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University & Pro Programs

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Master Artificial Intelligence
18 coding exercises 3 projects
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Master Data Science & Machine Learning in Python
136 coding exercises 6 projects
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Master Python programming
51 coding exercises 3 projects
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Machine Learning Essentials with Python
1 coding exercise 1 project

Free Machine Learning Courses

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Machine Learning Pipeline
star   4.53 4.5K+ learners 2 hrs

Skills: Introduction to Machine Learning, Understanding the ML Pipeline, Data Preparation, Formatting Data, Data Transformation, Building ML models, Analyzing ML models

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Introduction to XGBoost
star   4.66 712 learners 1.5 hrs

Skills: Python skills, Basic ML concepts

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Feature Engineering Importance
star   4.54 1.6K+ learners 1 hr

Skills: Feature Engineering, Feature Selection

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Unsupervised Machine Learning with K-means
star   4.42 11.6K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

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Market Basket Analysis
star   4.3 2.8K+ learners 2 hrs

Skills: Market Basket Analysis

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Predictive Analytics for Machine Learning
602 learners 2 hrs

Skills: Predictive Analytics, Feature Engineering, Deep Feature Synthesis, Model Selection Techniques

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Random Forest
star   4.36 2.5K+ learners 1 hr

Skills: Random Forest

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Outlier Detection
star   4.44 2K+ learners 1.5 hrs

Skills: Outlier Detection, Handling missing values and outliers, Data Visualization

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Bagging and Boosting
star   4.62 2K+ learners 1 hr

Skills: Working with Prediction Errors, Understanding Ensemble Methods, Introduction to Bagging and Boosting, Bagging vs Boosting, Practical Demo in Python

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Importance of Statistics in Machine Learning
star   4.46 1.7K+ learners 1 hr

Skills: Big Data, Statistics and Measures of Central Tendency

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Python IDEs for Machine Learning
1.4K+ learners 2.5 hrs

Skills: Introduction to Python IDEs, Spyder, Google Colab, Jupyter Notebook

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COVID-19 Outbreak Prediction
star   4.38 10K+ learners 1 hr

Skills: Random Forest Regressor, EDA, Seaborn, Pandas

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Machine Learning Pipeline
star   4.53 4.5K+ learners 2 hrs

Skills: Introduction to Machine Learning, Understanding the ML Pipeline, Data Preparation, Formatting Data, Data Transformation, Building ML models, Analyzing ML models

free icon BASICS
Introduction to XGBoost
star   4.66 712 learners 1.5 hrs

Skills: Python skills, Basic ML concepts

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

Skills: Feature Engineering, Feature Selection

free icon BASICS
Unsupervised Machine Learning with K-means
star   4.42 11.6K+ learners 1.5 hrs

Skills: Unsupervised Learning,Clustering, k-means Clustering

free icon BASICS
Market Basket Analysis
star   4.3 2.8K+ learners 2 hrs

Skills: Market Basket Analysis

free icon BASICS
Predictive Analytics for Machine Learning
star   3.83 602 learners 2 hrs

Skills: Predictive Analytics, Feature Engineering, Deep Feature Synthesis, Model Selection Techniques

free icon BASICS
Random Forest
star   4.36 2.5K+ learners 1 hr

Skills: Random Forest

free icon BASICS
Outlier Detection
star   4.44 2K+ learners 1.5 hrs

Skills: Outlier Detection, Handling missing values and outliers, Data Visualization

free icon BASICS
Bagging and Boosting
star   4.62 2K+ learners 1 hr

Skills: Working with Prediction Errors, Understanding Ensemble Methods, Introduction to Bagging and Boosting, Bagging vs Boosting, Practical Demo in Python

free icon BASICS
Importance of Statistics in Machine Learning
star   4.46 1.7K+ learners 1 hr

Skills: Big Data, Statistics and Measures of Central Tendency

free icon BASICS
Python IDEs for Machine Learning
star   4.5 1.4K+ learners 2.5 hrs

