Top 9 Best Machine Learning Courses in 2024

What is Machine Learning?

The ability of computers to recognize patterns in data and use them to make predictions that are beyond our capabilities – is transforming the world in profound ways. With the rapid advancement of technology, machine learning is spawning innovations on a large and small scale, from customer service chatbots to facial recognition software to autonomous cars. Since many companies in different sectors have embraced it, there are a variety of career opportunities across the industry.

If you have a background in machine learning, you can become a Machine Learning Engineer, Natural Language Processing (NLP) Scientist, Data Scientist, Human-Centered Machine Learning Designer or Business Intelligence Developer. In recent years, the demand for machine learning specialists has risen, with big tech companies willing to pay generous salaries to the best candidates. 

Interested in building a career in this niche? Take the best Machine Learning course to acquire comprehensive knowledge of the field and related concepts. Students enrolled in ML courses gain the knowledge and skills that they need to navigate real-life challenges. Whether you are interested in getting a glimpse of machine learning or trying to build a career in the field, you will gain much-needed exposure through these courses.

In this article, we’ve listed the best machine courses and programs you can take to upskill yourself and secure one of the best machine-learning jobs in 2023. Develop the necessary industry-ready skills and knowledge with one of the top online course machine learning. Let’s check out these courses now!

Best Machine Learning Courses ( Free & Paid) to Enroll in 2023

In curating the list of best online machine learning courses for 2023, a number of factors/aspects were taken into consideration. In this way, the overall pool of courses gets narrowed quickly, but the overall goal is to assist you in choosing a course that is worth your time and energy.

• Syllabus Covered: The list is drafted taking into account what is covered in the syllabus and how well it has been designed to cater to different expertise levels.
  
• Course Highlights & outcomes: Furthermore, we have discussed the course outcomes and other features that will help students gain marketable skills, such as placement assistance, mock interviews, and hands-on projects.
  
• Skills Required: We have discussed the prerequisite skills that candidates need to possess to enrol in the course.
  
• Course Duration: We have determined the duration of each course.
  
• Course Fees: The courses are ranked according to their features and fees, so you get the best value.

Below is a list of the best machine learning courses, certifications, and programs you can pursue in 2023 to become a machine learning expert and land your dream job. This list includes both free and paid certificate programs that have received widespread acclaim and are used by thousands of students, professionals and learners worldwide.

1. Data Science and Machine Learning Program by Scaler 

Designed with insights from advisors from the top 50 tech companies, this program is considered to be the most popular online course in Data Science and Machine Learning. The course adds value to you as a developer and enables you to understand the mathematics behind multiple machine-learning algorithms. With the well-structured modules and hands-on training, you will be prepared to tackle the toughest Machine Learning and Data Science challenges. Getting started with Scaler’s Data Science and Machine Learning program does not require any coding experience. It is designed to cater to students with different expertise levels: beginners, intermediates, and advanced. It is among the most popular and highly rated courses on this list.

Course Highlights: 

• Covers all aspects of data science and machine learning, starting with the basics of programming (string, decision tree and controls, binary, loops, etc.) to intermediate programming topics (arrays, number systems, OOPs, sorting, hashing, recursion, etc.) to ML Engineering (Neural networks, NLP, Reinforcement Learning, PyTorch, Keras, etc.) and advanced programming topics (Stacks, Queues, Heap, DB and System design, Hashing, Greedy, etc.).
  
• It offers career support, and 1:1 mentorship programs with Machine Learning Engineers, and has a strong student community of 20K.
  
• Give you access to 600+ placement partners (such as Myntra, Paytm, Olx, Atlassian, Google, Flipkart, Adobe, etc.) to help you find a data science job.
  
• As part of the program, you’ll be working on real-world projects and getting real-time feedback from industry professionals. 
  
• Experienced experts assist you in optimizing your resume and LinkedIn profile.
  
• Provide affordable scholarships and financing.
  
• You can try it for free with a 14-day money-back guarantee.
  
• It assists you in preparing for interviews by conducting mock interviews with people in the industry.
  
• Provide ongoing support after you complete the course, boosting your confidence day by day.

Skills Required: Coding experience or the knowledge of at least one programming language is needed to get started with Scaler’s Data Science and Machine Learning program. 

Course Duration: 11–13 months.

Course Fees: INR 3.49 lakhs including GST, 100% refund if you decide to withdraw within two weeks (EMIs available).

Link: Check Scaler’s Data Science and Machine Learning Program

2. Machine Learning by Stanford University

This Machine Learning course is taught by Andrew Ng, who was formerly Chief Scientist at Baidu and Director of Google Brain Deep Learning Project. It includes both theoretical and practical aspects of machine learning algorithms. Furthermore, you can learn how to implement machine learning algorithms for computer vision, text understanding, database mining, and creating robots. Additionally, you’ll be given the opportunity to complete practical projects using Octave and Matlab that involve optical character recognition. Upon completing this course, you will receive a Shareable Certificate that can be displayed on your resume or LinkedIn profile. 

