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    4.6

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    4.89

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Free Model Evaluation Courses

img icon BASICS
Python for Machine Learning
star   4.51 470K+ learners 1.5 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

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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

img icon BASICS
Supervised Machine Learning with Logistic Regression and Naïve Bayes
star   4.43 21.8K+ learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

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KNN Algorithm
star   4.41 3K+ learners 0.5 hr

Skills: KNN, KNN Demo

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Unsupervised Machine Learning with K-means
star   4.42 11.5K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

img icon BASICS
Hierarchical Clustering
star   4.52 2.1K+ 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

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Feature Engineering
star   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

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Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

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Machine Learning Modelling
star   4.62 4.8K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

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Machine Learning Projects
star   4.5 3.2K+ learners 1.5 hrs

Skills: Exploratory Data Analysis, Machine Learning Algorithms

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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

img icon BASICS
Bias Variance Tradeoff
star   4.59 1.2K+ learners 0.5 hr

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

free icon BASICS
Python for Machine Learning
star   4.51 470K+ learners 1.5 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 BASICS
Supervised Machine Learning with Tree Based Models

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

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

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

free icon BASICS
KNN Algorithm
star   4.41 3K+ learners 0.5 hr

Skills: KNN, KNN Demo

free icon BASICS
Unsupervised Machine Learning with K-means
star   4.42 11.5K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

free icon BASICS
Hierarchical Clustering
star   4.52 2.1K+ 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 BASICS
Feature Engineering
star   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

free icon BASICS
Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

free icon BASICS
Machine Learning Modelling
star   4.62 4.8K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

free icon BASICS
Machine Learning Projects
star   4.5 3.2K+ learners 1.5 hrs

Skills: Exploratory Data Analysis, Machine Learning Algorithms

free icon BASICS
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 BASICS
Bias Variance Tradeoff
star   4.59 1.2K+ learners 0.5 hr

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

Get started with these courses

img icon BASICS
Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

img icon BASICS
Bias Variance Tradeoff
star   4.59 1.2K+ learners 0.5 hr

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

img icon BASICS
Hierarchical Clustering
star   4.52 2.1K+ 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

img icon BASICS
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

img icon BASICS
Python for Machine Learning
star   4.51 470K+ learners 1.5 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

img icon BASICS
Supervised Machine Learning with Logistic Regression and Naïve Bayes
star   4.43 21.8K+ learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

img icon BASICS
Unsupervised Machine Learning with K-means
star   4.42 11.5K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

img icon BASICS
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

img icon BASICS
Machine Learning Modelling
star   4.62 4.8K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

img icon BASICS
Feature Engineering
star   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

img icon BASICS
Machine Learning Projects
star   4.5 3.2K+ learners 1.5 hrs

Skills: Exploratory Data Analysis, Machine Learning Algorithms

img icon BASICS
KNN Algorithm
star   4.41 3K+ learners 0.5 hr

Skills: KNN, KNN Demo

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img icon BASICS
Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

img icon BASICS
Bias Variance Tradeoff
star   4.59 1.2K+ learners 0.5 hr

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

img icon BASICS
Hierarchical Clustering
star   4.52 2.1K+ 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

img icon BASICS
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

Popular

img icon BASICS
Python for Machine Learning
star   4.51 470K+ learners 1.5 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

img icon BASICS
Supervised Machine Learning with Logistic Regression and Naïve Bayes
star   4.43 21.8K+ learners 2 hrs

Skills: Scikit Learn Library,Logistic Regression, Naïve Bayes

img icon BASICS
Unsupervised Machine Learning with K-means
star   4.42 11.5K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

img icon BASICS
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

img icon BASICS
Machine Learning Modelling
star   4.62 4.8K+ learners 2.5 hrs

Skills: Linear Regression, Logistic Regression, Naïve Bayes

img icon BASICS
Feature Engineering
star   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

img icon BASICS
Machine Learning Projects
star   4.5 3.2K+ learners 1.5 hrs

Skills: Exploratory Data Analysis, Machine Learning Algorithms

img icon BASICS
KNN Algorithm
star   4.41 3K+ learners 0.5 hr

Skills: KNN, KNN Demo

Learner reviews of the Free Model Evaluation Courses

Our learners share their experiences of our courses

4.5
68%
24%
5%
1%
2%
Reviewer Profile

5.0

“The Python Course Was Well-Structured and Informative, Offering Clear Explanations That Enhanced My Programming Skills Effectively”
The Python course exceeded my expectations with its comprehensive content and engaging format. The instructor provided clear explanations and practical examples, which made complex concepts easy to understand. The hands-on exercises reinforced my learning, allowing me to apply my skills effectively. Overall, it was an enriching experience that significantly boosted my programming confidence.
Reviewer Profile

