Top 5 Free Machine Learning Courses
Give a boost to your career and learn job-ready machine learning skills by taking the best free online courses.
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There is growing demand for machine learning (ML) engineers in the tech industry. Companies are trying to integrate intelligent products to boost profits and increase customer engagements. The upward trend of AI-related jobs on platforms like LinkedIn and Glassdoor suggests that companies are looking for data scientists with machine learning experience, ML engineers, MLOps engineers, and data engineers with a background in developing AI products. The world is becoming intelligent with time, and to meet the demand, organizations are paying a base salary of $130K+ Glassdoor.
If you are looking to grow in your career and want to earn $130K a year then start your career by completing a professional course on machine learning. Before taking these courses, you need to understand the prerequisites. Almost all of them want you to have experience in Python, Statistics, and Data Science. The advanced courses want you to have hands-on experience with deep learning frameworks and an understanding of the machine learning ecosystem. In this blog, we will learn about the top five free machine learning courses. The level of difficulty of courses ranges from beginner to advanced.
Machine Learning by Stanford
Machine Learning by Stanford University is the most popular and highly rated online course on the internet. Most of my colleagues have taken various Andrew Ng courses, and they all had positive experiences. If you are a beginner with the experience in Python language, I will highly suggest you start your journey by taking a free course by Stanford University. You can also pay an extra $75 to get a certificate and gain access to additional learning resources.
Image from Coursera
Duration: 61 hours
The course will provide a brief introduction to machine learning that will start from linear algebra to creating real-world application (Photo OCR). The course includes supervised learning, unsupervised learning, neural networks, core machine learning algorithms, optimization, and real-world applications such as anomaly detection and recommender systems.
I had a positive experience with Coursera and Andrews Ng courses as he explains everything in a new and effective way. This course will help you jump-start your machine learning career and help you create a strong base for you to build amazing AI applications.
CS50's Introduction to Artificial Intelligence with Python (Harvard University)
CS50's Introduction to Artificial Intelligence with Python | edX teaches you the necessary skills that will put you on the track of becoming a machine learning engineer. In this course, you will be learning machine learning algorithms that have given the rise to technologies; game-playing engines, image classification, machine translation, and stock price predictions. It is a free course, but you can pay to get a certificate, complete access to course work, and interactive projects.
Image by edX
Duration: 140 hours
Benefits: Self-paced, World’s best university Instructors, Career future-proofing
The course includes hands-on experience with machine learning frameworks, graph search algorithms, adversarial search, knowledge representation, logical inference, probability theory, Bayesian networks, Markov models, constraint satisfaction, machine learning, reinforcement learning, neural networks, and natural language processing. You will also learn to design intelligent systems by working on portfolio projects.
Machine Learning by Columbia University
Machine Learning | edX by Columbia University is an intermediate course that helps you master essential machine learning algorithms. It is a free course, but the paid option will give you access to extra features and certificates.
Image by edX
Duration: 96 hours
The course includes supervised learning ( regression/ classification), unsupervised learning data modeling and analysis, and optimizing model performance. The course also covers Markov models, non-negative matrix factorization, continuous state-space models, Laplace approximation, kernel methods, and Gaussian processes. This course is for individuals who want to learn in-depth about model architecture and algorithms.
Practical Deep Learning for Coders
I am a big fan of Jeremy Howard’s work and his free course on Practical Deep Learning for Coders (fast.ai) is just amazing. You get to enjoy assessment exercises, community support, and easy-to-follow youtube tutorials. You will also learn the state-of-the-art models and an easy-to-use deep learning framework (fast.ai) which is built upon PyTorch.
The course covers all the core topics of machine learning with real-world examples. It also includes data ethics, machine learning in production, and developing web applications. You will see a lot of fast.ai alumni working for Google and Amazon so, if you are serious with your career and want to learn machine learning concepts that will land you a job then complete this course within a month and move to the second part.
Image by fast.ai
Duration: 20 - 60 hours
Level: Beginner to Intermediate
The course includes training the deep learning model, evidence-based learning, machine learning in production, stochastic gradient descent, data ethics, tabular data, and natural language processing. Every chapter includes questioners and project exercises that you can run on Google Colab or Gradient. I will highly recommend you to take this course after learning the basics of machine learning. The course has helped me understand the deep workings of neural networks and think out of the box.
Machine Learning Engineering for Production
Machine Learning Engineering for Production (MLOps) is for experienced data scientists and machine learning engineers. The course has helped me understand the data-centric approach of optimizing model performance and taught me production techniques such as developing model pipelines, managing metadata, project scoping and design, concept drift, and human-level performance. You can audit the courses for free, which means you can have access to video tutorials, quizzes, and read course content. You have to pay a monthly fee to get access to projects, project review, and certification.
The specialization is divided into four courses:
- Introduction to Machine Learning in Production
- Machine Learning Data Lifecycle in Production
- Machine Learning Modeling Pipelines in Production
- Deploying Machine Learning Models in Production
Image by Coursera
Duration: 96 hours
The courses will help you excel in your career and prepare you to improve the performance of AI products by incorporating advanced tools. You will learn data drift, concept drifts, data-centric approaches, develop end-to-end machine learning systems, build data pipelines, machine learning operations, and learn advanced techniques on continuously monitoring production systems.
2022 is prime time to learn machine learning skills. If you are interested in beginning your journey or you want to excel in the data career, start taking these courses and complete them on time. These courses will help you build the portfolio and provide you with the necessary skills. The free courses also provide community-driven career support where professionals help each other in finding the right job.
In this blog, we have learned about the top five free machine learning courses. These courses have produced job-ready professionals who are working in top tech giants. The ranking of courses is based on theory, usability, interactive projects & exercises, popularity, real-world projects, and instructor style. I hope you liked my work and if you have further questions about a machine learning career then comment below. I will try to answer all of your questions.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.