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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BASICS

4.46 77.6K+ 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

4.51 472.6K+ 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

4.41 3K+ learners 0.5 hr

KNN Algorithm

Skills: KNN, KNN Demo

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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 3.4K+ 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 4.8K+ 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 77.6K+ learners 1 hr

BASICS

Python for Machine Learning

4.51 472.6K+ 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

KNN Algorithm

4.41 3K+ learners 0.5 hr

Skills: KNN, KNN Demo

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 3.4K+ 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

4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

BASICS

Machine Learning Modelling

4.62 4.8K+ 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

4.52 2.2K+ learners 1 hr

Hierarchical Clustering

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

4.51 472.6K+ learners 1.5 hrs

Python for Machine Learning

View Course

BASICS

4.46 77.6K+ 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 4.8K+ learners 2.5 hrs

Machine Learning Modelling

Skills: Linear Regression, Logistic Regression, Naïve Bayes

View Course

BASICS

4.58 3.4K+ 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

4.41 3K+ learners 0.5 hr

KNN Algorithm

Skills: KNN, KNN Demo

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

4.52 2.2K+ learners 1 hr

Hierarchical Clustering

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

4.51 472.6K+ learners 1.5 hrs

Python for Machine Learning

View Course

BASICS

4.46 77.6K+ 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 4.8K+ learners 2.5 hrs

Machine Learning Modelling

Skills: Linear Regression, Logistic Regression, Naïve Bayes

View Course

BASICS

4.58 3.4K+ 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

4.41 3K+ learners 0.5 hr

KNN Algorithm

Skills: KNN, KNN Demo

View Course

Learner reviews of the Free Model Evaluation Courses

Our learners share their experiences of our courses

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Luis Escalante

5.0

★★★★ ★

United States

“Advanced Machine Learning: Theories, Algorithms, and Applications in Modern Artificial Intelligence”

This course offers an in-depth exploration of machine learning, focusing on fundamental theories, cutting-edge algorithms, and real-world applications. Students will gain hands-on experience with supervised and unsupervised learning, neural networks, and deep learning models. The course also highlights the role of machine learning in modern AI advancements across industries such as healthcare, finance, and technology. Practical coding exercises, primarily in Python, will reinforce concepts and prepare students for advanced projects and research.

Insaf Ziedi

5.0

★★★★ ★

“Learning Machine Learning (ML) Can Be Both Challenging and Rewarding”

Learning machine learning involves mastering key areas such as mathematics (linear algebra, probability, and statistics), programming (especially Python), and understanding algorithms and data structures. As learners progress, they move on to explore supervised and unsupervised learning techniques, model evaluation metrics, and optimization strategies. Deep learning, with a focus on neural networks and frameworks like TensorFlow or PyTorch, becomes crucial at advanced stages.

ADITYA RAI (RA2211003020496)

4.0

★★★ ★ ☆

India

“Overall, It Was a Valuable Experience That Enhanced My Understanding of Machine Learning”

The machine learning course was excellent. The content was well-structured, covering both theoretical concepts and practical applications effectively. The instructor was knowledgeable and engaging, making complex topics accessible. I appreciated the hands-on projects, which reinforced learning. Overall, it was a valuable experience that enhanced my understanding of machine learning. Highly recommend for anyone looking to deepen their skills!

Vaishnavi Venkatesh

5.0

★★★★ ★

India

“Insightful and Engaging Learning Journey”

The content was well-structured and easy to follow, making complex concepts simple to grasp. The practical examples and real-world applications helped me connect theory to practice. I also appreciated the interactive approach, which kept me engaged throughout the learning process. Overall, it was a great learning experience that effectively expanded my knowledge.

Mohammad Shaikh

4.0

★★★ ★ ☆

United States

“Great Tutorial That Was Easy to Understand”

It was easy to follow, and I had a good time with the instructor. Really good for bare basics.

Thilina Dilshan

5.0

★★★★ ★

United Arab Emirates

“Machine Learning is the Best Choice at Great Learning”

Great choice and way more than expected. Would like to recommend.

Irham SAID

5.0

★★★★ ★

France

“Machine Learning: Supervised and Unsupervised”

I like the way that the professor explains, and it's so easy to follow.

Nirupathunga M

5.0

★★★★ ★

India

“Unsupervised Learning Was So Easy to Understand”

The way the faculty taught was so easy to understand. The depth for the beginner was so easy to grasp.

Shaik Arshiya undefined

5.0

★★★★ ★

India

“Foundations of Machine Learning”

I particularly enjoyed the hands-on projects that allowed me to implement algorithms and see their impact in real-time. The collaborative environment fostered discussions and insights that deepened my understanding of core concepts. Additionally, the instructors’ expertise and support helped clarify complex ideas and motivated me to explore advanced topics in machine learning.

Salma Mulla

5.0

★★★★ ★

India

“Introduction to Machine Learning Was Good”

Great Learning offers a comprehensive range of machine learning courses, from beginner-friendly to advanced levels. Their courses feature a well-structured curriculum, hands-on projects, experienced instructors, and flexible learning options. While they can be costly, the certificates earned can be valuable assets in the job market. Consider exploring their offerings, such as the Introduction to Machine Learning, Deep Learning, and Machine Learning with Python courses, to find the best fit for your learning goals.

Meet your faculty

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

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30+ years of experience in data science, ML, and analytics.
◆
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.

Get started with these courses

Learner reviews of the Free Model Evaluation Courses

Meet your faculty

Dr. Abhinanda Sarkar

Mr. Bharani Akella

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