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University Programs

UNIVERSITY
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Great Learning

12 weeks  • Online

UNIVERSITY
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Walsh College

2 Years  • Online

UNIVERSITY
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MIT Professional Education

14 Weeks  • Online

Learn from MIT Faculty
UNIVERSITY
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Johns Hopkins University

16 weeks  • Online

Free Supervised Machine Learning Courses

BASICS
Random Forest
star   4.37 2.5K+ Learners 1 hr

Skills: Random Forest

BASICS
Bagging and Boosting
star   4.62 2K+ Learners 1 hr

Skills: Working with Prediction Errors, Understanding Ensemble Methods, Introduction to Bagging and Boosting, Bagging vs Boosting, Practical Demo in Python

BASICS
Random Forest Regression
star   4.49 1.5K+ Learners 1 hr

Skills: Random Forest Regression, Hands-on, Logistic Regression vs Random Forest , Linear Regression vs Random Forest

free icon BASICS
Random Forest
star   4.37 2.5K+ Learners 1 hr

Skills: Random Forest

free icon BASICS
Bagging and Boosting
star   4.62 2K+ Learners 1 hr

Skills: Working with Prediction Errors, Understanding Ensemble Methods, Introduction to Bagging and Boosting, Bagging vs Boosting, Practical Demo in Python

free icon BASICS
Random Forest Regression
star   4.49 1.5K+ Learners 1 hr

Skills: Random Forest Regression, Hands-on, Logistic Regression vs Random Forest , Linear Regression vs Random Forest

Learn Supervised Machine Learning & Get Completion Certificates

Supervised machine learning is a vital subset of artificial intelligence that teaches algorithms to predict or make decisions from tagged training data. It involves guiding the algorithm with explicit feedback, mimicking a teacher-student learning relationship. This allows the model to extrapolate from training data to make accurate predictions on new data.

 

Key Highlights of Our Free Supervised Machine Learning Courses Collection

  • Foundational and Advanced Topics: The courses cover basic and intricate aspects of supervised learning, including classification and regression techniques.
  • Practical Applications: Explore real-world applications in various fields such as healthcare, finance, and marketing.
  • Comprehensive Learning: From data preparation to model evaluation, understand every step in the supervised machine learning pipeline.

 

Skills Covered

  • Pattern Recognition: Learn to identify patterns and relationships between input features and target variables.
  • Model Building: Gain expertise in constructing models for classification (categorizing data points) and regression (predicting continuous values).
  • Algorithm Application: Master the use of major algorithms, such as decision trees, neural networks, support vector machines, and more.
  • Performance Evaluation: Develop skills in assessing model accuracy using metrics like precision, recall, and F1 score.

 

Who Should Take Our Free Supervised Machine Learning Courses?

This course is designed for aspiring data scientists, AI specialists, and professionals who want to enhance their predictive analytics capabilities. It also suits students and researchers interested in applying machine learning to solve practical problems.

 

What Will You Learn in Free Supervised Machine Learning Courses?

  • Core Concepts: Understand the essentials of supervised learning, from data labeling to model optimization.
  • Classification Techniques: Learn to classify data into predefined categories using various algorithms.
  • Regression Methods: Explore how to predict numerical values using regression models.
  • Real-world Applications: Discover how supervised learning is applied in diverse industries to solve specific challenges.
  • Model Optimization: Get hands-on experience in refining machine learning models to enhance their accuracy and efficiency.

 

By the end of these courses, participants will be equipped to implement supervised machine learning models effectively, making them valuable assets in any data-driven organization.

 

Enroll in the Great Learning Academy's Free Supervised Machine Learning Courses today and earn a certificate in data structures to advance your programming skills and career.

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Get started with these courses

BASICS
Random Forest Regression
star   4.49 1.5K+ Learners 1 hr

Skills: Random Forest Regression, Hands-on, Logistic Regression vs Random Forest , Linear Regression vs Random Forest

BASICS
Supervised Machine Learning Tutorial
star   4.43 2.4K+ Learners 1 hr

Skills: Supervised Machine Learning, Linear Regression, Characteristics of Supervised Machine Learning

BASICS
Bagging and Boosting
star   4.62 2K+ Learners 1 hr

Skills: Working with Prediction Errors, Understanding Ensemble Methods, Introduction to Bagging and Boosting, Bagging vs Boosting, Practical Demo in Python

