Top 10 Online Machine Learning Courses 2022 Guide
Find top courses on Machine Learning in 2022 is a difficult task. There are dozens of courses/certifications accessible to self-start your career in Machine Learning. These courses are given in either online or offline. The main difficulty students facing is choosing the best out of these courses. In this post, you will find your solution because I am giving you an in-depth review of the top 10 best online machine learning courses in 2022.
Top 10 Online Machine Learning Courses 2022
I have categorized all these courses based on the following essential criteria.
- Course Content/Description
- Career Opportunities
- Highest Reviewed
- Best Sellers
- Highest Rated
- Newest etc
- Course Quality
These courses are suitable for beginner level, intermediate as well as advanced level experts.
For most of the Udemy courses, you will get the following benefits.
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
- 30-Day Money-Back Guarantee
So below is the one in-depth review of the Top 10 Best Advanced Machine Learning Courses, Training program accessible online in 2022 to enable you to Learn Machine Learning.
In this course, 2,769,435 students had already enrolled!
This is, by far, one of the best machine learning courses on the internet.
Andrew Ng creates the course, which is Co-Founder of Coursera and Professor at Stanford University. The program has been attended by more than 2.7 million students & professionals globally, who have given the course an average rating of a whopping 4.9 out of 5.
Finding machine learning laptops or the top best monitors for your next AI / ML project is easy. My recommendation is that buy a Lambda laptop for your next machine learning project. They are the best in the business. If the quality of your computer, laptop or monitor is high, then you will love to craft the best ML apps.
This course gives a broad introduction to machine learning, data mining, and statistical pattern recognition.
The following topics are included in the course.
- Supervised learning, which includes non-parametric algorithms, support vector machines, kernels, neural networks.
- Unsupervised learning includes clustering, recommender systems, deep learning.
- Best practices in machine learning include variance theory, innovation process in machine learning, and Artificial Intelligence.
- The course will also cover some case studies and applications to study how to apply learning algorithms to building real-time applications like smart robots, text understanding (web search, anti-spam), computer vision, audio, medical informatics, database mining, and other different areas.
Skills you will gain.
- Logistic Regression.
- Artificial Neural Network.
- Machine Learning (ML) Algorithms.
Deep Dive Into The Modern Artificial Intelligence Techniques.
You will teach a computer to see, read, draw, talk, play games, and solve real-time industry problems.
The advanced ML specialization introduces deep learning, reinforcement learning, natural language understanding, computer vision, and Bayesian methods.
In this course, Top Kaggle machine learning practitioners and CERN scientists will give their experience of solving real-world problems and help you to bridge the gaps between theory and practical.
Upon completing the following 7 courses, you can apply modern machine learning methods in the enterprise and understand the caveats of real-world data and settings scenarios.
About this Course
In this course, 10,157 students had already enrolled!
Mikhail Hushchyn and his colleagues create the course.
There are 7 Courses in this Specialization
- Introduction to Deep Learning
- How to Win the Data Science Competition
- Bayesian Methods for Machine Learning
- Practical Reinforcement Learning
- Deep Learning in Computer Vision
- Natural Language Processing
- Addressing the large Hadron Collider Challenges by Machine Learning
All the courses 100% online courses.
Start instantly and study at your schedule. Flexible Schedule.
Set and maintain flexible deadlines.
The course is available in the English language, and You can find the Subtitles in English and Korean languages.
How the Specialization Works
The Coursera Specialization is a series of courses that help you master the skill.
First, enroll in the Specialization immediately or review its courses and choose the one you would like to start with.
When you subscribe to a particular course that is part of the Specialization, you’re automatically subscribed to the full Specialization.
Every Specialization covers a hands-on project.
You need to have successfully finished the project(s) to complete the specialization and earn your certificate.
If the specialization covers a separate course for the hands-on project, you’ll need to complete each of the other courses before you can start it.
Earn the Certificate
When you complete every course and complete the hands-on project, you will receive the Certificate you can share with prospective employers and your professional network.
The course is sharable on LinkedIn. You can add your certificates in the Certifications section of your LinkedIn profile on printed resumes, CVs, or other documents.
Learn how to create Machine Learning Algorithms in Python programming and R programming language from two Data Science experts.
The Author of the course is Jose Portilla.
The course has above 525,000+ students already enrolled.
The course has 4.5 (61,741 ratings) out of 5 stars.
- Data Preprocessing.
- Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.
- Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification.
- Clustering: K-Means, Hierarchical Clustering.
- Association Rule Learning: Apriori, Eclat.
- Reinforcement Learning: Upper Confidence Bound, Thompson Sampling.
- Natural Language Processing: Bag-of-words model and algorithms for NLP.
- Deep Learning: Artificial Neural Networks, Convolutional Neural Networks.
- Dimensionality Reduction: PCA, LDA, Kernel PCA.
- Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost.
What you will learn
- You will learn Machine Learning in Python & R programming languages.
- Have a great foreknowledge of many Machine Learning models.
- Make accurate predictions.
- Make a robust analysis.
- Make robust Machine Learning models.
- Create a secure added value for your business.
- Handle particular topics like Reinforcement Learning, NLP, and Deep Learning.
- Build an army of powerful Machine Learning models and know-how to combine them to solve any problem.
Learn how to use Python NumPy, Pandas, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more libraries and frameworks.
The author of the course is Jose Portilla.
The course had 290,000+ students enrolled.
The ratings for the course are 4.5 (61,741 ratings) out of 5, which is pretty impressive.
- Programming with Python programming language.
- NumPy with Python.
- Using pandas Data Frames to solve complex tasks.
