machine learning course

10 Best + Free Online Machine Learning Courses

If buzzwords like deep learning, neural learning, and artificial intelligence pique your interest, we’ve compiled a comprehensive list of the finest free machine learning courses available right now. While the notion of machine learning may be unfamiliar to the general public, individuals in the know understand its significance. The ability to educate oneself on all elements of this area might be the difference between a mundane IT job and one that is incredibly exciting!
Several services that you undoubtedly use on a daily basis are powered by machine learning. For example, Netflix movie suggestions and Amazon suggested products are the result of sophisticated algorithms that generate statistics from enormous volumes of data. You’ll get a list of “items you might like” as a consequence of the data.
It’s a little creepy for a lot of individuals. For others in the sector, though, it represents a revolutionary approach to profile customers and boosts e-commerce by personalizing the buying experience. So, whether you like it or not, it’s a trend that’s here to stay and is only getting bigger, making it an excellent career choice for techies.

List of Best + Free Online Machine Learning Courses

1. Machine Learning A-Z™: Hands-On Python & R In Data Science(Udemy)

Do you want to learn more about Machine Learning? Then this is the course for you!
This course was created by two expert Data Scientists to share their knowledge and to assist you in learning difficult theories, algorithms, and coding libraries in a straightforward manner.
We’ll take you through the world of machine learning one step at a time. You’ll learn new skills and gain a better grasp of this tough yet lucrative sub-field of Data Science with each session.

What you will learn –

  • Master Machine Learning on Python & R.
  • Have a great intuition of many Machine Learning models.
  • Make accurate predictions.
  • Make powerful analysis.
  • Make robust Machine Learning models.
  • Create strong added value to your business.
  • Use Machine Learning for personal purpose.
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning.
  • Handle advanced techniques like Dimensionality Reduction.
  • Know which Machine Learning model to choose for each type of problem.
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 44 hours approx

Instructor – Kirill Eremenko, Hadelin de Ponteves & SuperDataScience Support.

2. Machine Learning Course by Stanford University (Coursera)

The science of getting computers to act without being explicitly programmed is known as machine learning. Self-driving cars, realistic speech recognition, successful web search, and a much-enhanced understanding of the human genome have all been made possible by machine learning in the last decade. Machine learning is now so common that you probably use it thousands of times a day without even realizing it. Many academics believe it is the most effective technique to get closer to human-level AI. This program will teach you about the most effective machine learning techniques and give you practice implementing and using them on your own. MoSign Up for this Course significantly, you’ll master not only the theoretical foundations of learning but also the practical know-how required to apply these strategies to new challenges quickly and effectively. Finally, you’ll learn about some of Silicon Valley’s best practices in machine learning and AI innovation.

What you will learn –

  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). 
  • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). 
  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). 
  • The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Pre-requirements-

No prerequisites are needed.

Who can take this course-

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 60  hours approx

Instructor – Andrew Ng.

3. Learning from Data (Introductory Machine Learning Course)(California Institute of Technology)

This course covers the fundamentals of machine learning (ML), including theory, techniques, and applications. Big Data, as well as many financial, medical, commercial, and scientific applications, rely heavily on machine learning. It allows computational systems to enhance their performance over time as they gain experience from observable data. ML has become one of the most popular disciplines of study today, with undergraduate and graduate students at Caltech studying it in 15 distinct majors. This course strikes a balance between theory and practice, covering both mathematical and heuristic aspects.

What you will learn –

  • Tensorflow and Keras can be used to create artificial neural networks.
  • Deep learning can be used to classify images, data, and sentiments.
  • Use linear regression, polynomial regression, and multivariate regression to make predictions.
  • MatPlotLib and Seaborn for Data Visualization.
  • Apache Spark’s MLLib allows you to implement machine learning at a large scale.
  • K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA can all be used to classify data.
  • To choose and optimise your models, use train/test and K-Fold cross validation.
  • Using item-based and user-based collaborative filtering, create a movie recommendation system.
  • Remove outliers from your input data.
  • T-Testing and P-Values are used to design and analyse A/B tests.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 10  weeks approx.

