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Free Supervised Machine Learning Courses

img icon BASICS
Data Preparation for Machine Learning
star   4.48 7.4K+ learners 1 hr

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

img icon BASICS
Python for Machine Learning
star   4.51 472.8K+ 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

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Python Libraries for Machine Learning
star   4.55 10.1K+ learners 2.5 hrs

Skills: Numpy, Pandas, Matplotlib, SeaBorn

img icon BASICS
Basics of Machine Learning
star   4.39 148.7K+ learners 2.5 hrs

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

img icon BASICS
Machine Learning Algorithms
star   4.49 32.4K+ learners 1.5 hrs

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

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Supervised Machine Learning Tutorial
star   4.43 2.3K+ learners 1 hr

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

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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

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

Skills: Logistic Regression on Customer Data

img icon 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

img icon BASICS
Decision Tree
star   4.43 3.6K+ learners 1.5 hrs

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

img icon BASICS
Support Vector Machines
star   4.53 3.2K+ learners 1 hr

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

img icon BASICS
KNN Algorithm
star   4.41 3K+ learners 0.5 hr

Skills: KNN, KNN Demo

free icon BASICS
Data Preparation for Machine Learning
star   4.48 7.4K+ learners 1 hr

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

free icon BASICS
Python for Machine Learning
star   4.51 472.8K+ 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

free icon BASICS
Python Libraries for Machine Learning
star   4.55 10.1K+ learners 2.5 hrs

Skills: Numpy, Pandas, Matplotlib, SeaBorn

free icon BASICS
Basics of Machine Learning
star   4.39 148.7K+ learners 2.5 hrs

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

free icon BASICS
Machine Learning Algorithms
star   4.49 32.4K+ learners 1.5 hrs

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

free icon BASICS
Supervised Machine Learning Tutorial
star   4.43 2.3K+ learners 1 hr

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

free icon BASICS
Supervised Machine Learning with Logistic Regression and Naïve Bayes

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

free icon BASICS
Logistic Regression on Customer Data
star   4.5 3.2K+ learners 1 hr

Skills: Logistic Regression on Customer Data

free icon BASICS
Supervised Machine Learning with Tree Based Models

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

free icon BASICS
Decision Tree
star   4.43 3.6K+ learners 1.5 hrs

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

free icon BASICS
Support Vector Machines
star   4.53 3.2K+ learners 1 hr

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

free icon BASICS
KNN Algorithm
star   4.41 3K+ learners 0.5 hr

Skills: KNN, KNN Demo

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

img 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

img icon BASICS
Supervised Machine Learning Tutorial
star   4.43 2.3K+ learners 1 hr

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

img icon BASICS
KNN Algorithm
star   4.41 3K+ learners 0.5 hr

Skills: KNN, KNN Demo

img 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

img icon BASICS
Support Vector Machines
star   4.53 3.2K+ learners 1 hr

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

img icon BASICS
Random Forest
star   4.36 2.5K+ learners 1 hr

Skills: Random Forest

img icon BASICS
Logistic Regression on Customer Data
star   4.5 3.2K+ learners 1 hr

Skills: Logistic Regression on Customer Data

img icon BASICS
Python for Machine Learning
star   4.51 472.8K+ 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

img icon BASICS
Basics of Machine Learning
star   4.39 148.7K+ learners 2.5 hrs

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

img icon BASICS
Machine Learning Algorithms
star   4.49 32.4K+ learners 1.5 hrs

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

img icon 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

img icon BASICS
Python Libraries for Machine Learning
star   4.55 10.1K+ learners 2.5 hrs

Skills: Numpy, Pandas, Matplotlib, SeaBorn

img icon 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

img icon BASICS
Data Preparation for Machine Learning
star   4.48 7.4K+ learners 1 hr

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

img icon BASICS
Decision Tree
star   4.43 3.6K+ learners 1.5 hrs

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

New

img 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

img icon BASICS
Supervised Machine Learning Tutorial
star   4.43 2.3K+ learners 1 hr

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

img icon BASICS
KNN Algorithm
star   4.41 3K+ learners 0.5 hr

Skills: KNN, KNN Demo

img 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

img icon BASICS
Support Vector Machines
star   4.53 3.2K+ learners 1 hr

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

img icon BASICS
Random Forest
star   4.36 2.5K+ learners 1 hr

Skills: Random Forest

img icon BASICS
Logistic Regression on Customer Data
star   4.5 3.2K+ learners 1 hr