- Use pandas to handle Excel Files.
- Web scraping with python.
- Connect Python to SQL.
- Use matplotlib and seaborn for data visualizations.
- Use plotly for interactive visualizations.
- Machine Learning with SciKit Learn, including.
- Linear Regression and much more
What you will learn
- How to use Python for Data Science and Machine Learning.
- How to use Spark for Big Data Analysis.
- How to implement Machine Learning Algorithms.
- Learn how to use NumPy for Numerical Data.
- Learn how to use Pandas for Data Analysis.
- Learn how to use Matplotlib for Python Plotting.
- Learn how to use Seaborn for statistical plots.
- How to use Plotly for interactive dynamic visualizations.
- How to use SciKit-Learn for Machine Learning Tasks.
- How to use K-Means Clustering.
- How to use Linear Regression.
- How to use Logistic Regression.
- How to use Natural Language Processing and Spam Filters.
- How to use Random Forest and Decision Trees.
- How to use Support Vector Machines.
- How to use Neural Networks.
Kickstart your Career in Data Science & ML. Master data science, learn Python & SQL, analyze & visualize data, build machine learning models.
SAEED AGHABOZORGI instructs the course.
The course has over 100,000+ students already enrolled!
This course deep-dives into machine learning basics using an approachable and popular programming language, which is Python.
In this course, we will be going through two main components:
- You will learn about the purpose of Machine Learning and where it applies to real-world apps.
- You will get a primary overview of Machine Learning topics such as supervised vs. unsupervised learning, model evaluation, and Machine Learning algorithms.
Learn all about Mathematics for Machine Learning, which the necessity of data science and machine learning.
For a lot of higher-level courses in Machine Learning and Data Science, you will find that you need to freshen up on the basics in the mathematics stuff you may have studied before in school or the university, but which was taught in a completely different context, or not very seriously, such that you struggle to relate it to how it’s practiced in real-world Computer Science.
This Mathematics course is especially for machine learning. It aims to bridge that gap, getting you up to speed in the underlying mathematics, building the natural understanding, and comparing it to Machine Learning and Data Science.
There are 3 Courses in this Specialization
- Mathematics for Machine Learning: Linear Algebra
- Mathematics for Machine Learning: Multivariate Calculus
- Mathematics for Machine Learning: PCA
In the first course on Linear Algebra, we deep dive into what linear algebra is and its relation to data. Then we look through what vectors and matrices are and how to work with them.
In the second course, there is Multivariate Calculus, in which we look at how to optimize the fitting functions to get good fits to data.
It starts with basic calculus and then uses the matrices and vectors from the first course to determine data fitting.
The third course, Dimensionality Reducing with a Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data.
This course is for intermediate-level devs and will require Python and numpy knowledge.
Skills you will gain
- Eigenvalues And Eigenvectors.
- Principal Component Analysis (PCA).
- Multivariable Calculus.
- Linear Algebra.
It is a complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks.
Sundog Education creates the course by Frank Kane.
The course has over 115,000+ students enrolled.
The ratings for the course are excellent. It has 4.5(18,428 ratings) out of 5.
It was last updated in December 2019, so all the latest updates are included in the course.
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 a 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.
- Design and evaluate A/B tests using T-Tests and P-Values.
Learn how to use the R language for data science and machine learning, and data visualization!
Jose Portilla creates the course.
The course has over 50,000 students enrolled.
The ratings for the course are excellent. It has 4.6 (9,261 ratings) out of 5.
What you will learn
- Program in R.
- Use R for Data Analysis.
- Create Data Visualizations.
- Use R to handle csv, excel, SQL files, or web scraping.
- Use R to manipulate data easily.
- Use R for Machine Learning Algorithms.
- Use R for Data Science.
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts.
The course is Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team.
The course has over 200,000+ students enrolled.
The ratings for the course are excellent. It has 4.5 (26,941 ratings) out of 5.
- Understand the presentiment behind Artificial Neural Networks.
- Apply Artificial Neural Networks in the application.
- Understand the intuition behind Convolutional Neural Networks.
- Apply Convolutional Neural Networks in practice.
- Understand the foreknowledge behind Recurrent Neural Networks.
- Apply Recurrent Neural Networks in practice.
- Understand the foreknowledge behind Self-Organizing Maps.
- Apply Self-Organizing Maps in an application.
- Understand the foreknowledge behind Boltzmann Machines.
- Apply Boltzmann Machines in an application.
- Understand the foreknowledge behind AutoEncoders.
- Apply AutoEncoders in an application.
Learn how to use Google’s Deep Learning Framework – TensorFlow with Python! Solve problems with cutting-edge techniques.
Jose Portilla creates the course.
The course has over 70,000+ students enrolled.
The ratings for the course are excellent. It has 4.4 (13,352 ratings) out of 5.
- Understand how Neural Networks work.
- Build your own Neural Network from Scratch using Python.
- Use TensorFlow for Classification and Regression Tasks.
- Use TensorFlow for Image Classification with Convolutional Neural Networks.
- Use TensorFlow for Time Series Analysis with Recurrent Neural Networks.
- Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders.
- Learn how to conduct Reinforcement Learning with OpenAI Gym.
- Create Generative Adversarial Networks with TensorFlow.
Machine Learning is currently one of the most prominent careers in the IT industry.
If you want to stay ahead in the competition, you had to take online machine learning courses.
In this article, we have listed the best machine learning courses from Udemy and Coursera.
If you want to work in big tech companies like Amazon, Facebook, Google, Microsoft, IBM, you need to have deep knowledge of Artificial Intelligence, Machine Learning, and Deep Learning.