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    4.6

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    4.89

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University Programs

UNIVERSITY
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Great Learning

12 weeks  • Online

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Walsh College

2 Years  • Online

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MIT Professional Education

14 Weeks  • Online

Learn from MIT Faculty
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Johns Hopkins University

16 weeks  • Online

Free Model Evaluation Courses

BASICS
Introduction to Machine Learning
star   4.46 77.7K+ 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
Python for Machine Learning
star   4.51 473.7K+ 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
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
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
Unsupervised Machine Learning with K-means
star   4.42 11.6K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

BASICS
Hierarchical Clustering
star   4.52 2.2K+ learners 1 hr

Skills: Introduction to Hierarchical Clustering, Agglomerative Hierarchical Clustering, Euclidean Distance, Manhattan Distance, Minkowski Distance, Jaccard Index, Cosine Similarity, Optimal Number of Clusters

BASICS
Feature Engineering
star   4.58 3.4K+ learners 1.5 hrs

Skills: Process of feature engineering, Feature engineering techniques, Correlation matrix, Model performance analysis, Feature engineering demo using a real-life dataset

BASICS
Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

BASICS
Machine Learning Modelling
star   4.62 4.9K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

BASICS
k-fold Cross Validation
star   4.61 1.8K+ learners 1 hr

Skills: Introduction to Cross Validation, Process of Cross Validation, Types of Cross Validation

BASICS
Bias Variance Tradeoff
star   4.59 1.3K+ learners 0.5 hr

Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?

free icon BASICS
Introduction to Machine Learning
star   4.46 77.7K+ 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.

free icon BASICS
Python for Machine Learning
star   4.51 473.7K+ 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
Supervised Machine Learning with Tree Based Models

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

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

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

free icon BASICS
Unsupervised Machine Learning with K-means
star   4.42 11.6K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

free icon BASICS
Hierarchical Clustering
star   4.52 2.2K+ learners 1 hr

Skills: Introduction to Hierarchical Clustering, Agglomerative Hierarchical Clustering, Euclidean Distance, Manhattan Distance, Minkowski Distance, Jaccard Index, Cosine Similarity, Optimal Number of Clusters

free icon BASICS
Feature Engineering
star   4.58 3.4K+ learners 1.5 hrs

Skills: Process of feature engineering, Feature engineering techniques, Correlation matrix, Model performance analysis, Feature engineering demo using a real-life dataset

free icon BASICS
Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

free icon BASICS
Machine Learning Modelling
star   4.62 4.9K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

free icon BASICS
k-fold Cross Validation
star   4.61 1.8K+ learners 1 hr

Skills: Introduction to Cross Validation, Process of Cross Validation, Types of Cross Validation

free icon BASICS
Bias Variance Tradeoff
star   4.59 1.3K+ learners 0.5 hr

Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?

Get started with these courses

BASICS
Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

BASICS
Bias Variance Tradeoff
star   4.59 1.3K+ learners 0.5 hr

Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?

BASICS
k-fold Cross Validation
star   4.61 1.8K+ learners 1 hr

Skills: Introduction to Cross Validation, Process of Cross Validation, Types of Cross Validation

BASICS
Python for Machine Learning
star   4.51 473.7K+ 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
Introduction to Machine Learning
star   4.46 77.7K+ 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
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
Unsupervised Machine Learning with K-means
star   4.42 11.6K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

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
Machine Learning Modelling
star   4.62 4.9K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

BASICS
Feature Engineering
star   4.58 3.4K+ learners 1.5 hrs

Skills: Process of feature engineering, Feature engineering techniques, Correlation matrix, Model performance analysis, Feature engineering demo using a real-life dataset

BASICS
Hierarchical Clustering
star   4.52 2.2K+ learners 1 hr

Skills: Introduction to Hierarchical Clustering, Agglomerative Hierarchical Clustering, Euclidean Distance, Manhattan Distance, Minkowski Distance, Jaccard Index, Cosine Similarity, Optimal Number of Clusters

New

BASICS
Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

BASICS
Bias Variance Tradeoff
star   4.59 1.3K+ learners 0.5 hr

Skills: Bias, Variance, Trade-off, How to avoid overfitting and underfitting?

BASICS
k-fold Cross Validation
star   4.61 1.8K+ learners 1 hr

Skills: Introduction to Cross Validation, Process of Cross Validation, Types of Cross Validation

Popular

BASICS
Python for Machine Learning
star   4.51 473.7K+ 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
Introduction to Machine Learning
star   4.46 77.7K+ 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
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
Unsupervised Machine Learning with K-means
star   4.42 11.6K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

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
Machine Learning Modelling
star   4.62 4.9K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

BASICS
Feature Engineering
star   4.58 3.4K+ learners 1.5 hrs

Skills: Process of feature engineering, Feature engineering techniques, Correlation matrix, Model performance analysis, Feature engineering demo using a real-life dataset

BASICS
Hierarchical Clustering
star   4.52 2.2K+ learners 1 hr

Skills: Introduction to Hierarchical Clustering, Agglomerative Hierarchical Clustering, Euclidean Distance, Manhattan Distance, Minkowski Distance, Jaccard Index, Cosine Similarity, Optimal Number of Clusters

