https://ift.tt/JbYXile When approaching machine learning, everyone has a different level of knowledge and skill. There are dozens, if not h...
When approaching machine learning, everyone has a different level of knowledge and skill. There are dozens, if not hundreds, of machine learning courses, and finding the right one can be difficult. The ideal course should do the following:
- Minimize the amount of redundant knowledge
- Maximize your enjoyment of new topics
- Provide real-world programming examples
- Boost your resumé through portfolio projects
This guide will help you find the best machine learning course based on your existing knowledge and comfort with programming. We analyzed the top courses available based on 14 unique variables and came up with the following list.
Here’s a summary of the best machine learning courses in 2022:
- Best for Beginner Learners: Dataquest’s Machine Learning in Python
- Best for Intermediate Learners: Machine Learning Specialization
- Best for Advanced Learners: Machine Learning with Python: from Linear Models to Deep Learning
- Best Free Course: Google’s Machine Learning Crash Course
- Best Online Machine Learning Bootcamp: Coding Dojo’s Machine Learning Bootcamp
- Best In-person Machine Learning Bootcamp: Columbia Engineering Data Analytics Boot Camp
- Best for Data Scientists: Dataquest’s Data Scientist in Python Pathway
- Best for Musicians and Artists: Machine Learning for Musicians and Artists
Best for Beginner Learners
Dataquest’s Machine Learning in Python
Dataquest offers the best course for beginners looking to grow their careers in data science. The course provides a foundation for learning supervised and unsupervised machine learning algorithms.
Topics covered include linear regression, logistic regression, k-means clustering, decision trees, and so much more. Each topic is taught with the necessary amount of detail to prepare you for solving real-world problems.
Best of all, the course allows you to jump right into writing code. Each lesson is coupled with a real-world project that allows you to build your portfolio to showcase to future employers and reinforce your knowledge base.
Projects you will work on include:
- Predicting insurance costs using linear regression modeling
- Classifying heart disease using logistic regression
- Working with employment data to determine productivity thresholds
The course includes a final capstone project that is reviewed by an expert to fully round out your portfolio.
Here’s our detailed breakdown of the course:
| Price: $399/yr, $49/mo (discounts offered) | Thoroughness: Complete introduction to machine learning with portfolio projects for resumé building |
| Upfront fees: Free to start | Speciality: Supervised and unsupervised algorithms Python programming |
| Ratings: 4.8 (359 reviews) | Required prerequisites: Python fundamentals Basic math concepts |
| Estimated duration: 2 months (10 hours per week) | Pacing: Self-paced |
| Certificate Program: Yes | Number of projects: 7 |
| Students enrolled: 400 | Refund option: Refund available if you’re unsatisfied after completing a career path |
| Learning style: Coding modules and guided projects | Career services: Certification upon completion and strong community support |
Student Testimonials
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“Dataquest actually makes you think and apply your skills. You don’t really need to spend $10,000 dollars on a bootcamp.” —Miguel Couto |
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“The learning paths on Dataquest are incredible. You don’t have to guess what you should learn next.” —Otávio Silveira |
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“I can’t believe how easily and clearly complex material is presented on Dataquest.” —Viktoria Jorayeva |
Machine Learning A-Z: Python & R in Data Science
This is a great course for beginners who are looking to learn how to use machine learning in either Python or R. The course jumps right into teaching different machine learning algorithms and concepts, including support vector regression, natural language processing, and deep learning.
The course offers a combined format of videos and quizzes that lets you check your understanding along the way. You’ll complete several practical exercises that are highly complementary to the material you’ll learn.
