (Udemy)

This beginner-friendly course introduces the core concepts of artificial intelligence and machine learning in a simple, easy-to-understand way. It is ideal for learners with no prior background who want to understand how machine learning works before moving to hands-on coding courses.

Course Details:
Rating: 4.5/5 | Learners: 122,776+ | Duration: 4.5 hrs | Level: Beginner | Certificate: Yes | Access: Full Lifetime Access |

Understand the fundamentals of AI and machine learning, including supervised and unsupervised learning, classification and regression, clustering, dimensionality reduction, reinforcement learning, and generative AI. Learn key concepts such as features, labels, model training, and underfitting vs overfitting, making this a perfect starting point for absolute beginners.

17

Python Data Science: Unsupervised Machine Learning (Udemy)

This bestselling course focuses on unsupervised machine learning techniques using Python, helping learners analyze data without labeled outcomes. It is ideal for those who want to master clustering, anomaly detection, dimensionality reduction, and recommendation systems.

Course Details:
Rating: 4.6/5 | Learners: 3,829+ | Duration: 16.5 hrs | Level: Beginner to Intermediate | Certificate: Yes | Access: Full Lifetime Access |

Learn unsupervised machine learning with Python by working on clustering, anomaly detection, dimensionality reduction, and recommender systems. Apply K-Means, Hierarchical Clustering, DBSCAN, Isolation Forests, PCA, and t-SNE. Build recommendation engines using content-based and collaborative filtering techniques such as cosine similarity and SVD, with real-world data preparation and feature engineering.

18

Machine Learning & Deep Learning Masterclass for Beginners (Udemy)

This beginner-focused masterclass provides a complete, end-to-end introduction to machine learning and deep learning. It is designed for learners who want a structured learning path covering Python, mathematics, data preparation, model building, and real-world projects.

Course Details:
Rating: 4.5/5 | Learners: 1,468+ | Duration: 25.5 hrs | Level: Beginner | Certificate: Yes | Access: Full Lifetime Access |

Learn the complete machine learning and deep learning workflow from scratch. Master Python fundamentals, data collection and cleaning, EDA, feature engineering, PCA, and supervised learning models such as linear and logistic regression, KMeans, decision trees, random forests, and boosting algorithms. Explore deep learning with TensorFlow, neural networks, and GenAI concepts including NLP, chatbots with LLaMA, and text-to-image generation using Stable Diffusion.

19

Python & Machine Learning for Financial Analysis (Udemy)

This practical course focuses on applying Python and machine learning techniques to real-world financial analysis problems. It is ideal for learners who want to combine data science and machine learning skills with finance, banking, and investment use cases.

Course Details:
Rating: 4.5/5 | Learners: 103,008+ | Duration: 23 hrs | Level: Beginner to Intermediate | Certificate: Yes | Access: Full Lifetime Access |

Learn Python and machine learning with a strong focus on financial applications. Work with NumPy, Pandas, Matplotlib, and scikit-learn to analyze financial data, calculate portfolio returns, risk, and Sharpe ratios, and understand CAPM. Build and evaluate regression, classification, and clustering models, apply neural networks including ANN, RNN, and LSTM, and use feature engineering and model evaluation techniques for real-world finance and banking problems.

20

Machine Learning y Data Science: Curso Completo con Python (Udemy)

This bestselling Spanish-language course offers a complete introduction to machine learning and data science using Python. It focuses on both theory and practical implementation, helping learners build real-world machine learning projects from scratch.

Course Details:
Rating: 4.7/5 | Learners: 22,427+ | Duration: 30.5 hrs | Level: Beginner to Advanced | Certificate: Yes | Access: Full Lifetime Access |

Learn machine learning and data science in Spanish using Python. Understand core ML algorithms and mathematical intuition, apply techniques to real-world use cases, and build complete end-to-end machine learning projects. Work with offline and online environments, create predictive models, and apply machine learning techniques to complex domains such as cybersecurity.

21

Machine Learning e Data Science com Python de A a Z (Udemy)

This bestselling Portuguese-language course provides a complete, end-to-end introduction to machine learning and data science using Python. It combines strong theoretical foundations with extensive hands-on practice to prepare learners for real-world AI and data science roles.

Course Details:
Rating: 4.7/5 | Learners: 50,791+ | Duration: 45.5 hrs | Level: Beginner to Advanced | Certificate: Yes | Access: Full Lifetime Access |

Learn machine learning and data science in Portuguese from A to Z using Python. Work with NumPy, pandas, and scikit-learn to build classification, regression, clustering, association rules, and reinforcement learning models. Apply PCA, Kernel PCA, LDA, handle imbalanced datasets, detect outliers, build predictive models for finance and healthcare, implement NLP with spaCy, computer vision tasks, and time-series forecasting with ARIMA and Facebook Prophet.

22

Machine Learning avec Python : La formation complète (Udemy)

This bestselling French-language course teaches the fundamentals of machine learning with Python through real-world prediction models. It is ideal for learners who want a clear, practical introduction to machine learning concepts used in data science.

Course Details:
Rating: 4.5/5 | Learners: 6,742+ | Duration: 14 hrs | Level: Beginner to Intermediate | Certificate: Yes | Access: Full Lifetime Access |

Learn machine learning fundamentals in French using Python. Cover key algorithms such as k-Nearest Neighbors, linear and logistic regression, k-means clustering, and neural networks. Build and evaluate predictive models, apply holdout and k-fold cross-validation techniques, and gain hands-on experience with real data science use cases.

Common Questions About Machine Learning (FAQs)

1. Do I need a math background to learn Machine Learning?

Basic math helps, but many beginner-friendly courses teach concepts in a simple, intuitive way.

2. How long does it take to learn Machine Learning?

Beginners typically take 2–3 months to learn basics and 6–12 months to become job-ready with projects.

3. Which programming language should I use for ML?

Python is the most popular and beginner-friendly choice. Some courses also teach R.

4. Are ML courses beginner-friendly?

Yes. Most ML courses start from scratch and gradually introduce advanced topics.

5. Do these courses include real-world projects?

Yes — top ML courses include end-to-end projects, datasets, exercises, and hands-on coding.

6. What jobs can I get after learning ML?

You can apply for roles like Machine Learning Engineer, Data Scientist, AI Engineer, Analyst, and Research roles.

7. Do I need to know Python before starting ML?

Basic Python helps, but many courses teach Python basics alongside ML.

8. Is Machine Learning required for AI careers?

Yes. ML is the foundation of AI, and most AI roles require strong ML knowledge.

Who Should Learn Machine Learning?

  • Students interested in AI and data science

  • Software developers

  • Data analysts and engineers

  • AI and ML professionals

  • Researchers and advanced learners

Final Thoughts
Machine learning is the backbone of modern AI systems. Strong knowledge of mathematics, algorithms, and practical implementation separates beginners from professionals. Choose a course that balances math clarity, algorithm understanding, and hands-on projects. With consistent learning, machine learning can open doors to high-impact roles in the AI-driven world.

Want to Learn More After Machine Learning?

If you want to go deeper and expand your career, explore these related learning paths:

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