5.0

Country Flag India
“An Engaging Introduction to Python for Machine Learning”
This course provided a fantastic introduction to machine learning concepts through Python, blending theory with practical exercises that reinforced learning at every step. I appreciated how complex ideas were broken down into understandable parts, with ample real-world examples and projects to apply what was taught. The step-by-step approach to data preprocessing, model building, and evaluation made it easy to follow along and grasp each concept. Overall, it's an excellent resource for anyone looking to enter the field of machine learning with a solid Python base.
Reviewer Profile

5.0

Country Flag India
“Python for Machine Learning by Great Learning”
The "Python for Machine Learning" course at Great Learning offers an excellent foundation for anyone looking to explore the world of machine learning. The course provides a deep dive into Python programming, specifically tailored to the needs of machine learning applications. It covers key concepts such as data preprocessing, model building, and evaluating machine learning algorithms, using libraries like Pandas and NumPy. The course structure is well-organized, making it ideal for both beginners and those with some prior experience in Python.
Reviewer Profile

5.0

Country Flag India
“The Course Was Well-Structured and Easy to Follow. The Instructor Was Knowledgeable and Engaging.”
The course was well-structured and easy to follow. The instructor was knowledgeable and engaging. The course material was relevant and up-to-date. The assignments were challenging but fair. The discussions were insightful and thought-provoking.
Reviewer Profile
Bismillah Abbasi

5.0

“The Python Course Was an Incredibly Rewarding Learning Experience. It Provided a Strong Foundation in Programming Concepts and Allowed Me to Gain Experience with Real-World Applications”
The Python course was an incredibly rewarding learning experience. It provided a strong foundation in programming concepts and allowed me to gain hands-on experience with real-world applications. I started by learning the basics—variables, data types, loops, and conditionals—before progressing to more advanced topics like object-oriented programming, file handling, and libraries like NumPy and Pandas for data analysis.
Reviewer Profile
Asia Siddiqui

5.0

“The Curriculum is Easy to Follow and Quizzes are Designed in a Good Way”
I really appreciated how well-organized the curriculum was. The clear progression of topics made it easy to understand each concept before moving on to the next. Additionally, the practical examples helped reinforce the material, making it more engaging and relatable. The resources provided were also very helpful in deepening my understanding. Overall, it was a great learning experience!
Reviewer Profile

5.0

Country Flag Indonesia
“Instructor, Topic Depth, Easy to Follow”
The instructor demonstrated a strong understanding of the topic, presenting complex concepts in a clear and engaging manner. The depth of coverage allowed for a thorough exploration of the subject, while the pacing was well-suited for learners at various levels. The use of practical examples and visual aids made the material easy to follow, enhancing comprehension. Overall, the instructor's ability to break down intricate ideas into digestible segments contributed significantly to a positive learning experience.
Reviewer Profile

5.0

“Engaging and Comprehensive Learning”
I really enjoyed the quizzes and assignments as they provided a practical and hands-on approach to the learning material. The content was easy to follow, and the examples used were relevant and clear. It was particularly helpful to have structured feedback on my answers, which allowed me to understand the concepts better. The interactive format kept me engaged throughout the learning process.
Reviewer Profile

5.0

“Engaging and Informative Learning Experience”
The course provided a clear and structured approach to learning Python concepts, with well-designed quizzes and assignments that reinforced the material effectively. I particularly appreciated the practical examples and the focus on real-world applications. However, more detailed explanations in some sections would enhance the experience further. Overall, it was an excellent course for beginners and intermediate learners.
Reviewer Profile

5.0

Country Flag United States
“The Python for Machine Learning Certification is an Excellent Resource for Anyone Looking to Break into the Field of Machine Learning with a Strong Foundation in Python”
This certification strikes a great balance between theory and application, making it ideal for students, professionals, and anyone seeking to understand machine learning concepts through Python. While there’s room for improvement, the course delivers significant value and is a worthy investment in building foundational skills for a career in AI or data science.

Meet your faculty

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

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.