Course Highlights:

• This course will introduce you to basics of machine learning, Linear Regression with One Variable and Multiple Variables, Neural Networks,  Logistic Regression, Unsupervised Learning, Regularization, Support Vector Machines, etc., that you can learn using Octave or MATLAB.
  
• Over the course of eleven weeks, this course covers various aspects and applications of Machine Learning. 
  
• You’ll learn how to deal with tasks such as multiclass classification and anomaly detection. 
  
• There is at least one auto-graded quiz each week. 

Skills Required: A basic understanding of linear algebra, probability, and statistics is required.

Course Duration: 11 weeks (Approx.)

Course Fees: $4,056.00-$5,408.00

3. Machine Learning Specialization by the University of Washington

This Machine Learning Specialization is designed to teach theoretical knowledge and hands-on experience to give students a solid foundation of Regression algorithms, Clustering algorithms, Classification algorithms, and Information Retrieval. This three-course certificate program will prepare you for the role of a machine learning scientist or engineer. A software developer, statistician, experienced applied mathematician or data scientist aspiring to be a machine learning scientist should take this course. Upon completing this course, you will receive a Shareable Certificate that can be displayed on your resume or LinkedIn profile. 

Course Highlights:

• This course will introduce you to statistical analyses, mathematical modelling, probability, and optimization techniques, Supervised and unsupervised learning models, advanced machine learning applications, deep learning concepts and applications, etc.
  
• You will gain hands-on experience working with open-source tools such as TensorFlow, Sci-kit-learn, and Keras. 
  
• You will be taught how to create intelligent applications, analyze large datasets, etc., using Machine Learning. 
  
• This program offers you the convenience of online learning as well as the immediacy of real-time interaction.

Skills Required: If you are a software engineer, software developer, or another type of engineer, you will need some programming experience in C/C++, Java, or Python. Equivalent personal projects such as those in Kaggle. Also, undergraduate mathematics courses cover linear algebra, calculus, and probability. Undergraduate courses in statistics or completion of the Foundations of Statistics course. If you are a statistician, applied mathematician, data scientist, or have a PhD in another quantitative field then some experience as a statistician, data scientist, applied mathematician, or a PhD in a quantitative field is required.

Course Duration: 8 Months

Course Fees: $4,548

4. Machine Learning Crash Course with TensorFlow APIs

As a hands-on introduction to machine learning, the crash course offered by Google is a great deal of practical experience. It starts by asking you about your previous experience in machine learning. You will be directed to the appropriate resources based on your answer, so you can maximize your time. Furthermore, it is pragmatic and flexible. It is intended for complete beginners, but will also allow those who have some prior experience in machine learning to take the course as a refresher. 

Course Highlights:

• This course will introduce you to basic machine-learning concepts, such as regressions, loss functions, and gradient descent.
  
• The course includes video lectures, real-world case studies, and hands-on practice exercises.
  
• This Crash Course teaches you the basics of machine learning, as well as how to apply them to real-life problems.
  
• Video lectures from Google researchers.

Skills Required: Programming knowledge is not mandatory, but applicants should have a basic understanding of mathematics and statistics.

Course Duration: 15 hours (Approx)

5. Machine Learning for Data Science and Analytics by ColumbiaX

This course intends to give you a basic understanding of machine learning and its different algorithms. During this course, you will learn about Machine Learning algorithms such as Support Vector Machines, Logistic Regression, Unsupervised Learning, Linear Regression with One and Multiple Variables, etc. You will also learn how to find hidden meaning in large amounts of data by using data analysis and topic modeling. This machine learning course emphasizes the theory of statistical machine learning than the practical application of machine learning. Upon completing this course, you will receive a shareable certificate proving your proficiency in Machine Learning for Data Science and Analytics.

Course Highlights:

• You will gain an understanding of machine learning and be able to develop practical solutions through predictive analytics.
  
• The curriculum is well-structured.
  
• The lessons are in-depth and informative.
  
• The course is self-paced. Hence, you can schedule and learn when it is convenient for you.

Skills Required: Applicants should have a basic understanding of math and computer programming.

Course Duration: 5 Weeks (Approx)

Course Fees: Free

6. Machine Learning by HarvardX

In this course, you will learn the basics of Machine Learning, principal component analysis, and regularization by developing a movie recommender system. Building the movie recommendation system will allow you to learn how to train algorithms using training data so that you can predict the results/outcomes of future datasets. The course will cover Machine Learning algorithms such as Linear Regression with One Variable and Multiple Variables,  Unsupervised Learning, Support Vector Machines, Logistic Regression, etc. Upon completing this course, you will receive a shareable certificate proving your proficiency in Machine Learning for Data Science and Analytics.