BASICS
Support Vector Machines
star   4.53 3.2K+ Learners 1 hr

Skills: Introduction to Machine Learning, Kernel Functions, SVM Demo in Python

BASICS
Decision Tree
star   4.43 3.6K+ Learners 1.5 hrs

Skills: Entropy, Heterogeneity, Shannon's Entropy, Preventing Overfitting

BASICS
Random Forest
star   4.37 2.5K+ Learners 1 hr

Skills: Random Forest

BASICS
Logistic Regression on Customer Data
star   4.5 3.2K+ Learners 1 hr

Skills: Logistic Regression on Customer Data

BASICS
Python for Machine Learning
star   4.51 474.8K+ Learners 1.5 hrs

Skills: NumPy Arrays, NumPy Operations, NumPy Math, Saving & Loading NumPy, Pandas Series, Pandas DataFrame, Pandas Functions (Mean, Median, Max, Min), Data Manipulation, Supervised Learning, Unsupervised Learning, Machine Learning with Python

BASICS
Basics of Machine Learning
star   4.39 149.4K+ Learners 2.5 hrs

Skills: Introduction to Machine Learning, Supervised Machine Learning, Linear Regression, Pearson's Coefficient, Coefficient of Determinant

BASICS
Introduction to Machine Learning
star   4.46 77.9K+ Learners 1 hr

Skills: Learn the fundamentals of machine learning, including supervised and unsupervised learning, regression, and recommendation systems. Join this free machine learning course to apply these skills in real-world business scenarios.

BASICS
Machine Learning Algorithms
star   4.49 32.5K+ Learners 1.5 hrs

Skills: Classification (Logistic Regression, Decision Trees, SVM), Regression (Linear, Ridge, Lasso), Clustering (K-means, Hierarchical), model evaluation, cross validation

BASICS
Supervised Machine Learning with Logistic Regression and Naïve Bayes
star   4.43 21.9K+ Learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

BASICS
Python Libraries for Machine Learning
star   4.55 10.2K+ Learners 2.5 hrs

Skills: Numpy, Pandas, Matplotlib, SeaBorn

BASICS
Supervised Machine Learning with Tree Based Models
star   4.56 9.9K+ Learners 2 hrs

Skills: Scikit Learn Library, Decision Tree, Random Forest, Demonstration for Algorithms

BASICS
Data Preparation for Machine Learning
star   4.49 7.5K+ Learners 1 hr

Skills: Data Leakage, Data Balancing, K-fold Cross Validation, Model Building

New

BASICS
Random Forest Regression
star   4.49 1.5K+ Learners 1 hr

Skills: Random Forest Regression, Hands-on, Logistic Regression vs Random Forest , Linear Regression vs Random Forest

BASICS
Supervised Machine Learning Tutorial
star   4.43 2.4K+ Learners 1 hr

Skills: Supervised Machine Learning, Linear Regression, Characteristics of Supervised Machine Learning

BASICS
Bagging and Boosting
star   4.62 2K+ Learners 1 hr

Skills: Working with Prediction Errors, Understanding Ensemble Methods, Introduction to Bagging and Boosting, Bagging vs Boosting, Practical Demo in Python

BASICS
Support Vector Machines
star   4.53 3.2K+ Learners 1 hr

Skills: Introduction to Machine Learning, Kernel Functions, SVM Demo in Python

BASICS
Decision Tree
star   4.43 3.6K+ Learners 1.5 hrs

Skills: Entropy, Heterogeneity, Shannon's Entropy, Preventing Overfitting

BASICS
Random Forest
star   4.37 2.5K+ Learners 1 hr

Skills: Random Forest

BASICS
Logistic Regression on Customer Data
star   4.5 3.2K+ Learners 1 hr

Skills: Logistic Regression on Customer Data

Popular

BASICS
Python for Machine Learning
star   4.51 474.8K+ Learners 1.5 hrs

Skills: NumPy Arrays, NumPy Operations, NumPy Math, Saving & Loading NumPy, Pandas Series, Pandas DataFrame, Pandas Functions (Mean, Median, Max, Min), Data Manipulation, Supervised Learning, Unsupervised Learning, Machine Learning with Python

BASICS
Basics of Machine Learning
star   4.39 149.4K+ Learners 2.5 hrs

Skills: Introduction to Machine Learning, Supervised Machine Learning, Linear Regression, Pearson's Coefficient, Coefficient of Determinant

BASICS
Introduction to Machine Learning
star   4.46 77.9K+ Learners 1 hr

Skills: Learn the fundamentals of machine learning, including supervised and unsupervised learning, regression, and recommendation systems. Join this free machine learning course to apply these skills in real-world business scenarios.