Skills: Introduction to Python IDEs, Spyder, Google Colab, Jupyter Notebook

free icon BASICS
COVID-19 Outbreak Prediction
star   4.38 10K+ learners 1 hr

Skills: Random Forest Regressor, EDA, Seaborn, Pandas

Learn Machine Learning for Free

These free machine learning courses online give you a practical learning path from data preparation to model building with Python. You learn how to prevent data leakage, balance datasets, and use k-fold cross-validation, then build strong fundamentals with NumPy arrays and operations, Pandas dataframes, and core data manipulation. You also strengthen EDA and visualization skills using Matplotlib, Seaborn, Plotly, SciPy, and scikit learn, backed by statistics and probability for better evaluation.

Starting with data preparation, you will learn data leakage checks, data balancing, and k-fold cross-validation, then use NumPy and Pandas for data manipulation and exploration. You will build a strong foundation in statistics and probability, then train models such as linear regression, logistic regression, Naive Bayes, decision trees, random forests, SVMs, and k-means clustering using scikit learn. You will also learn visualization with Matplotlib, Seaborn, and Plotly, work with SciPy tools, complete prediction and EDA projects, and deploy a model using Flask, building skills that prepare you for neural networks, natural language processing, and TensorFlow workflows.

Skills You’ll Gain in These Best Free Machine Learning Courses 

  • Machine Learning Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines.

  • Programming and Libraries: Python, NumPy, Pandas, scikit learn, and TensorFlow.

  • Modeling and Evaluation: Data preprocessing, Model training, Model validation, and Performance evaluation metrics.

  • Project and Delivery Skills: Build and test machine learning models, Iterate and improve model performance.

  • Core Foundations: Neural networks fundamentals, and Natural language processing fundamentals.
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Get started with these courses

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Basics of Unsupervised Machine Learning
star   4.2 1.3K+ learners 7 hrs

Skills: Basics of Python

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Introduction to XGBoost
star   4.66 712 learners 1.5 hrs

Skills: Python skills, Basic ML concepts

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Artificial Intelligence and Machine Learning Projects
star   4.44 4.2K+ learners 1.5 hrs

Skills: Machine Learning Algorithms

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Predictive Analytics for Machine Learning
602 learners 2 hrs

Skills: Predictive Analytics, Feature Engineering, Deep Feature Synthesis, Model Selection Techniques

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Python IDEs for Machine Learning
1.4K+ learners 2.5 hrs

Skills: Introduction to Python IDEs, Spyder, Google Colab, Jupyter Notebook

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Cross Validation in Machine Learning
252 learners 1.5 hrs

Skills: Cross-validation, K-fold CV and LOOCV, ROC-AUC curve

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Feature Engineering for Machine Learning
star   4.46 896 learners 1 hr

Skills: Introduction to Feature Engineering, Under sampling, Over sampling

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Applications of Data Science & Machine Learning
star   4.65 1.2K+ learners 1 hr

Skills: Statistical analysis, Deep Learning, how to work and process large and unstructured data sets, and Data Visualization and among others.

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Python for Machine Learning and Data Science
star   4.65 10K+ learners 3 hrs

Skills: Introduction to NumPy, Pandas and Data Visualization in Python

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LDA in Entertainment Industry
star   4.64 1K+ learners 1 hr

Skills: Application of LDA, Building Pipelines, Data Balancing, Data Validation

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Machine Learning Landscape
star   4.63 3.9K+ learners 1.5 hrs

Skills: Machine Learning Landscape

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Bagging and Boosting
star   4.62 2K+ learners 1 hr

Skills: Working with Prediction Errors, Understanding Ensemble Methods, Introduction to Bagging and Boosting, Bagging vs Boosting, Practical Demo in Python

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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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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

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Introduction to Supervised Learning
star   4.61 2.2K+ learners 1 hr

Skills: Machine Learning, Supervised Learning

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Bias Variance Tradeoff
star   4.59 1.3K+ learners 0.5 hr

