15 Best Machine Learning Courses, Tutorials, Training 202413 min read

Are you looking for the Best Machine Learning Courses to master yourself?

Grab this list of Best Machine Learning Tutorials, Training, classes, and Certification.

Now, this is the most trending and demanding subject today both for career and business intelligence.

Let’s get started, how much money could you make if you could predict the price of a stock or if you could predict which color will be in fashion six months later? Here, You can predict almost anything that you wish. The future will be in your own palms.

Apart from this, Machine Learning algorithms are the technology that is enabling you to bring the future into your hand.  Besides, this is the technology that predicts the future using the immense amount of data that has been recorded in the past.

Moreover, artificial intelligence has been used by the human race for a long time. Along with this, predicting spam in your email inbox has been used for a long time. However, they were not as much as reliable to have high bets on. But machine learning algorithms have changed the game completely.

At the same time, now if you want the most premium jobs with the highest salaries all around the world then you need to aim for the job of a machine learning algorithm expert.

However, your small investment to learn machine learning will pay you back with an infinite percentage of interest. And that is something which you can bet on.

Particularly, you did a lot of research and then came up with the Best Machine Learning Courses offered by the university of Washington and Stanford university!

Best Artificial Intelligence (AI) Courses for you, which will enhance your skills in advanced programming languages for instance Python, R, Data Science, Neural Networks, Cluster Analysis, Scala, Spark 2.0 etc.; so that you are guaranteed to get the best and the highest paid salary of all times by being a machine learning expert.

Now, Imagine yourself getting a job at Space X, Facebook or Microsoft, and Google, making self-driving cars or making the best of the best apps for smartphones and other smart devices. Sounds Awesome?

Yes, the applications on your smartphone use machine learning such as Twitter, Gmail, Snap Chat, and many others.

15 Best Machine Learning Courses, Tutorials, Training 2024

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

BEST SELLER

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks.

What you will learn:

• Building an artificial neural network.
• Classify images, data, and sentiments using deep learning.
• Making predictions using linear regression, polynomial regression, and multivariate regression.
• Data Visualization.
• Implementing machine learning.
• Understanding reinforcement learning.
• Classify data using K-Means clustering
• Using train/test and K-Fold cross-validation to choose and tune your models.
• Building a movie recommender system.
• Cleaning your input data to remove outliers.
• Design and evaluate A/B tests using T-Tests and P-Values

Requirements:

• Desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer.
• Some prior coding or scripting experience is required.
• At least high school-level math skills will be required.

Who this course is for:

• Software developers
• Programmers

Instructors: Sundog Education by Frank Kane, Frank Kane, Sundog Education Team

Students Enrolled:  190.6K +

Ratings: 4.6 out of 5.0

ENROLL NOW

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

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

What you will learn:

• Master Machine Learning on Python & R.
• Having a great intuition of many Machine Learning models.
• Making accurate predictions.
• Making a powerful analysis.
• Making robust Machine Learning models.
• Creating strong added value to your business.
• Using Machine Learning for personal purposes.
• Handling specific topics like Reinforcement Learning, NLP, and Deep Learning.
• Handling advanced techniques like Dimensionality Reduction.
• Knowing which Machine Learning model to choose for each type of problem.
• Building an army of powerful Machine Learning models.

Requirements:

High school mathematics level.

Eligibility:

Interested in Machine Learning.

Instructors:
Kirill Eremenko, Hadelin de Ponteves, Ligency Team, Ligency Team

Students Enrolled: 1025K +

Ratings: 4.5  out of 5.0

Enroll Now

Read More: Review of Machine Learning Course A-Z™: Hands-On Python & R In Data Science

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3. Data Science and Machine Learning Bootcamp with R (Udemy)

Initially, from the course, you’ll learn how to use the R programming language for data science and machine learning and data visualization!

What you will learn:

• Program in R.
• Using R for Data Analysis.
• Creating Data Visualizations.
• Using R to handle CSV, excel, SQL files, or web scraping.
• Using R to manipulate data easily.
• Using R for Machine Learning Algorithms.
• Using R for Data Science.