Learner reviews of the Free Model Evaluation Courses

Our learners share their experiences of our courses

4.49
67%
24%
6%
1%
2%
Reviewer Profile

5.0

India
“I completed a free certificate course on Machine Learning from Great Learning, and it was an insightful experience that significantly enhanced my understanding and skills.”
As a free course, it was accessible to anyone interested in learning about machine learning, regardless of their background. The instructors explained complex topics in a simplified manner, making it ideal for beginners. Earning a certificate at the end of the course added value to my learning journey, which I can now showcase in my professional profile. Overall, this course has been an excellent stepping stone into the world of machine learning, and I highly recommend it to anyone looking to start their journey in this exciting field.
Reviewer Profile

5.0

“It was an incredible experience! The content was well-structured, covering all the essential topics in a clear and engaging way.”
The content was well-structured, covering all the essential topics in a clear and engaging way. Each module built upon the last, making complex concepts easy to understand. The instructor was knowledgeable and responsive, providing valuable insights and practical examples that helped deepen my understanding. I also appreciated the flexibility to learn at my own pace and the hands-on assignments, which reinforced the lessons. This course exceeded my expectations and has left me with skills and knowledge. Highly recommended for anyone looking to expand their expertise!
Reviewer Profile

5.0

India
“Introduction to Machine Learning by Great Learning”
The online free course was well-structured and provided valuable insights into the subject matter. The content was clear, relevant, and easy to understand, with practical examples that enhanced the learning experience. The accessibility of the materials, including videos and quizzes, made it convenient to follow along at my own pace. The instructor was knowledgeable and explained concepts effectively, ensuring a good grasp of the topics. Overall, it was an excellent learning opportunity, and I appreciate the effort put into making quality education available for free.
Reviewer Profile

5.0

India
“Overall, this course is excellent for beginners and intermediate learners looking to strengthen their machine learning skills. I highly recommend it to anyone.”
I recently completed the "Machine Learning Fundamentals" course, and it exceeded my expectations in every way. The curriculum is well-structured, covering all the key concepts, from data preprocessing to model evaluation, and introducing essential algorithms like regression, classification, and clustering. The hands-on exercises helped me reinforce my understanding by allowing me to build and test models on real datasets.
Reviewer Profile

5.0

India
“ML Foundations: Understanding Core Machine Learning Concepts”
Machine Learning (ML) is transforming industries by automating decisions, enhancing experiences, and solving complex issues. The "ML Foundations" course introduces core ML concepts, covering topics like supervised/unsupervised learning, classification, regression, clustering, and model evaluation. Through theory and hands-on exercises, participants learn essential techniques using libraries like Scikit-learn, TensorFlow, and Keras—preparing them for further study and real-world applications.
Reviewer Profile

5.0

India
“An Engaging Start to My Machine Learning Journey”
I really enjoyed how the course broke down complex concepts into simple, relatable examples. The hands-on projects were a highlight for me, as they made it easy to apply what I learned, like building models using linear regression and decision trees. The instructors explained everything clearly, and the real-world examples helped me see how machine learning is used in different industries. It was a great balance of theory and practical learning, and I feel much more confident about diving deeper into the field.
Reviewer Profile

5.0

India
“Comprehensive introduction to machine learning with practical examples, hands-on exercises, and real-world applications that enhanced my understanding.”
I really appreciated the course's structured approach and emphasis on practical applications. The use of real-world examples and hands-on exercises made the learning experience engaging and highly relevant. The instructors explained complex concepts in an easy-to-understand manner, which helped build a strong foundation. The inclusion of quizzes and projects allowed me to test my knowledge and gain confidence in applying machine learning techniques. Overall, it was an enriching and well-designed course.
Reviewer Profile

5.0

India
“Exploring The Power of Machine Learning”
The Machine Learning course was a transformative journey, covering core concepts like supervised and unsupervised learning, evaluation metrics, and model optimization. Practical exercises with real-world datasets deepened my understanding of algorithms such as decision trees, SVM, and clustering techniques. The hands-on projects not only enhanced my coding skills but also demonstrated the impactful applications of ML in industries like healthcare, finance, and e-commerce.
Reviewer Profile

4.0

India
“A Life-Changing Learning Experience”
I had an amazing time with this course! The material was explained in a way that was easy to understand, and the practical exercises allowed me to immediately apply what I learned. The course was engaging, and the real-world examples made everything click. It really helped me build my confidence and gain a deeper understanding of machine learning. I highly recommend it to anyone looking to grow their skills in this area.
Reviewer Profile

5.0

India
“Comprehensive and Practical Machine Learning Course”
The Machine Learning course offered by Great Learning is an excellent blend of theoretical concepts and practical applications. The content is well-structured, catering to both beginners and those with some prior knowledge in the field. Hands-on exercises, real-world case studies, and access to knowledgeable mentors ensure a solid understanding of key topics like supervised and unsupervised learning, model evaluation, and more. The platform also provides strong support and learning resources.

Meet your faculty

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

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.