4. Best Deep Learning Course (Coursera)

With Andrew Ng himself teaching the course, this is clearly one of the most sought-after deep learning certificates. Andrew is the Co-Founder of Coursera, a global learning platform, and has previously led the Google Brain and Baidu AI, groups. Teaching Assistants Younes Bensouda Mourri from Stanford University’s Mathematical & Computational Sciences and Kian Katanforoosh, an Adjunct Lecturer at Stanford University, will join him. Overall, we have no reservations about naming this the Best Deep Learning Certification available. This certification course will teach you the fundamentals of deep learning, how to design neural networks, and how to manage machine learning projects. Real-time case studies will include, among other things, sign language reading, music production, and natural language processing. Along with the theory, you’ll learn how to put it all together with Python and TensorFlow.

What you will learn –

  • Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications.
  • Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow.
  • Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning.
  • Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data.
  •  Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 20 weeks approx.

Instructor – Andrew Ng.

5. Machine Learning for Data Science and Analytics(Edx)

Machine learning is a rapidly growing discipline that is employed in a variety of applications, including web searches, ad placement, credit scoring, stock trading, and many others.
Machine learning and algorithms are covered in this data science course. You’ll gain a basic understanding of machine learning principles and use predictive analytics to come up with real solutions. We’ll also look at why algorithms are so important in Big Data analysis.

What you will learn –

  • What machine learning is and how it is related to statistics and data analysis.
  • How machine learning uses computer algorithms to search for patterns in data.
  • How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth.
  • How to uncover hidden themes in large collections of documents using topic modeling.
  • How to prepare data, deal with missing data and create custom data analysis solutions for different industries.
  • Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 5 weeks approx

Instructor – David Blei, Itsik Peer, Ansaf Salleb-Aouissi, Cliff Stein

6. Deep Learning Course (deeplearning.ai)

The Deep Learning Specialization is a fundamental program that will enable you to participate in the creation of cutting-edge AI technology by helping you grasp the capabilities, problems, and repercussions of deep learning.
You’ll learn how to create and train neural network designs like Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers, as well as how to improve them with tactics like Dropout, BatchNorm, Xavier/He initialization, and more in this Specialization. Prepare to use Python and TensorFlow to learn theoretical topics and their industry applications, and to handle real-world problems like speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
Many sectors are being transformed by artificial intelligence. The Deep Learning Specialization paves the road for you to take the next step in your AI career by assisting you in gaining the necessary knowledge and abilities. You’ll also get career advice from deep learning specialists from industry and academia along the way.

What you will learn –

  • Create and train deep neural networks, vectorize neural networks, determine architecture parameters, and use deep learning in your applications.
  • To construct DL applications, follow best methods for training and developing test sets, as well as analysing bias and variance. Use conventional NN approaches, apply optimization methods, and design a neural network in TensorFlow.
  • Apply end-to-end, transfer, and multi-task learning strategies to reduce errors in machine learning systems, grasp complex ML settings, and apply end-to-end, transfer, and multi-task learning.
  • Construct a Convolutional Neural Network, utilise it to do visual detection and identification tasks, make art using neural style transfer, and apply these methods to image, video, and other 2D/3D data.
  • Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), use RNNs for character-level language modelling, work with NLP and Word Embeddings, and conduct Named Entity Recognition and Question Answering with HuggingFace tokenizers and transformers.
  • Many sectors are being transformed by artificial intelligence. The Deep Learning Specialization paves the road for you to take the next step in your AI career by assisting you in gaining the necessary knowledge and abilities. You’ll also get career advice from deep learning specialists from industry and academia along the way.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration –  24 weeks approx

Instructor – Jan Zawadzki, Kritika Jalan & Chris Morrow

7. Machine Learning for All (Coursera)

Machine Learning, also known as Artificial Intelligence or AI, is currently one of the most fascinating areas in technology. We hear about new developments in facial recognition technology, self-driving cars, and computers that can converse like human people on a daily basis. Machine Learning technology is set to revolutionize practically every aspect of human life and work, and as a result, it will have a significant impact on all of our lives, therefore you’ll want to learn more about it.
Machine Learning has a reputation for being one of the most difficult fields in computer science to grasp, requiring sophisticated math and engineering skills. While it’s true that working as a Machine Learning engineer necessitates a lot of math and programming, we believe that anybody can grasp the fundamental concepts of Machine Learning and that everyone should, given the technology’s importance. The most significant AI breakthroughs may sound like science fiction, yet they all boil down to one fundamental concept: using data to train statistical algorithms. Even if you have no prior knowledge of arithmetic or programming, you will learn the fundamentals of machine learning in this course.

