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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FREE

4.51 459.8K+ learners

Python for Machine Learning

1.5 hrs

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Supervised Machine Learning with Tree Based Models

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Supervised Machine Learning with Tree Based Models

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Supervised Machine Learning with Logistic Regression and Naïve Bayes

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Supervised Machine Learning with Logistic Regression and Naïve Bayes

2 hrs

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KNN Algorithm

0.5 hr

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Unsupervised Machine Learning with K-means

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Unsupervised Machine Learning with K-means

1.5 hrs

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Hierarchical Clustering

1 hr

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Feature Engineering

1.5 hrs

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Feature Engineering Importance

1 hr

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Machine Learning Modelling

2.5 hrs

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FREE

Python for Machine Learning

4.51 459.8K+ learners 1.5 hrs

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Supervised Machine Learning with Tree Based Models

4.56 9.7K+ learners 2 hrs

FREE

Supervised Machine Learning with Logistic Regression and Naïve Bayes

4.43 21.3K+ learners 2 hrs

FREE

KNN Algorithm

4.41 2.9K+ learners 0.5 hr

FREE

Unsupervised Machine Learning with K-means

4.42 11.4K+ learners 1.5 hrs

FREE

Hierarchical Clustering

4.52 2.1K+ learners 1 hr

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Feature Engineering

4.58 3.1K+ learners 1.5 hrs

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Feature Engineering Importance

4.54 1.5K+ learners 1 hr

FREE

Machine Learning Modelling

4.62 4.6K+ learners 2.5 hrs

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Feature Engineering Importance

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k-fold Cross Validation

1 hr

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4.51 459.8K+ learners

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1.5 hrs

View Course

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Supervised Machine Learning with Logistic Regression and Naïve Bayes

2 hrs

View Course

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4.42 11.4K+ learners

Unsupervised Machine Learning with K-means

1.5 hrs

View Course

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Supervised Machine Learning with Tree Based Models

2 hrs

View Course

Learner reviews of the Free Model Evaluation Courses

Our learners share their experiences of our courses

4.5

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Irsa Imtiaz

5.0

★★★★ ★

“My Experience About This Course Was Great”

I really enjoyed the course, and the course flow is very easy to understand. I love having this course. The learning experience is really great.

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Abishek SK (RA2111003040136)

4.0

★★★ ★ ☆

“The Hands-On Projects and Real-World Datasets Provided Excellent Practical Experience”

This course was a great introduction to Python for Machine Learning. I really enjoyed the clear explanations of core concepts like supervised learning, unsupervised learning, and deep learning. The hands-on labs with libraries like TensorFlow, Keras, and Scikit-learn helped solidify my understanding. I also appreciated the focus on real-world applications and the opportunities to work on projects that applied these techniques to real datasets. Overall, it's a great course for anyone looking to build a strong foundation in machine learning with Python.

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Rakesh Battina

5.0

★★★★ ★

“Great Learning: Bridging Knowledge and Industry Skills”

Great Learning offers exceptional courses on NumPy, Pandas, and introductory AI and machine learning. The classes are well-structured, providing a solid foundation in data manipulation and analysis. Instructors are highly knowledgeable and offer real-world insights, making complex topics easy to understand. The hands-on projects reinforce learning and prepare students for industry challenges. This platform is perfect for anyone looking to enhance their skills and advance in the field of data science and AI. Highly recommended!

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Elham Ebrahimikiadehi

5.0

★★★★ ★

“Quality and Usability of the Course”

It was an easy-to-use and applicable course. My data analysis journey started with your courses.

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Madhu Yadav

4.0

★★★ ★ ☆

“Python for Machine Learning (NumPy and Pandas)”

The "Python for Machine Learning" course at Great Learning offers a thorough introduction to key machine learning concepts and practical applications. The course content is well-structured, covering essential topics such as data preprocessing, feature engineering, and model evaluation. The hands-on projects and coding exercises provide valuable practical experience, reinforcing theoretical knowledge. The instructor's clear explanations and real-world examples greatly enhance the learning experience.

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Panagiota Grosdouli

4.0

★★★ ★ ☆

“Really Enjoyed the Lesson. It Was Easy to Follow and Well-Structured”

I really enjoyed the lesson. It was easy to follow and well-structured, making the concepts clear and understandable. The explanations were concise, and the examples helped reinforce the material. Overall, it was an engaging and informative session that kept me interested throughout.

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Praju K K

5.0

★★★★ ★

“Highlight of Your Learning Experience: I Enjoyed the Hands-On Projects and Collaborative Discussions”

I thoroughly enjoyed the hands-on projects that allowed me to apply theoretical concepts in practical scenarios. Collaborative discussions with peers enhanced my understanding and provided diverse perspectives, making the learning process more engaging and enjoyable.

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Mouli Gunisetti

5.0

★★★★ ★

“Comprehensive Introduction to Pandas and NumPy in Machine Learning”

I recently completed the Python for Machine Learning course with Great Learning, and I found it very informative and practical. The introduction to pandas and NumPy was particularly helpful, and the exercises gave me a solid hands-on experience with these libraries. The instructors were clear and made the concepts easy to understand. However, I would have appreciated more complex real-world examples in the exercises. Overall, it was a great learning experience!

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