BASICS
Machine Learning Algorithms
star   4.49 32.5K+ Learners 1.5 hrs

Skills: Classification (Logistic Regression, Decision Trees, SVM), Regression (Linear, Ridge, Lasso), Clustering (K-means, Hierarchical), model evaluation, cross validation

BASICS
Supervised Machine Learning with Logistic Regression and Naïve Bayes
star   4.43 21.9K+ Learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

BASICS
Python Libraries for Machine Learning
star   4.55 10.2K+ Learners 2.5 hrs

Skills: Numpy, Pandas, Matplotlib, SeaBorn

BASICS
Supervised Machine Learning with Tree Based Models
star   4.56 9.9K+ Learners 2 hrs

Skills: Scikit Learn Library, Decision Tree, Random Forest, Demonstration for Algorithms

BASICS
Data Preparation for Machine Learning
star   4.49 7.5K+ Learners 1 hr

Skills: Data Leakage, Data Balancing, K-fold Cross Validation, Model Building

Learner reviews of the Free Supervised Machine Learning Courses

Our learners share their experiences of our courses

4.48
67%
24%
6%
1%
2%
Reviewer Profile

5.0

India
“Machine Learning, Models, and Their Main Steps”
This is the best course I have taken. There is a lot of knowledge based on machine learning, machine learning types, and how machine learning works. It also covers the main steps when performing machine learning model-based work.
Reviewer Profile

4.0

India
“Comprehensive and Insightful Course”
The machine learning course is exceptional, providing a solid foundation in concepts like supervised, unsupervised learning, and model evaluation. The hands-on projects make learning engaging, while real-world examples enhance understanding. The instructors break down complex algorithms like regression, decision trees, and neural networks with clarity. It's perfect for beginners and intermediate learners aiming to apply ML techniques effectively.
Reviewer Profile

5.0

India
“Highlight of My Learning Experience in Introduction to Machine Learning”
I really enjoyed the hands-on approach in the Introduction to Machine Learning course. The practical exercises helped me understand key concepts like supervised and unsupervised learning, as well as algorithms such as linear regression and decision trees. The real-world applications and project work made the learning experience engaging and gave me the confidence to apply what I learned to solve real problems.
Reviewer Profile

5.0

India
“Hands-On Experience with ML Models and Practical Applications”
I enjoyed how the course provided both theoretical knowledge and hands-on practice. Exploring real-world datasets and seeing the models in action made concepts like regression, classification, and clustering more tangible. The assignments were engaging, and the opportunity to work with tools like Python and Scikit-learn helped deepen my understanding.
Reviewer Profile

5.0

India
“The Machine Learning Course: An Enlightening and Transformative Experience”
Throughout the Machine Learning course, I had the opportunity to dive into both the theory and the practical aspects of building intelligent systems. One of the most rewarding parts was grasping the mathematical foundations behind algorithms, such as linear regression, logistic regression, and neural networks. In the end, what stood out most was the versatility and impact of machine learning. This experience has truly been a stepping stone in my journey as a data scientist, and I’m eager to apply the knowledge gained to new, real-world challenges!
Reviewer Profile

5.0

India
“Highlight of My Learning Experience: Engaging Projects and Hands-On Learning”
I especially enjoyed the interactive nature of the sessions, where I could apply theories to real-world scenarios. The collaborative environment and the opportunity to discuss ideas with peers made the learning process more enjoyable and insightful.
Reviewer Profile

4.0

India
“Engaging Learning Experience and Enjoyment”
I particularly enjoyed the clear and concise curriculum and the practical, real-world examples provided by the instructor. The skill-based approach was especially helpful in reinforcing my understanding of the topics. The quizzes and assignments were well-structured and provided valuable opportunities to apply my knowledge.
Reviewer Profile

5.0

India
“The Machine Learning Course: Insights into Core Concepts”
The ML course was insightful, offering practical tools, core concepts, and real-world applications.
Reviewer Profile

4.0

India
“Engaging Content and Practical Application”
I really enjoyed the clear explanations and hands-on approach to learning. The course provided a great balance of theory and practical exercises, allowing me to apply concepts in real-world scenarios.
Reviewer Profile

5.0

India
“Introduction to Machine Learning: Concepts, Techniques, and Applications”
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on developing algorithms and models that allow computers to learn patterns from data and make predictions or decisions without being explicitly programmed. It is at the core of many modern technologies, including search engines, recommendation systems, autonomous vehicles, and natural language processing.