Requirements:

• Computer Access with download privileges.
• Basic Math Skills!!

Eligibility:

• Interested in becoming a Data Scientist!!

Instructors: Jose Portilla

Students Enrolled:  93.2K +

Ratings: 4.7 out of 5.0

Enroll Now

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4. Artificial Intelligence: Reinforcement Learning in Python (Udemy)

BEST SELLER

Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications.

What you will learn:

• Applying gradient-based supervised machine learning methods to reinforcement learning
• Understanding reinforcement learning on a technical level.
• Understanding the relationship between reinforcement learning and psychology.
• Implementing 17 different reinforcement learning algorithms.

Requirements:

• Calculus (derivatives).
• Probability / Markov Models.
• Numpy, Matplotlib.
• Beneficial have experience with at least a few supervised machine learning methods.
• Gradient descent.
• Good object-oriented programming skills.

Eligibility: Interested in artificial intelligence, data science, machine learning, and deep learning.

Instructors:  lazy Programmer Team, Lazy Programmer Inc.

Students Enrolled:  45.4K +

Ratings: 4.7 out of 5.0

Enroll Now

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5. Best Machine Learning Course: Python for Data Science and Machine Learning Bootcamp (Udemy)

BEST SELLER

you’ll learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Machine Learning Engineering, Tensorflow, and more!

From the boot camp, you may also get to solve a case study and which can give you a real working experience with machine learning applications.

What you will learn:

• Using Python for Data Science and Machine Learning
• Using Spark for Big Data Analysis
• Implementing Machine Learning Algorithms
• Using NumPy for Numerical Data
• Using Pandas for Data Analysis
• Using Matplotlib for Python Plotting
• Using Seaborn for statistical plots
• Using Plotly for interactive dynamic visualizations
• Using SciKit-Learn for Machine Learning Tasks
• K-Means Clustering.
• Logistic Regression.
• Linear Regression.
• Random Forest and Decision Trees.
• Natural Language Processing and Spam Filters.
• Neural Networks.
• Supporting Vector Machines.

Requirements:

• Some programming experience.
• Admin permissions to download files.

Eligibility: Basic Programming experience

Instructor: Jose Portilla

Students Enrolled:  691.6K +

Ratings: 4.6 out of 5.0

Enroll Now

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6. Scala and Spark for Big Data and Machine Learning (Udemy)

Firstly, learn the latest Big Data technology – Spark and Scala, including Spark 2.0 DataFrames!

Then, you will also learn machine learning applications and applying machine learning!

What you will learn:

• Using Scala for Programming.
• Using Spark 2.0 DataFrames to read and manipulate data.
• Using Spark to Process Large Datasets.
• Understanding how to use Spark on AWS and DataBricks.

Requirements:

• Basic Programming Knowledge in some languages.
• Basic Math Skills.
• English Language.

Who this course is for:

• Interested in learning Big Data Technologies
• Interested to learn the course.

Instructor: Jose Portilla

Students Enrolled:  31.4K +

Ratings: 4.6/5

Enroll Now

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7. Bayesian Machine Learning in Python: A/B Testing (Udemy)

BEST SELLER

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More.

What you will learn:

• Using adaptive algorithms to improve A/B testing performance.
• Understanding the difference between Bayesian and frequentist statistics.
• Applying Bayesian methods to A/B testing.

Requirements:

• Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF).
• Python coding with the Numpy stack.

Eligibility:

• Students
• Professionals

Instructor: Lazy Programmer Inc.

Students Enrolled: 38K +

Ratings: 4.6 out of 5.0

Enroll Now

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8. Cluster Analysis and Unsupervised Machine Learning in Python

(Udemy)

BEST SELLER

Initially, you will learn the data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.