What you will learn –

  • Y​ou will understand the basic of how modern machine learning technologies work.
  • Y​ou will be able to explain and predict how data affects the results of machine learning.
  • Y​ou will be able to use a non-programming based platform train a machine learning module using a dataset.
  • Y​ou will be able to form an informed opinion on the benefits and dangers of machine learning to society.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 22 hours approx

Instructor – Dr. Marco Gillies

8. Machine Learning, Data Science and Deep Learning with Python (Udemy)

This data science course will teach you the principles of machine learning and artificial intelligence (AI), which are used by organizations like Google, Amazon, and even Udemy to extract meaning and insights from large data sets.
This course will teach you the techniques used by professional data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career field if you have any programming or scripting experience. This thorough machine learning curriculum comprises over 100 lectures over 14 hours of video, with most topics including hands-on Python code examples for reference and practice.
Each idea is explained in plain English, avoiding the use of jargon and complicated mathematical symbols. It’s then illustrated with Python code that you can play around with and develop on, as well as notes for future reference. This course does not focus on academic, thoroughly mathematical discussion of these algorithms; rather, it focuses on practical comprehension and application. You’ll be given a final project to apply everything you’ve learned in the end!

What you will learn –

  • Build artificial neural networks with Tensorflow and Keras.
  • Classify images, data, and sentiments using deep learning.
  • Make predictions using linear regression, polynomial regression, and multivariate regression.
  • Data Visualization with MatPlotLib and Seaborn.
  • Implement machine learning at massive scale with Apache Spark’s MLLib.
  • Understand reinforcement learning – and how to build a Pac-Man bot.
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA.
  • Use train/test and K-Fold cross validation to choose and tune your models.
  • Build a movie recommender system using item-based and user-based collaborative filtering.
  • Clean your input data to remove outliers.
  • Design and evaluate A/B tests using T-Tests and P-Values.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 14.5 hours approx

Instructor – Sundog Education team & Frank Kane

9. Python for Everybody Specialization(Coursera)

This Specialization builds on the success of the Python for Everyone course and uses the Python programming language to introduce fundamental programming concepts such as data structures, networked application program interfaces, and databases. In the Capstone Project, you’ll design and build your own data retrieval, processing, and visualization applications using the technologies you’ve learned throughout the Specialization.

What you will learn –

  • Install Python and write your first program.
  • Describe the basics of the Python programming language.
  • Use variables to store, retrieve and calculate information.
  • Utilize core programming tools such as functions and loops.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 32 weeks approx

Instructor – Charles Russell Severance

10. Advanced Machine Learning Course by HSE (Coursera)

Deep learning, reinforcement learning, natural language comprehension, computer vision, and Bayesian approaches are all included in this specialization. Top Kaggle machine learning practitioners and CERN scientists will discuss their real-world problem-solving experience and assist you in closing the gap between theory and practice. You will be able to apply modern machine learning methods in businesses and comprehend the caveats of real-world data and circumstances after completing seven courses.

What you will learn –

  • Understand how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks.
  •  Learn how to preprocess the data and generate new features from various sources such as text and images.
  •  Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions.
  • Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data. 
  • Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them. 
  •  Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance. 
  •  Master the art of combining different machine learning models and learn how to ensemble. 
  •  Get exposed to past (winning) solutions and codes and learn how to read them.

Pre-requirements –

No prerequisites are needed.

Who can take this course –

This course is open to anyone who wants to learn more and make better use of their time.

Duration – 40 weeks approx

Instructor – Evgeny Sokolov

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