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

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Python for Machine Learning
star   4.51 471.9K+ 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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Basics of Machine Learning
star   4.39 148.3K+ learners 2.5 hrs

Skills: Introduction to Machine Learning, Supervised Machine Learning, Linear Regression, Pearson's Coefficient, Coefficient of Determinant

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Machine Learning with Python
star   4.57 96.5K+ learners 11 hrs

Skills: Python, Statistics, Reinforcement learning, Machine learning

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Introduction to Machine Learning
star   4.46 77.5K+ learners 1 hr

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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Statistics for Machine Learning
star   4.58 43.7K+ learners 2 hrs

Skills: Descriptive Statistics, Measures of Dispersion Range and IQR,,Central Tendency and 3 Ms,The Empirical Rule and Chebyshev Rule,Correlation Analysis

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Machine Learning Algorithms
star   4.49 32.3K+ learners 1.5 hrs

Skills: Classification (Logistic Regression, Decision Trees, SVM), Regression (Linear, Ridge, Lasso), Clustering (K-means, Hierarchical), model evaluation, cross validation

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Regression Analysis Using R
star   4.53 30.3K+ learners 2.5 hrs

Skills: Linear Regression, Concept of Multicollinearity, R Square, Predictive Modeling

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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

New

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Basics of Unsupervised Machine Learning
star   4.2 1.3K+ learners 7 hrs

Skills: Basics of Python

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Introduction to XGBoost
star   4.66 712 learners 1.5 hrs

Skills: Python skills, Basic ML concepts

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Artificial Intelligence and Machine Learning Projects
star   4.44 4.2K+ learners 1.5 hrs

Skills: Machine Learning Algorithms

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Predictive Analytics for Machine Learning
602 learners 2 hrs

Skills: Predictive Analytics, Feature Engineering, Deep Feature Synthesis, Model Selection Techniques

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Python IDEs for Machine Learning
1.4K+ learners 2.5 hrs

Skills: Introduction to Python IDEs, Spyder, Google Colab, Jupyter Notebook

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Cross Validation in Machine Learning
252 learners 1.5 hrs

Skills: Cross-validation, K-fold CV and LOOCV, ROC-AUC curve

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Feature Engineering for Machine Learning
star   4.46 896 learners 1 hr

Skills: Introduction to Feature Engineering, Under sampling, Over sampling

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Applications of Data Science & Machine Learning
star   4.65 1.2K+ learners 1 hr

Skills: Statistical analysis, Deep Learning, how to work and process large and unstructured data sets, and Data Visualization and among others.

Trending

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Python for Machine Learning and Data Science
star   4.65 10K+ learners 3 hrs

Skills: Introduction to NumPy, Pandas and Data Visualization in Python

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LDA in Entertainment Industry
star   4.64 1K+ learners 1 hr

Skills: Application of LDA, Building Pipelines, Data Balancing, Data Validation

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Machine Learning Landscape
star   4.63 3.9K+ learners 1.5 hrs

Skills: Machine Learning Landscape

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Bagging and Boosting
star   4.62 2K+ learners 1 hr

Skills: Working with Prediction Errors, Understanding Ensemble Methods, Introduction to Bagging and Boosting, Bagging vs Boosting, Practical Demo in Python

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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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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

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Introduction to Supervised Learning
star   4.61 2.2K+ learners 1 hr

Skills: Machine Learning, Supervised Learning

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Bias Variance Tradeoff
star   4.59 1.3K+ learners 0.5 hr

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

Popular

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Python for Machine Learning
star   4.51 471.9K+ 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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Basics of Machine Learning
star   4.39 148.3K+ learners 2.5 hrs

Skills: Introduction to Machine Learning, Supervised Machine Learning, Linear Regression, Pearson's Coefficient, Coefficient of Determinant

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Machine Learning with Python
star   4.57 96.5K+ learners 11 hrs

