Top Free Machine Learning Courses to Take Up in 2024
Machine learning is among the most exciting fields of computer science and statistics that have been helping many industries to become more efficient and smart. The job market demands skilled and knowledgeable professionals but still faces a huge talent gap. To join this trending workforce, we recommend you learn machine learning. We have hand-picked some best-rated yet free machine learning courses to help you improve your skills. Let’s explore.
Content – Free Machine Learning Courses
- Machine Learning by Stanford University
- Unsupervised Machine Learning by IBM
- Introduction to Embedded Machine Learning by Edge Impulse
- Process Mining: Data Science in Action by Eindhoven University of Technology
- Introduction to Machine Learning with R by Simplilearn
- Introduction to Machine Learning by NPTEL
- Machine Learning With Big Data by University of California San Diego
- Getting Started with Machine Learning Algorithms by Simplilearn
- Data Science: Machine Learning from Harvard University
- Practical Machine Learning by Johns Hopkins University
Criteria Used to Pick Free Machine Learning Courses
We have followed the below criteria to pick the best free machine learning courses for you. The course –
- Focus on the concepts of machine learning
- Explain how algorithms work mathematically
- Use popular open-source programming languages, tools, and libraries
- Has programming assignments for practice
- Has the right combination of theory and applications
- Comprises hands-on projects and case studies
- Skilled, experienced, engaging, and personable instructors are teaching the course
- Is rated greater than or equal to 4.5 out of 5
- It is self-paced and on-demand
Explore – Machine Learning Courses
Best-suited Machine Learning courses for you
Learn Machine Learning with these high-rated online courses
1. Machine Learning by Stanford University on Coursera
Rating – 4.9
Number of votes – 169,090
This is by far the best machine learning course available online and is enrolled by over 4,758,219 students globally. The course is created by Andrew Ng, Co-Founder of Coursera and Professor at Stanford University. It introduces you to machine learning, data mining, and statistical pattern recognition.
The course also includes a number of case studies and applications to help you apply machine learning algorithms to build smart robots, learn text recognition, computer vision, database mining, etc. You can enrol in this course for free, use a 7-day free trial, and access the course material.
Duration – 4 weeks
Skill Level – Intermediate
Course Content
- Linear Regression with One Variable & Review
- Linear Regression with Multiple Variables
- Octave/Matlab Tutorial
- Logistic Regression
- Regularization
- Neural Networks: Representation
- Neural Networks: Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
- Support Vector Machines
- Unsupervised Learning
- Dimensionality Reduction
- Anomaly Detection
- Recommender Systems
- Large Scale Machine Learning
- Application Example: Photo OCR
2. Unsupervised Machine Learning by IBM on Coursera
Rating – 4.8
Number of votes – 125
In this course, you will learn about Unsupervised Learning. You can find insights from data sets with no target or labelled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning and select the best-suited algorithm. The course will help you learn the best practices for unsupervised learning.
Duration – 3 Weeks
Skill Level – Beginner
Course Content
- Introduction to Unsupervised Learning and K Means
- Selecting a clustering algorithm
- Dimensionality Reduction
3. Introduction to Embedded Machine Learning by Edge Impulse on Coursera
Rating – 4.8
Number of votes – 302
Introduction to Embedded Machine Learning will provide an overview of machine learning functioning, training neural networks, and deploying those networks to microcontrollers, also known as embedded machine learning or TinyML. The course will cover the concepts and vocabulary to understand the fundamentals of machine learning. You will also gain hands-on learning experience through demonstrations and projects.
Duration – 3 Weeks
Skill Level – Intermediate
Course Content
- Introduction to Machine Learning
- Introduction to Neural Networks
- Audio classification and Keyword Spotting
4. Process Mining: Data Science in Action by Eindhoven University of Technology on Coursera
Rating – 4.7
Number of votes – 1045
Process Mining: Data Science in Action covers the key analysis techniques in process mining and process discovery algorithms. You will explore and learn about different types of process discovery algorithms to automatically learn process models from raw event data. It is an introductory-level course with various practical assignments.
Duration – 4 weeks
Skill Level – Intermediate
Course Content
- Introduction and Data Mining
- Process Models and Process Discovery
- Different Types of Process Models
- Process Discovery Techniques and Conformance Checking
- Enrichment of Process Models
- Operational Support and Conclusion
5. Introduction to Machine Learning with R by Simplilearn
Rating – 4.7
Introduction to Machine Learning with R by Simplilearn is a relatively new course but has received some good reviews from the course takers. You will learn the basics of machine learning and ML algorithms, such as linear regression, logistic regression, decision trees, random forests, SVM, and hierarchical clustering techniques. The course also covers R programming in detail, along with time series analysis in R. It has self-paced video lessons, and you will receive a completion certificate on course completion.
