Tech companies need more machine learning (ML) engineers. Companies are integrating intelligent products to promote earnings and customer engagement. Start your career with machine learning training to make $130K a year. You must know the prerequisites before taking these courses. Most require Python, Statistics, and Data Science experience. Advanced courses require hands-on experience with deep learning frameworks and machine learning ecosystem knowledge. This article covers the top free machine learning courses.
What do you mean by free machine learning courses?
You may get both the fundamentals and more advanced knowledge of Machine Learning through free online courses. These courses cover supervised and unsupervised learning, pre-processing data, engineering features, creating models, and testing their performance. Common concepts include deep learning, natural language processing, and big data.
Students will get an in-depth introduction to the area, emphasizing its practical applications in these courses. Case studies, examples, programming languages, and their applications are all covered in these courses. A free machine-learning certificate is another perk for students who finish these courses. It will teach students how to use Machine Learning algorithms in practical ways so that they can master the technology.
Enrolling in a machine learning free course covering the fundamentals can be helpful if you are starting in machine learning. If you want to get a solid grounding in the subject and then go on to more in-depth self-directed study, this is a great place to start.
A plethora of online machine-learning courses are at your fingertips. The length of these courses, from introductory seminars to massive open online courses (MOOCs), can vary greatly, as can the price.
List of must-try free ML courses
- Introduction to Machine Learning with R by Simplilearn
Though new, Simplilearn’s Introduction to Machine Learning with R has garnered positive feedback. You will learn linear regression, logistic regression, decision trees, random forests, SVM, and hierarchical clustering. The course covers intensive R programming and time series analysis. Upon finishing the course, you will get a completion certificate and access to self-paced video lessons.
- Getting started with Machine Learning Algorithms by Simplilearn
A further no-cost course that Simplilearn offers is Getting Started with Machine Learning Algorithms. Algorithms for machine learning will be covered, including k-means clustering, principal component analysis (PCA), reinforcement learning, Q-learning, and supervised and unsupervised learning. Simplilearn online courses teach you everything you need to know to become an expert machine learning engineer.
- Deep Learning Prerequisites: The Numpy Stack in Python V2
This free training will hone your Deep Learning and NumPy stack skills. Four main Python libraries—Numpy, Scipy, Pandas, and the Matplotlib stack—are covered here; they are essential to AI, DL, and ML.
You will also get knowledge of numerical algorithm implementation using Numpy, Scipy, Matplotlib, and Pandas. Lastly, you will understand the advantages and disadvantages of machine learning models.
- Machine Learning by Stanford
If you’re looking for a top-rated online course, go for Machine Learning, which Stanford University offers. Start with a Stanford University-provided free system if you are completely new to Python. An additional $75 will grant you access to supplementary learning materials and a certificate.
Beginning with linear algebra and moving on to the development of practical applications (Photo OCR), this course will offer a concise overview of machine learning. Anomaly detection, recommender systems, optimization, core ML methods, neural networks, supervised and unsupervised learning, and other real-world applications are covered in the course.
- Machine Learning Engineering for Production
Data scientists and engineers with competence in machine learning can benefit from Machine Learning Engineering for Production (MLOps). The course covers production approaches such as creating model pipelines, managing metadata, project scoping and design, concept drift, and human-level performance that make you understand the data-centric approach to maximizing model performance. All course materials, like video lessons, quizzes, and readings, are available during your free audit. Access to projects, project approval, and certification are subject to a monthly cost.
You will be well-prepared to enhance the performance of AI products by integrating advanced tools, and the courses will help you succeed professionally. You will gain knowledge of data drift, idea drift, data-centric methodologies, end-to-end ML system development, data pipeline building, ML operations, and advanced CM techniques.
- Machine Learning for Data Science and Analytics
This machine learning (ML) artificial intelligence course is the next best thing to attending Columbia University if you’ve ever wanted to but have not had the opportunity. It is run by some of the most experienced lecturers at the university, including computer science and statistics experts, and is dedicated to data scientists. You will master the basics of machine learning (ML) and its techniques, such as supervised and unsupervised learning, linear regression, and more.
- Fundamentals of Reinforcement Learning
This subfield of machine learning (ML) will be covered extensively in the upcoming course. This technology is present in various practical contexts, including autonomous vehicles, healthcare, video games, and advertising. This area is diverse enough to pique the interest of anybody; for example, the University of Alberta’s AI course is structured over four semesters and includes practical programming projects and exams that put what you learn into practice by allowing you to solve real-world business challenges.
- Machine Learning Crash Course by Google
Google offers an introductory crash course in machine learning called “Machine Learning Crash Course.” Essential concepts, including neural networks, logistic, and linear regression, are covered. Get a solid foundation in ML and grasp its practical applications in diverse fields with this course’s interactive activities and real-world examples.
By the way
Machine learning is an exciting field that has recently become a popular career path. You can choose the one that best fits your career goals using this list of free machine learning courses.
On top of enrolling in these courses, you should try your hand at machine learning projects with actual data sets. If you want to learn more about machine learning and make connections with other professionals and subject experts, try doing projects on Kaggle and GitHub and joining online forums like Stack Overflow, Machine Learning Stories – Hacker Noon, MetaOptimize Q+A, etc.
