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    4.6

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    4.89

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    4.94

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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

FREE
Introduction to Machine Learning
star   4.46 78.2K+ 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
Python for Machine Learning
star   4.51 476.1K+ Learners 2 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
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

FREE
Supervised Machine Learning with Logistic Regression and Naïve Bayes
star   4.43 22K+ Learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

FREE
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
Feature Engineering
star   4.58 3.5K+ 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
Machine Learning Modelling
star   4.62 4.9K+ Learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

FREE
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
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 FREE
Introduction to Machine Learning
star   4.46 78.2K+ 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 FREE
Python for Machine Learning
star   4.51 476.1K+ Learners 2 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 FREE
Supervised Machine Learning with Tree Based Models

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

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

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

free icon FREE
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 FREE
Feature Engineering
star   4.58 3.5K+ 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 FREE
Machine Learning Modelling
star   4.62 4.9K+ Learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

free icon FREE
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 FREE
Bias Variance Tradeoff
star   4.59 1.3K+ Learners 0.5 hr

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

Learner reviews of the Free Model Evaluation Courses

Our learners share their experiences of our courses

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Reviewer Profile

4.0

★★★ ★ ☆
India
“The Course Was Engaging, Well-Structured, and Informative, Offering Valuable Knowledge and Skills”
The course was highly engaging and well-structured, providing a comprehensive understanding of the subject. The instructors were knowledgeable and supportive, making complex topics easy to grasp. Interactive sessions and practical examples enhanced learning, while the resources were both relevant and helpful. Overall, it was a rewarding experience that significantly enriched my knowledge and skills in the field.
Reviewer Profile

5.0

★★★★ ★
Netherlands
“Mastering Machine Learning: A Hands-On Journey from Basics to Advanced Insights”
This course is a comprehensive introduction to machine learning that takes you from the basics to more advanced techniques in a structured, easy-to-follow way. It starts with foundational concepts, such as different types of machine learning (supervised, unsupervised, and reinforcement learning), and explains them clearly.
Reviewer Profile

5.0

★★★★ ★
India
“The Course Offered a Well-Structured Curriculum, Covering Theoretical Concepts and Practical Applications in an Engaging Manner”
The video lectures were clear and concise, and the inclusion of case studies and real-world projects helped bridge the gap between theory and practice. Live sessions with industry experts provided opportunities for insightful discussions, while peer interaction in forums fostered a collaborative environment.
Reviewer Profile

5.0

★★★★ ★
India
“The Course Offered a Well-Structured Curriculum, Covering Theoretical Concepts and Practical Applications in an Engaging Manner”
The video lectures were clear and concise, and the inclusion of case studies and real-world projects helped bridge the gap between theory and practice. Live sessions with industry experts provided opportunities for insightful discussions, while peer interaction in forums fostered a collaborative environment.
Reviewer Profile

5.0

★★★★ ★
India
“Machine Learning Enables Computers to Learn from Data, Identifying Patterns, and Making Decisions Without Explicit Programming”
This beginner-friendly course covers ML fundamentals, including supervised learning (labeled data), unsupervised learning (hidden patterns), and reinforcement learning (decision-making via rewards). You'll learn essential math concepts, Python programming, and practical tools like Jupyter Notebook, NumPy, and scikit-learn to build and evaluate simple ML models.
Reviewer Profile

5.0

★★★★ ★
India
“Good & Efficient to Learn and I Am Very Interested to Study This Course”
The course content was comprehensive, covering essential machine learning concepts such as supervised and unsupervised learning, classification, regression, and clustering. The inclusion of real-world examples and case studies helped to understand the practical applications of ML.
Reviewer Profile

4.0

★★★ ★ ☆
India
“A Well-Structured, Engaging Machine Learning Program Covering Basics, Recommender Systems, and Real Examples”
I thoroughly enjoyed the training and gained a solid foundation in machine learning, covering essentials such as definitions, types, recommender systems, and more. The tutorial was well-structured, aligned with the syllabus, and presented in an easy-to-understand manner with real-life examples that made the session both engaging and highly valuable.
Reviewer Profile

5.0

★★★★ ★
India
“Thrill of Solving Complex Problems with ML Algorithms”
What I enjoyed most about the ML course was diving into the hands-on projects, where theory met practice. Solving real-world problems with powerful algorithms and uncovering hidden patterns in data was incredibly satisfying and eye-opening. It truly highlighted the transformative power of machine learning.
Reviewer Profile

5.0

★★★★ ★
India
“Enhancing Skills with Comprehensive Learning: A Review of Great Learning Courses”
Great Learning generally receives positive feedback for its comprehensive courses, industry-relevant content, and expert instructors. Learners appreciate the hands-on projects and mentorship support, which help in applying concepts practically. However, some users mention that the pace can sometimes feel fast, and access to resources post-course could improve. Overall, it's seen as a strong platform for skill development in data science, AI, and other fields.
Reviewer Profile

5.0

★★★★ ★
India
“I Recently Completed the Machine Learning Course from Great Learning Academy, and I Am Extremely Satisfied with the Overall Experience”
The content quality was excellent, with a good mix of theory and practical examples. The instructors were knowledgeable and presented concepts in a clear, easy-to-understand manner. The hands-on exercises and projects were particularly useful in reinforcing the concepts learned. Additionally, the learning platform was user-friendly and provided ample opportunities for self-paced learning, which allowed me to review concepts as needed. The availability of resources such as discussion forums and support from mentors further enhanced my learning experience.

Meet your faculty

Meet industry experts who will teach you relevant skills in Model Evaluation

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

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.