Here’s our detailed breakdown of the course:
| Price: $84.99 | Thoroughness: Thorough introduction to machine learning algorithms |
| Upfront fees: Paid in-full | Speciality: Supervised and unsupervised algorithms Natural language processing Python and R programming |
| Ratings: 4.5 (163,675 Reviews) | Required prerequisites: Python and R fundamentalsBasic math concepts |
| Estimated duration: 42.5 Hours | Pacing: Self-paced |
| Certificate Program: Yes | Number of projects: 0 |
| Students enrolled: 906,661 | Refund option: Refund within 30 days |
| Learning style: Video lectures, articles, and quizzes | Career services: Certificate upon completion |
This is an amazing course for the beginners who want to understand about everything in machine learning. Thank you to the instructors (Hadelin de Ponteves and Kirill Eremenko) for explaining it clearly and making it easy to understand. I hope this knowledge can help me for developing my start-up, advancing technology, and giving benefits to others.
—Nu’man Amri M.
Best Free Courses
Google’s Machine Learning Crash Course with TensorFlow APIs
This Google course is great for both beginners looking to learn machine learning and experienced data scientists looking for a refresher. The course starts off by asking for your experience level before recommending specific resources for maximum efficiency (a smart machine learning course!).
The course consists of 25 lessons, takes 15 hours to complete, and includes real-world case studies with interactive visualizations. You’ll additionally get hands-on experience with Tensorflow, Google’s in-house machine learning framework.
The course is highly condensed and eliminates core concepts, so further research is required for a deeper understanding of the covered topics.
Highlights of the course:
- Lectures from Google researchers
- Access to companion Kaggle datasets for real-world applications
- Insights into key machine learning concepts, including loss, gradient descent, and neural networks
Here’s our detailed breakdown of the course:
| Price: Free | Thoroughness: High-level overview of core machine learning concepts |
| Upfront fees: None | Speciality: Python programming with Tensorflow neural networks |
| Ratings: Not Public | Required prerequisites: Python fundamentals Basic math Calculus (for advanced topics) |
| Estimated duration: 15 hours | Pacing: Self-paced |
| Certificate Program: No | Number of projects: None |
| Students enrolled: Not public | Refund option: Not available (free course) |
| Learning style: 25 Lessons, 30+ exercises, lectures from Google researchers | Career services: None |
Kaggle’s Intro to Machine Learning
Kaggle is the best platform on the internet to access raw data and put your data science skills to the test through sponsored competitions.
This course is Kaggle’s introduction to everything data science and is completely free. The material is highly succinct, can be completed in 3 hours, and will give you an introduction to using Kaggle’s platform.
The course walks through basic data exploration, underfitting and overfitting models, random forests, and, finally, prepares you to compete in competitions. While not very long, the course is a great introduction to the Kaggle platform, which is a great resource for any aspiring machine learning expert or data scientist.
Here’s our detailed breakdown of the course:
| Price: Free | Thoroughness: High-level overview of machine learning concepts |
| Upfront fees: None | Speciality: Kaggle’s programming platform |
| Ratings: Not public | Required prerequisites: Python fundamentals Basic math concepts |
| Estimated duration: 3 Hours | Pacing: Self-paced |
| Certificate Program: No | Number of projects: 0 |
| Students enrolled: Not public | Refund option: Not available (free course) |
| Learning style: 7 Lessons | Career services: None |
Best for Intermediate Learners
Machine Learning Specialization
This program is composed of three courses:
- Supervised Machine Learning: Regression and Classification
- Advanced Learning Algorithms
- Unsupervised Learning, Recommenders, and Reinforcement Learning
This is a great course for people with prior machine learning knowledge, and it covers a broad range of topics that will leave you feeling like a pro.
The curriculum starts with supervised machine learning, where you’ll learn about linear and logistic regression. From there, you’ll jump into more advanced algorithms and learn how to build neural networks using Tensorflow. Finally, you’ll finish with unsupervised learning to perform clustering and anomaly detection and build a deep reinforcement learning model.
