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 76.7K+ 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 468K+ 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 9.8K+ 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 21.7K+ 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 11.5K+ learners 1.5 hrs

Unsupervised Machine Learning with K-means

Skills: Unsupervised Learning,Clustering, k-means Clustering

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BASICS

4.52 2.1K+ 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 3.3K+ 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 1.2K+ 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 76.7K+ learners 1 hr

BASICS

Python for Machine Learning

4.51 468K+ learners 1.5 hrs

BASICS

Supervised Machine Learning with Tree Based Models

4.56 9.8K+ 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 21.7K+ 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 11.5K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

BASICS

Hierarchical Clustering

4.52 2.1K+ learners 1 hr

BASICS

Feature Engineering

4.58 3.3K+ 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 1.2K+ learners 0.5 hr

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

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Get started with these courses

BASICS

4.54 1.6K+ learners 1 hr

Feature Engineering Importance

Skills: Feature Engineering, Feature Selection

View Course

BASICS

4.59 1.2K+ learners 0.5 hr

Bias Variance Tradeoff

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

View Course

BASICS

4.52 2.1K+ 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 468K+ learners 1.5 hrs

Python for Machine Learning

View Course

BASICS

4.46 76.7K+ learners 1 hr

Introduction to Machine Learning

View Course

BASICS

4.43 21.7K+ 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 11.5K+ learners 1.5 hrs

Unsupervised Machine Learning with K-means

Skills: Unsupervised Learning,Clustering, k-means Clustering

View Course

BASICS

4.56 9.8K+ 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 3.3K+ 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 1.2K+ learners 0.5 hr

Bias Variance Tradeoff

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

View Course

BASICS

4.52 2.1K+ 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 468K+ learners 1.5 hrs

Python for Machine Learning

View Course

BASICS

4.46 76.7K+ learners 1 hr

Introduction to Machine Learning

View Course

BASICS

4.43 21.7K+ 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 11.5K+ learners 1.5 hrs

Unsupervised Machine Learning with K-means

Skills: Unsupervised Learning,Clustering, k-means Clustering

View Course

BASICS

4.56 9.8K+ 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 3.3K+ 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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Sravan Vemula

5.0

★★★★ ★

India

“Mastering Machine Learning for Beginners”

This course provides a solid foundation in Machine Learning, covering key concepts like supervised and unsupervised learning, recommendation systems, and practical applications. It simplifies complex topics with clear examples and quizzes, making it ideal for beginners. A hands-on approach ensures learners can apply knowledge effectively in real-world scenarios.

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EVANI V S S LALITHA .

5.0

★★★★ ★

India

“Insightful and Practical Learning Journey”

I really enjoyed the structure of the course and the way complex topics like machine learning were broken down into simple, understandable lessons. The interactive quizzes reinforced my understanding, and the practical approach kept me engaged throughout. This course gave me both foundational knowledge and practical insights, preparing me well for real-world applications. I highly recommend it to anyone looking to get started with machine learning!

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Pratiksha Pandit

5.0

★★★★ ★

India

“The Teacher Was Very Knowledgeable, Engaging, and Supportive”

The teacher was very knowledgeable, engaging, and supportive, making the learning experience both enjoyable and informative.

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Jian Malik Hidayat

5.0

★★★★ ★

Indonesia

“Understand Advanced Concepts with Real Examples”

This assignment really helped me understand the basic and advanced concepts of the topics studied. The explanations are clear, and the examples are relevant to real-world situations, making the material easier to digest. I feel more confident to apply this knowledge in future projects. Highly recommended for those who want to deepen their understanding.

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Faizan Madad

5.0

★★★★ ★

“Amazing Teacher - Great Teaching Style”

I really loved how simple he made it all sound. I have no previous experience in machine learning, and the teacher's choice of words and teaching method is really amazing.

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ANANT K.R Singh

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.

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Suvedha Balachandran

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.

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Rajeswari p

5.0

★★★★ ★

India

“Great Learning on 'Introduction to Machine Learning'”

It was really very good, with clear explanations about supervised and unsupervised learning, the Netflix prize, and ML on the cloud.

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Byron Ogembo

5.0

★★★★ ★

“Comprehensive and Engaging Learning Experience”

Great Learning offers a comprehensive and engaging learning experience. The well-structured courses, expert instructors, and interactive learning materials make complex topics accessible. The platform's flexibility allows for self-paced learning, while the supportive community fosters collaboration and knowledge sharing. The hands-on projects and real-world case studies enhance practical application. Overall, Great Learning provides a valuable opportunity for professional development and skill enhancement.

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Cynthia Aguonye

5.0

★★★★ ★

Nigeria

“Highlight of Your Learning Experience”

I loved how the course combined videos, quizzes, and hands-on activities to keep things interesting and cater to different learning styles. It was a comprehensive experience that not only increased my knowledge but also built my confidence in applying these skills.

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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.
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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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