What you will learn:

• Understanding the regular K-Means algorithm.
• Understanding and enumerating the disadvantages of K-Means Clustering.
• Understand the soft or fuzzy K-Means Clustering algorithm.
• Implementing Soft K-Means Clustering in Code.
• Understanding Hierarchical Clustering.
• Explaining algorithmically how Hierarchical Agglomerative Clustering works.
• Applying Scipy’s Hierarchical Clustering library to data.
• Understanding how to read a dendrogram.
• Understanding the different distance metrics used in clustering.
• Understanding the difference between single linkage, complete linkage, Ward linkage, and UPGMA.
• Understanding the Gaussian mixture model and how to use it for density estimation.
• Writing a GMM in Python code.
• Explaining when GMM is equivalent to K-Means Clustering..
• Explain the expectation-maximization algorithm.
• Understanding how GMM overcomes some disadvantages of K-Means.
• Understanding the Singular Covariance problem and how to fix it.

Requirements:

• Knowing code in Python and Numpy.
• Installed Numpy and Scipy.
• Matrix arithmetic, probability.

Eligibility: Students and professionals are interested in machine learning and data science!!!

Instructor: Lazy Programmer Team, Lazy Programmer Inc.

Students Enrolled: 27.1K+

Ratings: 4.8 out of 5.05

Enroll Now

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9. DP-100: A-Z Machine Learning using Azure Machine Learning (AzureML) (Udemy)

BEST SELLER

Firstly, Microsoft Azure DP-100: Designing and Implementing a Data Science Solution Exam Covered. Learn Azure Machine Learning

What you will learn:

• Master Data Science and Machine Learning Models using Azure ML.
• Understanding the concepts and intuition of Machine Learning algorithms.
• Building Machine Learning models within minutes.
• Choosing the correct Machine Learning Algorithm using the cheat sheet.
• Deploying production-grade Machine Learning algorithms.
• Deploying Machine Learning web services in the simplest form possible including excel.
• Bringing in great value to the business you manage.

Requirements:

• Basic Math is good enough
• A free or paid subscription to Microsoft Azure is required.

Eligibility:

• Developers
• Business
• Analysts
• Functional Experts
• Interested in Machine Learning
• Non-technical professionals
• Business Process Managers
• Marketing professionals.

Instructor: Jitesh Khurkhuriya, Python, Data Science & Machine Learning A-Z Team

Students Enrolled: 44.2K +

Ratings: 4.5 out of 5.0

Enroll Now

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10. Unsupervised Machine Learning Hidden Markov Models in Python (Udemy)

BEST SELLER

Basically, HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.

What you will learn:

• Understanding and enumerating the various applications of Markov Models and Hidden Markov Models.
• Understanding how Markov Models work.
• Writing a Markov Model in code.
• Applying Markov Models to any sequence of data.
• Understanding the mathematics behind Markov chains.
• Applying Markov models to language.
• Applying Markov models to website analytics.
• Understanding how Google’s PageRank works.
• Understanding Hidden Markov Models.
• Writing a Hidden Markov Model in Code.
• Writing a Hidden Markov Model using Theano.
• Understanding how gradient descent, which is normally used in deep learning, can be used for HMMs.

Requirements:

• Familiarity with probability and statistics.
• Understand Gaussian mixture models.
• Being comfortable with Python and Numpy.

Who this course is for:

• Students
• Professionals
• Interested in data analysis such as data, optimize their website experience, strengthening their machine learning knowledge
• Interested in DNA analysis and gene expression
• Interested in a modeling language and generating text from a model

Instructor: Lazy Programmer Team, Lazy Programmer Inc.

Students Enrolled: 27.9K +

Ratings: 4.7 out of 5.0

Enroll Now

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11. Machine Learning Course: Introduction to Machine Learning for Data Science (Udemy)

Primarily, a primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.

What you will learn:

• Understanding what Computer Science, Algorithms, Programming, Data, Big Data, Artificial Intelligence, Machine Learning, and Data Science is.
• How these different domains fit together, how they are different, and how to avoid the marketing fluff!
• The Impacts of Machine Learning and Data Science is having on society.
• Understanding computer technology has changed the world, with an appreciation of scale.
• Problems Machine Learning can solve, and how the Machine Learning Process works.
• Problems with Machine Learning, to successfully implement it without losing your mind!