Hands on work with Python includes:
- Building machine learning models with NumPy and scikit-learn
- Training neural networks with Tensorflow
- Building recommender systems with a collaborative filtering approach
- Constructing deep reinforcement learning models
Coursera offers a paid subscription version of this program, which allows you to get a certificate of completion, pending completion of the projects and a passing score. Alternatively, you can audit the course for free, but you won’t be eligible for the certificate.
Here’s our detailed breakdown of the course:
| Price: Free; Coursera Plus ($39/month) | Thoroughness: Thorough overview of core machine learning concepts |
| Upfront fees: Free trial (7 days) | Speciality: Python programming with Tensorflow Supervised and unsupervised algorithms |
| Ratings: 4.9 (6,013 Reviews) | Required prerequisites: Python fundamentals |
| Estimated duration: 3 months (9 hours per week) | Pacing: Self-paced |
| Certificate Program: Yes | Number of projects: 1 |
| Students enrolled: 98,393 | Refund option: Refund available within 14 days of payment |
| Learning style: Video lectures, interactive coding exercises, and a final project | Career services: Certificate for showcasing skills |
Student Testimonials
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“I got hooked with everything going on in the courses, from course content and TA feedback, to meetup events and the professor’s Twitter feed.” —Zeeshan U. |
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“The truth is that I love learning — I’m always seeking new opportunities to learn. The quality of content on Coursera always exceeds my expectations.” —Natalie H. |
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”It’s important for me to be able to learn as much as I can. My courses on Coursera have given me confidence and hope for the future.” —Richard B. |
Machine Learning by Georgia Tech
This course, which is part of Georgia Tech’s Online Masters Program, is offered for free through Udacity! The course is a graduate level course and has some lofty prerequisites, but provides an excellent dive into several core machine learning concepts.
Led by instructors Michael Littman, Charles Isbell, and Pushkar Kolhe, you will explore supervised, unsupervised, and reinforcement learning concepts through video lectures and assignments. You will additionally get an introduction to Markov decision processes and information theory.
This course is also part of Udacity’s NanoDegree program, a collection of data science and machine learning-related courses that can be completed with a paid subscription.
Here’s our detailed breakdown of the course:
| Price: Free; $1,077 (NanoDegree Program) | Thoroughness: Detailed curriculum focused on supervised, unsupervised, and reinforcement learning |
| Upfront fees: Free to start | Speciality: Python programmingSupervised, unsupervised, and reinforcement learningInformation theory |
| Ratings: Not public | Required prerequisites: Probability Theory, Linear Algebra and Statistics Familiarity with any programming language Familiarity with Neural Networks |
| Estimated duration: 4 months | Pacing: Self-paced |
| Certificate Program: No (NanoDegree with paid option) | Number of projects: 0 |
| Students enrolled: Not public | Refund option: Refund available within the first 2 days of first month’s subscription date |
| Learning style: Video lectures, assignments, interactive quizzes | Career services: No services offered |
Best for Advanced Learners
Machine Learning with Python: from Linear Models to Deep Learning
This course is part of the MITx MicroMasters program in statistics and data science and requires a proficiency in Python programming and college-level multivariable calculus to take.
This course provides advanced teaching on topics ranging from linear regression to neural networks and reinforcement learning. This course provides hand-on examples in Python, and students will be able to complete several projects, including:
- An automatic review analyzer
- Digit recognition with neural networks
- Reinforcement learning
The course is taught by professors Regina Barzilay, Tommi Jaakkola, and Karene Chu out of MIT, and spans a total of 15 weeks. This course and three other courses comprise the full MicroMasters program at MIT. To earn the degree, you must successfully complete all four courses and pass a proctored exam. Unfortunately, this course is not offered until February 2023, but it’s well worth the price when offered!
