Model Evaluation Courses

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Model evaluation is crucial in predictive modeling to assess performance and accuracy. It helps determine how well a model generalizes to new data and captures underlying patterns, guiding informed decision-making and iterative improvements.

Free courses on model evaluation teach you to use evaluation metrics, apply cross-validation techniques, and make decisions based on performance measures. These skills are essential for identifying model weaknesses, improving performance, and selecting the best predictive models. Mastering model evaluation ensures reliable, accurate models for data-driven insights and effective decision-making.

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UNIVERSITY

Great Learning

Generative AI for Practitioners

12 weeks • Online

UNIVERSITY

Walsh College

MS in Artificial Intelligence & Machine Learning

2 Years • Online

UNIVERSITY

MIT IDSS

AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact

12 weeks • Online

UNIVERSITY

MIT Professional Education

No Code and Agentic AI

14 Weeks • Online

Learn from MIT Faculty

UNIVERSITY

Johns Hopkins University

Applied Generative AI and Agentic AI

16 weeks • Online

BASICS

4.46 77.9K+ Learners 1 hr

Introduction to Machine Learning

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.

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BASICS

4.51 474.6K+ Learners 1.5 hrs

Python for Machine Learning

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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BASICS

Supervised Machine Learning with Tree Based Models

4.56 9.9K+ Learners 2 hrs

Supervised Machine Learning with Tree Based Models

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

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BASICS

Supervised Machine Learning with Logistic Regression and Naïve Bayes

4.43 21.9K+ Learners 2 hrs

Supervised Machine Learning with Logistic Regression and Naïve Bayes

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

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BASICS

Unsupervised Machine Learning with K-means

4.42 11.6K+ Learners 1.5 hrs

Unsupervised Machine Learning with K-means

Skills: Unsupervised Learning,Clustering, k-means Clustering

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BASICS

4.52 2.2K+ Learners 1 hr

Hierarchical Clustering

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

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BASICS

4.58 3.5K+ Learners 1.5 hrs

Feature Engineering

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

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BASICS

4.54 1.6K+ Learners 1 hr

Feature Engineering Importance

Skills: Feature Engineering, Feature Selection

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BASICS

4.62 4.9K+ Learners 2.5 hrs

Machine Learning Modelling

Skills: Linear Regression, Logistic Regression, Naïve Bayes

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BASICS

4.61 1.8K+ Learners 1 hr

k-fold Cross Validation

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

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BASICS

4.59 1.3K+ Learners 0.5 hr

Bias Variance Tradeoff

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

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BASICS

Introduction to Machine Learning

4.46 77.9K+ learners 1 hr

BASICS

Python for Machine Learning

4.51 474.6K+ learners 1.5 hrs

BASICS

Supervised Machine Learning with Tree Based Models

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

4.43 21.9K+ learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

BASICS

Unsupervised Machine Learning with K-means

4.42 11.6K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

BASICS

Hierarchical Clustering

4.52 2.2K+ learners 1 hr

BASICS

Feature Engineering

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

BASICS

Feature Engineering Importance

4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

BASICS

Machine Learning Modelling

4.62 4.9K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

BASICS

k-fold Cross Validation

4.61 1.8K+ learners 1 hr

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

BASICS

Bias Variance Tradeoff

4.59 1.3K+ learners 0.5 hr

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

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BASICS

4.54 1.6K+ Learners 1 hr

Feature Engineering Importance

Skills: Feature Engineering, Feature Selection

View Course

BASICS

4.59 1.3K+ Learners 0.5 hr

Bias Variance Tradeoff

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

View Course

BASICS

4.61 1.8K+ Learners 1 hr

k-fold Cross Validation

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

View Course

BASICS

4.51 474.6K+ Learners 1.5 hrs

Python for Machine Learning

View Course

BASICS

4.46 77.9K+ Learners 1 hr

Introduction to Machine Learning

View Course

BASICS

4.43 21.9K+ Learners 2 hrs

Supervised Machine Learning with Logistic Regression and Naïve Bayes

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

View Course

BASICS

4.42 11.6K+ Learners 1.5 hrs

Unsupervised Machine Learning with K-means

Skills: Unsupervised Learning,Clustering, k-means Clustering

View Course

BASICS

4.56 9.9K+ Learners 2 hrs

Supervised Machine Learning with Tree Based Models

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

View Course

BASICS

4.62 4.9K+ Learners 2.5 hrs

Machine Learning Modelling

Skills: Linear Regression, Logistic Regression, Naïve Bayes

View Course

BASICS

4.58 3.5K+ Learners 1.5 hrs

Feature Engineering

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

View Course

BASICS

4.52 2.2K+ Learners 1 hr

Hierarchical Clustering

View Course

New

BASICS

4.54 1.6K+ Learners 1 hr

Feature Engineering Importance

Skills: Feature Engineering, Feature Selection

View Course

BASICS

4.59 1.3K+ Learners 0.5 hr

Bias Variance Tradeoff

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

View Course

BASICS

4.61 1.8K+ Learners 1 hr

k-fold Cross Validation

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

View Course

Popular

BASICS

4.51 474.6K+ Learners 1.5 hrs

Python for Machine Learning

View Course

BASICS

4.46 77.9K+ Learners 1 hr

Introduction to Machine Learning

View Course

BASICS

4.43 21.9K+ Learners 2 hrs

Supervised Machine Learning with Logistic Regression and Naïve Bayes

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

View Course

BASICS

4.42 11.6K+ Learners 1.5 hrs

Unsupervised Machine Learning with K-means

Skills: Unsupervised Learning,Clustering, k-means Clustering

View Course

BASICS

4.56 9.9K+ Learners 2 hrs

Supervised Machine Learning with Tree Based Models

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

View Course

BASICS

4.62 4.9K+ Learners 2.5 hrs

Machine Learning Modelling

Skills: Linear Regression, Logistic Regression, Naïve Bayes

View Course

BASICS

4.58 3.5K+ Learners 1.5 hrs

Feature Engineering

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

View Course

BASICS

4.52 2.2K+ Learners 1 hr

Hierarchical Clustering

View Course

Learner reviews of the Free Model Evaluation Courses

Our learners share their experiences of our courses

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Anil Chandra

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.

Sayali Ubale

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.

PRAVEENA VYTLA

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.

22B01A4656 CS

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.

22B01A05G4 CSE

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!

Nanditha hegde

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.

nitin 22bca025

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.

NAMDEV 109

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.

22B01A05D0 CSE

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.

22B01A05D6 CSE

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 artificial intelligence

◆
30+ years of experience in data science, ML, and analytics.
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Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.

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.

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Learner reviews of the Free Model Evaluation Courses

Meet your faculty

Dr. Abhinanda Sarkar

Mr. Bharani Akella

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