Skills: Logistic Regression on Customer Data

Popular

img icon BASICS
Python for Machine Learning
star   4.51 472.8K+ 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

img icon BASICS
Basics of Machine Learning
star   4.39 148.7K+ learners 2.5 hrs

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

img icon BASICS
Machine Learning Algorithms
star   4.49 32.4K+ learners 1.5 hrs

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

img icon 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

img icon BASICS
Python Libraries for Machine Learning
star   4.55 10.1K+ learners 2.5 hrs

Skills: Numpy, Pandas, Matplotlib, SeaBorn

img icon 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

img icon BASICS
Data Preparation for Machine Learning
star   4.48 7.4K+ learners 1 hr

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

img icon BASICS
Decision Tree
star   4.43 3.6K+ learners 1.5 hrs

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

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

4.0

Country Flag India
“Preparing Data for Machine Learning”
Preparing data for machine learning is a crucial step that involves several key processes to ensure the model performs optimally. First, data collection and loading are essential, often done by gathering data from sources like databases, CSV files, or APIs, and using libraries like pandas to load them. Once data is loaded, data cleaning is critical, as missing values, incorrect data types, and outliers can skew results. Missing values can be filled with statistical measures (like mean or median) or dropped if necessary, while outliers can be identified and managed using methods.
Reviewer Profile

5.0

“i got huge knowlegde on what is machine learning and subjects related to machine learning. this is the best free course that i have found.”
I have gained extensive knowledge about machine learning and its related subjects through this course. It’s the best free resource I’ve found, providing clear explanations, practical insights, and a solid foundation in the field.
Reviewer Profile

5.0

Country Flag India
“Amazing And Informative Program Course”
i really like to the things that impress me and it was such a cute voice , i am really impreessed
Reviewer Profile

5.0

Country Flag India
“Preparing Data for Machine Learning Involves Key Steps”
Preparing data for machine learning involves several key steps. First, gather and clean the data by handling missing values, removing duplicates, and correcting errors. Next, transform the data by normalizing or scaling numerical features to ensure consistency. Categorical variables should be encoded using techniques like one-hot encoding or label encoding. Feature extraction or selection may be necessary to reduce dimensionality and enhance model performance.
Reviewer Profile

5.0

Country Flag India
“Highlight of my Learning Experience in Data Preparation for ML”
The Data Preparation for Machine Learning course was incredibly insightful. I particularly enjoyed learning about data cleaning, handling missing values, and feature engineering. The emphasis on transforming raw data into a format suitable for modeling was eye-opening. I gained practical skills like normalization, encoding categorical variables, and understanding the importance of data quality. This foundational knowledge has been crucial for building accurate and efficient ML models.
Reviewer Profile

5.0

Country Flag India
“Data preparation for machine learning”
Data preparation for Machine Learning is an excellent course that provides clear explanations and practical examples, making complex concepts easy to grasp. It effectively covers essential libraries like NumPy, pandas, and scikit-learn, offering a solid foundation for beginners and enhancing skills for advanced learners. Highly recommended!
Reviewer Profile

5.0

“You have very good curriculum with extraordinarily professor. I am really happy to be a student of your institute.”
Your course is really good with highly qualified tutor and awesome videos
Reviewer Profile
Rabia Altaf

4.0

“Effective Data Preparation for Machine Learning”
I enjoyed learning practical techniques for cleaning and preparing data for ML models.
Reviewer Profile

5.0

“Easy to learn, easy to grasp, and easy to follow”
Easy to understand and grasp the concept. Well organized content.
Reviewer Profile
Muhammad Rashid

5.0

“Good Understanding Achieved with Detailed Lecture”
I gained a thorough understanding of the topic through the detailed and well-structured lecture provided. The comprehensive explanations, real-world examples, and step-by-step approach made the concepts easy to grasp. Additionally, the lecturer's clarity and interactive teaching style helped address any doubts and provided a deeper insight into the subject. The engaging delivery and practical illustrations enhanced my learning experience, ensuring I retained the knowledge effectively and confidently.

Meet your faculty

Meet industry experts who will teach you relevant skills in artificial intelligence

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