Here’s our detailed breakdown of the course:
| Price: Free (limited access); $300 (full access) | Thoroughness: In-depth machine learning algorithms for proficiency in ML |
| Upfront fees: Free to start | Speciality: Python programming Supervised learning, deep learning, and reinforcement learning |
| Ratings: 4.9 (6,013 Reviews) | Required prerequisites: Good understanding of Python programmingProbability theoryCollege-level single/multivariable Calculus Vectors and matrices |
| Estimated duration: 15 Weeks, 10-14 Hours per week Starts 2/1/2023 |
Pacing: Instructor-paced |
| Certificate Program: Yes | Number of projects: 3 |
| Students enrolled: 174,710 | Refund option: Refund available within 14 days of purchase date |
| Learning style: Video lectures, graded exercises and exams (paid version only) | Career services: Certificate upon completion with paid subscription |
Introduction to Machine Learning in Production
This course comes from DeepLearning.AI, started by Stanford’s Andrew Ng. Designed for individuals with a thorough understanding of machine learning, this course walks through the practical deployment of machine learning models in a production environment.
The course is broken down into three week increments, focusing on the machine learning development lifecycle, selecting and training models, and working with different data types with real-world constraints.
This course differs from the other courses in this list by providing practical examples of day-to-day operations as a data scientist. You will learn foundational concepts of machine learning with the functional expertise of modern software development.
Here’s our detailed breakdown of the course:
| Price: $49/mo after free trial | Thoroughness: Thorough preparation for job success |
| Upfront fees: Free trial (7-days) | Speciality: Production quality code |
| Ratings: 4.8 (2,155 Reviews) | Required prerequisites: Some knowledge of AI/deep learning Intermediate understanding of Python programming Experience with any deep learning framework (PyTorch, Keras, or TensorFlow) |
| Estimated duration: 12 Hours | Pacing: Self-paced |
| Certificate Program: Yes | Number of projects: 0 |
| Students enrolled: 66,698 | Refund option: Refund available within 14 days of purchase date |
| Learning style: Video lectures, interactive coding exercises, and a final project | Career services: Certificate upon completion (paid subscription) |
Excellent course; you learn about the fundamentals of MLOps. A recommended course if you want to understand the life cycle of a machine learning algorithm in production.
—DG
I like the acknowledgement of the importance of data quality. Machine learning is much more than just training models. Real benefits can only be achieved when moving to real life data
—SA
Practical and well-structured advice throughout the lifecycle of ML. Examples from real world problems & experiences make the advice more tangible and help to reflect on your own problems.
—DC
Best Online Machine Learning Bootcamp
Coding Dojo’s Data Science Bootcamp
Coding Dojo offers a 16-week (or 20-week) long bootcamp that dives into the fundamentals of data science and machine learning with Python. The program introduces you to data preparation, analysis, and visualization, and how to properly apply machine learning algorithms to real-world examples.
The benefits of this program include building a portfolio to demonstrate your new skills to potential employers and a certificate of completion. The curriculum is hands-on with Python and walks you through topics, including regression models, database programming, unsupervised learning, and data visualization.
The course does come with a high price tag, but has great flexibility and boasts an 83.8% hiring rate within 180 days of graduating.
Here’s our detailed breakdown of the course:
| Price: $11,995 (16 weeks); $13,995 (20 weeks) | Thoroughness: Everything you need to know to get hired |
| Upfront fees: Paid in-full (scholarships and tuition financing options available) | Speciality: Python programming Supervised/Unsupervised learning Data manipulation and visualization |
| Ratings: Not public | Required prerequisites: Python fundamentals |
| Estimated duration: 16-20 Weeks, 20 hours per week | Pacing: Instructor-paced |
| Certificate Program: Yes | Number of projects: Several |
| Students enrolled: Not public | Refund option: Refund before 1st day of class (minus $100 administrative fee) |
| Learning style: Video lectures, assignments, projects | Career services: Job search, resumé prep, and mock interviews |
Best In-person Machine Learning Bootcamp
Columbia Engineering Data Analytics Boot Camp
Columbia University offers a fully-immersive, 24-week data analytics bootcamp to prepare you for a career in data science. The course covers a broad range of topics, which include SQL, intermediate Excel, machine learning, and software development best practices.