Duration – 10 Hours
Skill Level – Beginner
Course Content
- Introduction to Machine Learning
- Machine Learning Applications
- R programming Introduction and Installation
- Variables Data Types and Logical Operators in R
- Vectors and Lists in R
- Matrix and Data Frames in R
- Flow Control
- Functions in R
- Data Manipulation in R-dplyr and R-tidyr
- Data Visualization in R
- Linear Regression in R
- Logistic Regression in R
- Decision Tree in R
- Random Forest in R
- Support Vector Machine in R
- Hierarchical Clustering in R
- Time Series Analysis in R
6. Introduction to Machine Learning by NPTEL
Rating – 4.7
Number of votes – 2973
Introduction to Machine Learning by NPTEL is among the best-rated free online machine learning courses. Offered by IIT Madras, the course introduces some of the basic concepts of machine learning from a mathematical perspective. It also covers popular algorithms and architectures used in different learning paradigms. 27,889 course takers have already enrolled in this course.
Duration – 4 weeks
Skill Level – Undergraduate/Postgraduate
Course Content
- Introduction to Machine Learning
- Probability Theory
- Linear Algebra
- Statistical Decision Theory
- Linear Regression
- Dimensionality Reduction
- Classification – Linear Models
- Optimization
- Classification – Separating Hyperplane Approaches
- Artificial Neural Networks
- Parameter Estimation
- Decision Trees
- Evaluation Measures
- Hypothesis Testing
- Ensemble Methods
- Graphical Models
- Clustering
- Gaussian Mixture Models
- Spectral Clustering
- Learning Theory
- Frequent Itemset Mining
- Reinforcement Learning
7. Machine Learning With Big Data by University of California San Diego on Coursera
Rating – 4.6
Number of votes – 2385
Machine Learning With Big Data is a part of the Big Data Specialization on Coursera and offers an overview of machine learning techniques to explore, analyze, and leverage data. You will learn to use tools and algorithms to create machine learning models and scale those models up to big data problems.
Duration – 4 Weeks
Skill Level – Intermediate
Course Content
- Data Preparation
- Data Exploration
- Classification
- Evaluation of Machine Learning Models
- Regression, Cluster Analysis, and Association Analysis
8. Getting Started with Machine Learning Algorithms by Simplilearn
Rating – 4.5
Getting Started with Machine Learning Algorithms is another free course from Simplilearn. You will learn about machine learning algorithms, such as supervised learning algorithms and unsupervised learning algorithms, k-means clustering, PCA, reinforcement learning, and Q-learning. It takes you through all the skills essential for a skilled machine learning engineer.
Duration – 6 Hours
Skill Level – Beginner
Course Content
- Introduction to Machine Learning
- Supervised Learning Algorithms- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- Support Vector Machine (SVM)
- K Nearest Neighbors (KNN)
- Unsupervised Learning Algorithms- K Means Clustering
- Principal component analysis (PCA)
- Reinforcement Learning
- Q Learning
9. Data Science: Machine Learning from Harvard University on edX
Rating – 4.5
Data Science: Machine Learning from Harvard University course will help you to learn popular machine learning algorithms, PCA, and regularization by building a movie recommendation system. Besides, you will learn about training data and how to use a set of data to discover potentially predictive relationships. 379,372 students have already enrolled in this course.
Duration – 7 Hours
Skill Level – Intermediate
Course Content
- The basics of machine learning
- How to perform cross-validation to avoid overtraining
- Several popular machine learning algorithms
- How to build a recommendation system
- What is regularization, and why is it useful?
10. Practical Machine Learning by Johns Hopkins University on Coursera
Rating – 4.5
Number of votes – 3189
In the Practical Machine Learning course, you will learn the basic concepts of building and applying prediction functions. It will also cover the basics of training and tests sets, overfitting, and error rates and introduce a range of model-based and algorithmic machine learning methods, including regression, classification trees, Naive Bayes, and random forests.
Duration – 4 Weeks
Skill Level – Intermediate
Course Content
- Prediction, Errors, and Cross-Validation
- The Caret Package
- Predicting with trees, Random Forests, and model-based Predictions
- Regularized Regression and Combining Predictors
Conclusion
Machine learning is a fascinating subject and has been one of the hottest job profiles in the past few years. We hope this free machine learning course list will help you pick the right per your professional aspirations.
Apart from taking up these courses, we recommend you work on machine learning projects using real data sets. Create projects on Kaggle and GitHub and join online communities like Stack Overflow, Machine Learning Stories – Hacker Noon, MetaOptimize Q+A, etc., to understand better how machine learning works and get connected with domain experts and like-minded professionals.
All the best!
Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio
Top Picks & New Arrivals