Course Highlights:

• You will build a movie recommendation system to learn popular algorithms for machine learning, principal component analysis, and regularization.
  
• You will learn what regularization is and how it can be useful.
  
• This course is self-paced. It means that you can schedule and learn at your convenience.
  
• It has a well-structured curriculum.

Skills Required: Programming knowledge is not mandatory, but applicants should have a basic understanding of mathematics and statistics.

Course Duration: 8 Weeks 

Course Fees: Free or $99 for Certificate

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

This course is meant to teach you the fundamentals of Machine Learning and Data Science from A-Z. It is a great course for students who want to learn Data Science and Machine Learning, as well as for professionals who wish to enter these fields. After building an understanding of each concept and method, you can apply them to solve real problems using dedicated machine learning libraries. Upon completing this course, you will receive a Shareable Certificate that can be displayed on your resume or LinkedIn profile.

Course Highlights:

• This course teaches machine learning in both Python and R and focuses on more specific topics such as Deep Learning, Natural Language Processing, Reinforcement Learning, etc.
  
• This is a hands-on course that includes lots of code examples so that you can practice.
  
• If you are interested in diving right into “doing”, this might be the course for you.
  
• Over 40 hours of video lessons are interspersed with exercises in this course.

Skills Required: You only need to be familiar with some math concepts from high school.

Course Duration: 45 hours (Approx)

Course Fees: INR 3499 

8. Machine Learning with Python by IBM

The course introduces students to the basic concepts of machine learning by using the well-known programming language Python, and its applications in fields ranging from healthcare, telecommunications, and finance to high-performance computing. Additionally, it discusses the differences between supervised and unsupervised learning, as well as which type of learning is best suited to which tasks. After completing this course, you will have learned a great deal about the mathematics behind machine learning. Upon completing this course, you will receive a Shareable Certificate that can be displayed on your resume or LinkedIn profile.

Course Highlights:

• The course offers real-life examples of machine learning and shows you how it affects society in unexpected ways.
  
• This course covers topics such as Machine Learning algorithms, model evaluation, supervised vs unsupervised learning, etc.
  
• You can start instantly and learn at your own pace.
  
• This course will expose you to machine learning libraries like scikit-learn and SciPy.

Skills Required:  Applicants should have a basic understanding of math and computer programming. Some prior programming experience is also recommended.

Course Duration: 4 Weeks (Approx)

9. Machine Learning by Georgia Tech

This course will cover a broad range of machine learning topics, with an emphasis on breadth over depth. Instead of going into the finer details of implementing specific machine learning algorithms, the course takes a high-level approach to machine learning concepts. What makes this course so effective is its instructional approach. The course is taught by two instructors, and each lesson takes the form of a conversation between two of them, with one instructor acting as a student and raising questions, while the other one answering and explaining in detail. It’s refreshing to hear this kind of exchange in a machine learning course. 

Course Highlights:

• The course introduces you to supervised and unsupervised learning, reinforcement learning, regression and classification, clustering, feature selection, and randomized optimization, as well as markov decision processes, game theory, and decision-making.
  
• After completing this course, you will have a comprehensive understanding of supervised, unsupervised, and reinforcement learning, and their differences.
  
• Additionally, you will learn how to implement methods to solve these problems, interpret the results of these methods, and evaluate the correctness of their solutions.

Skills Required: Programming knowledge is not mandatory, but applicants should have a basic understanding of mathematics and statistics.

Course Duration: 4 Months (Approx)

Course Fees: Free

Conclusion

Machine learning is a great career choice if you’re passionate about data, automation, and algorithms. Your day will be filled with moving and processing large amounts of raw data, implementing algorithms to allow that data to be processed, and automating the process to optimize it. As a means of making products, services, and applications more intelligent and effective, machine learning is a major asset. Those interested in developing a career in this field should be familiar with Machine Learning and related concepts. Taking a best machine learning course tailored to your needs would have the potential of taking your career to the next level. 

The courses mentioned in this list, such as those from Scaler, Stanford University, University of Washington, Google, ColumbiaX, etc., will assist you in becoming an expert at machine learning since they cover most of the ML topics in depth. Moreover, these courses provide fantastic career and placement guidance. In addition to being cost-effective, these Machine Learning courses are also completely flexible, allowing you to learn anywhere, anytime.In contrast, some of the courses offered by Udemy, Coursera, Udacity, IIM, etc., focus on specific topics and might not be able to cater to all of the students regardless of their expertise level. Hopefully, this article met your expectations and you enjoyed learning about different options along the way. Get certified in Machine Learning to broaden your career options. 

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