Requirements:

• A passion to learn, and basic computer skills!
• Basic high-school-level mathematics.

Who this course is for:

• Interested in Machine Learning is used for Data Science.
• Business leaders, managers, app developers, and consumers!

Instructor: David Valentine

Students Enrolled: 63.7K +

Ratings: 4.4 out of 5.0

Enroll Now

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12. From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase

(Udemy)

A down-to-earth, shy but confident take on machine learning techniques that you can put to work today.

What you will learn:

• Identifying situations that call for the use of Machine Learning.
• Understanding which type of Machine learning problem you are solving and choose the appropriate solution.
• Using Machine Learning and Natural Language processing to solve problems like text classification, text summarization in Python.

Requirements:

• No prerequisites.
• knowledge of some undergraduate-level mathematics.
• Working knowledge of Python.

Who this course is for:

• Analytics Professionals
• Modelers
• Big Data Professionals
• Engineers
• Product managers
• Tech executives Investors
• MBA graduates
• Business Professionals!!!

Instructor: Loony Corn

Students Enrolled: 8.7K+

Ratings: 4.1 out of 5.0

Enroll Now

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13. Ensemble Machine Learning in Python: Random Forest, AdaBoost

(Udemy)

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python!

What you will learn:

• Understanding and deriving the bias-variance decomposition.
• Understand the bootstrap method and its application to bagging.
• Understanding why bagging improves classification and regression performance.
• Understanding and implementing the Random Forest.
• Understanding and implementing AdaBoost.

Requirements:

• Calculus (derivatives).
• Numpy, Matplotlib, Sci-Kit Learn.
• K-Nearest Neighbors, Decision Trees.
• Probability and Statistics (undergraduate level).
• Linear Regression, Logistic Regression.

Who this course is for:

• Students studying machine learning.
• Professionals.
• Entrepreneurs.
• Students in computer science.
• Wants to know some basic machine learning models

Instructor: Lazy Programmer Team, Lazy Programmer Inc.

Students Enrolled: 17.1K+

Ratings: 4.7 out of 5.0

Enroll Now

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14. The Complete Machine Learning Course with Python (Udemy)

To begin with, build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

What you will learn:

• Becoming an ideal candidate for this course!
• Solving any problem in your business, job, or personal life.
• Train machine learning
• Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning, etc.

Requirements:

• Basic Python programming knowledge is necessary.
• Good understanding of linear algebra.

Who this course is for:

• Willing and interested to learn machine learning algorithms with Python.
• Who has a deep interest in the practical application!
• Wishes to move beyond the basics
• Advanced EXCEL users who work with large datasets
• Interested to present their findings in a professional and convincing manner.

Instructor: Codestars by Rob Percival, Anthony NG, Rob Percival

Students Enrolled: 39K +

Ratings: 4.5 out of 5.0

Enroll Now

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15. AWS SageMaker – Certified Machine Learning Specialty Exam (Udemy)

BEST SELLER

Complete Guide to AWS Certified Machine Learning (MLS-C01) – Specialty and Practice Test!!!

What you will learn:

• Gain first-hand experience on how to train, optimize, deploy, and integrate ML in the AWS cloud
• AWS Built-in algorithms, Bring Your Own, Ready-to-use AI capabilities.
• Complete Guide to AWS Certified Machine Learning – Specialty (MLS-C01).
• Includes a high-quality Timed practice test (a lot of courses charge a separate fee for a practice test)

Requirements:

• Familiarity with Python.
• AWS Account.
• Basic knowledge of Pandas, Numpy, and Matplotlib.
• Be an active learner.

Who this course is for:

• Interested in AWS cloud-based machine learning and data science.
• AWS Certified Machine Learning.

Instructor: Chandra Lingam

Students Enrolled: 32.4K +

Ratings: 4.5 out of 5.0

Enroll Now

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So these are the Best Machine Learning Courses that will make you stand out from others and help you earn those extra hundred thousand of dollars. In this era, everybody needs to scale up their skills. And I wish you the best of luck on your journey to upgrade your skill set.

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