The program requires 3 days of work per week, allowing individuals to complete all requirements on a part-time basis. The highlight of the course is Project Demo Day, when students get to showcase their capstone projects to local professionals.
While the cost of attendance is high, you’ll benefit from a broad range of career services to help you get hired. You’ll be assigned a dedicated Profile Coach and Career Director, who will help you polish your resume and prepare you for interviews.
Here’s our detailed breakdown of the course:
| Price: $14,745 | Thoroughness: Everything you need to know to get hired |
| Upfront fees: Paid in-full (tuition financing options available) | Speciality: Python and Javascript programming SQL databases and ExcelMachine learning |
| Ratings: Not public | Required prerequisites: Python fundamentals Basic math skills Two years of experience in business, management, finance, statistics, or a related field |
| Estimated duration: 24 weeks, 3 days per week | Pacing: Instructor-paced |
| Certificate Program: Yes | Number of projects: Several |
| Students enrolled: Not public | Refund option: Available within the first calendar week of the program (initial deposits non-refundable) |
| Learning style: In-person lectures, coding assignments, capstone project | Career services: Dedicated Profile Coach and Career DirectorPortfolio reviews, resume and social media profile support, career content and practice sessions. |
Best for Data Scientists
Dataquest’s Data Scientist in Python Pathway
Dataquest is the best choice for entering a career in data science. The Data Scientist Pathway provides a comprehensive curriculum, which includes learning statistical and machine learning frameworks, working with databases, and designing stunning visualizations.
The career paths include a bundle of courses that will take you from beginner to fully qualified for a specific data science position. These career paths are as comprehensive as a $10,000 bootcamp, but at a fraction of the cost. Upon finishing the course, you will have a comprehensive portfolio of projects to show to potential employers and access to Dataquest’s community of experts and professionals.
Here’s our detailed breakdown of the course:
| Price: $399/yr, $49/mo (discounts offered) | Thoroughness: Everything you need to know to get hired |
| Upfront fees: Free to start | Speciality: Python programmingSQL and database programmingMachine learning All-encompassing data science skill set |
| Ratings: 4.8 (359 Reviews) | Required prerequisites: None |
| Estimated duration: 9 Months (10 Hours per week) | Pacing: Self-paced |
| Certificate Program: Yes | Number of projects: 28 |
| Students enrolled: 271,400 | Refund option: Available if you aren’t satisfied after completion |
| Learning style: 36 Courses, 28 Projects | Career services: Certification upon completion and strong community support |
They are like the real thing. Dataquest shows you exactly what you need to learn and then helps you apply it.
—Eddie Kirkland
Best for Musicians and Artists
Machine Learning for Musicians and Artists
Last in our list is a course for the musically inclined. The University of London’s machine learning course takes a very different angle than other courses on our list by focusing on machine learning techniques that can be used to make sense of human gesture, musical audio, and other real-time data.
In addition to teaching traditional machine learning methodology, like classification and regression, you’ll also learn how to connect your machine learning tools to common digital arts tools, such as Max/MSP and Unity 3D.
Here’s our detailed breakdown of the course:
| Price: $20/mo | Thoroughness: Ability to apply machine learning to artistic problems |
| Upfront fees: 1 session free | Speciality: Machine learning with a focus on digital arts integration |
| Ratings: 5.0 (89 Reviews) | Required prerequisites: Familiarity with Digital Arts Tools (Wekinator)Access to sensors (joystick, webcam, microphone, etc.) |
| Estimated duration: 7 sessions, 8 hours per session | Pacing: Self-paced |
| Certificate Program: Yes | Number of projects: 0 |
| Students enrolled: Not public | Refund option: No refund offered |
| Learning style: Videos and assignments | Career services: Certificate upon completion |
This is a great course for anyone interested in the future of Interaction Design.
—Antonio Rizzo
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