Meet your faculty

Meet industry experts who will teach you relevant skills in Supervised Machine Learning

instructor img

Dr. Abhinanda Sarkar

Senior Faculty & Director Academics, Great Learning
  • 30+ years of experience in data science, ML, and analytics.
  • Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.
instructor img

Mr. Bharani Akella

Data Scientist
Bharani has been working in the field of data science for the last 2 years. He has expertise in languages such as Python, R and Java. He also has expertise in the field of deep learning and has worked with deep learning frameworks such as Keras and TensorFlow. He has been in the technical content side from last 2 years and has taught numerous classes with respect to data science.
instructor img

Prof. Mukesh Rao

Senior Faculty, Academics, Great Learning
  • 20+ years of expertise in AI, machine learning, and analytics
  • Director - Academics at Great Learning

Frequently Asked Questions

How can I learn the Supervised Machine Learning course for free?

Great Learning offers free Supervised Machine Learning courses addressing basic to advanced concepts. Enroll in the course that suits your interest through the pool of courses and earn free Supervised Machine Learning certificates of course completion.

Can I learn about Supervised Machine Learning on my own?

With the support of online learning platforms, learning concepts on your own is now possible. Great Learning Academy is a platform that provides free Supervised Machine Learning courses where learners can learn at their own pace.

How long does it take to complete these Supervised Machine Learning courses?

These free Supervised Machine Learning courses offered by Great Learning Academy contain self-paced videos allowing learners to learn crucial concepts and gain in-demand supervised machine learning skills at their convenience.

Will I have lifetime access to these Supervised Machine Learning courses with certificates?

Yes. You will have lifelong access to these free Supervised Machine Learning courses Great Learning Academy offers.

What are my next learning options after these Supervised Machine Learning courses?


You can enroll in Great Learning's highly-appreciated MIT Data Science and Machine Learning Program, which will help you gain advanced ML skills in demand in industries. Complete the course to earn a certificate of course completion.

Is it worth learning Supervised Machine Learning?

Absolutely, it is worth learning Supervised Machine Learning. It is one of the most widely utilized types of machine learning and forms the basis for many real-world applications. Understanding supervised learning provides a solid foundation for other advanced machine learning concepts.
 

Why is Supervised Machine Learning so popular?

Supervised machine learning is popular due to its effectiveness and wide range of applications. It's a machine learning technique that uses labeled data for training a model. Due to its ability to solve real-world problems across a variety of domains, it has gained popularity. Many supervised learning algorithms are both efficient and interpretable, making them easy to implement and understand. This combination of effectiveness, applicability, and accessibility contributes to the popularity of supervised machine learning.

Will I get certificates after completing these free Supervised Machine Learning courses?

You will be awarded free Supervised Machine Learning certificates after completion of your enrolled Supervised Machine Learning free courses.

What knowledge and skills will I gain upon completing these free Supervised Machine Learning courses?

Upon completing these free Supervised Machine Learning courses, you'll gain an in-depth understanding of the core concepts and practical applications of supervised machine learning. This includes implementing and fine-tuning popular algorithms such as Logistic Regression, Naïve Bayes, and various Tree-Based Models.

How much do these Supervised Machine Learning courses cost?

These Supervised Machine Learning courses are provided by Great Learning Academy for free, allowing any learner to learn crucial concepts for free.

Who are eligible to take these free Supervised Machine Learning courses?

Learners, from freshers to working professionals who wish to learn about supervised machine learning and upskill, can enroll in these courses and earn free Supervised Machine Learning certificates of course completion.

What are the steps to enroll in these free Supervised Machine Learning courses?

Choose the free Supervised Machine Learning courses you are looking for and click on the "Enroll Now" button to start your learning experience.

Why take Supervised Machine Learning courses from Great Learning Academy?

Great Learning Academy is the proactive initiative by Great Learning, the leading e-Learning platform, to offer free industry-relevant courses. Free Supervised Machine Learning courses include courses ranging from beginner to advanced level to help learners choose the best fit for them.

What jobs demand you learn Supervised Machine Learning?

 

Here are some job roles that demand knowledge of Supervised Machine Learning:

1. Data Scientist

2. Machine Learning Engineer

3. AI Engineer

4. Data Analyst

5. Business Intelligence Analyst

6. Risk Analyst

7. Bioinformatics Specialist

8. Quantitative Analyst

9. Computer Vision Engineer

10. NLP Scientist