Skills: Python, Statistics, Reinforcement learning, Machine learning

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Introduction to Machine Learning
star   4.46 77.5K+ learners 1 hr

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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Statistics for Machine Learning
star   4.58 43.7K+ learners 2 hrs

Skills: Descriptive Statistics, Measures of Dispersion Range and IQR,,Central Tendency and 3 Ms,The Empirical Rule and Chebyshev Rule,Correlation Analysis

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Machine Learning Algorithms
star   4.49 32.3K+ learners 1.5 hrs

Skills: Classification (Logistic Regression, Decision Trees, SVM), Regression (Linear, Ridge, Lasso), Clustering (K-means, Hierarchical), model evaluation, cross validation

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Regression Analysis Using R
star   4.53 30.3K+ learners 2.5 hrs

Skills: Linear Regression, Concept of Multicollinearity, R Square, Predictive Modeling

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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

Our learners also choose

Learner reviews of the Free Machine Learning Courses

Our learners share their experiences of our courses

4.49
68%
23%
6%
1%
2%
Reviewer Profile

5.0

Country Flag Philippines
“Beginner in Python.”
Easy to follow and the instructor explained everything well.
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

“Great Introduction to NumPy and Pandas!”
The course does a fantastic job of breaking down complex library functions. I especially liked the quizzes on ndarray and DataFrame indexing—they really helped reinforce the difference between .loc and .iloc. It was easy to follow, though some more complex matrix operations could use a bit more depth.
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.
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Dr. Abhinanda Sarkar

Senior Faculty & Director Academics, Great Learning
  • 30+ years of experience in data science, ML, and analytics.
  • Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.
instructor img

Dr. Abhinanda Sarkar

Senior Faculty & Director Academics, Great Learning
  • 30+ years of experience in data science, ML, and analytics.
  • Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.
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.
instructor img

Dr. Sunil Kumar

GM - Engineering Innovation
  • 15+ years of industry experience in AI, machine learning, NLP
  • Published researcher, speaker, and author of 'R Machine Learning Projects
instructor img

Prof. Mukesh Rao

Senior Faculty, Academics, Great Learning
  • 20+ years of expertise in AI, machine learning, and analytics
  • Director - Academics at Great Learning

Frequently Asked Questions

What will I learn in these free machine learning courses?

These free Machine Learning courses online provide a comprehensive foundation in AI and data science. You will cover core concepts such as supervised, unsupervised, and reinforcement learning. Specifically, you will gain hands-on experience with algorithms like linear and logistic regression, decision trees, random forests, and k-means clustering. This structured approach makes them the best free machine learning courses for those wanting to bridge the gap between theory and real-world application.

Are these free machine learning courses online suitable for complete beginners?

Yes. We offer a best free machine learning course for beginners that starts with basic Python programming and essential statistics. You don't need a heavy coding background to start; the curriculum is designed to guide you from the ground up, making these free courses in machine learning accessible to students and career-switchers alike.

What specific technical skills will I gain from these free ml courses?

By enrolling in these free ml courses, you will acquire high-demand skills, including:

  • Data Preprocessing: Cleaning and structuring raw data for model training.

  • Supervised Learning: Building predictive models for classification and regression.

  • Unsupervised Learning: Discovering hidden patterns through clustering and dimensionality reduction.

  • Deep Learning: An introduction to neural networks and computer vision.

  • Model Evaluation: Using metrics like accuracy, precision, recall, and F1-score to tune performance.


Will I have lifetime access to these free Machine Learning courses with certificates?

Yes. You will have lifetime access to these courses after enrolling in them and access to certificates after completing the course.  

Which tools and libraries are covered in the curriculum?

Our free machine learning courses online focus on industry-standard tools. You will learn to use Python as your primary language, along with powerful libraries such as NumPy and Pandas for data manipulation, Matplotlib and Seaborn for visualization, and Scikit-learn for implementing advanced ML algorithms.

Will I get a certificate after completing these free Machine Learning courses?

All courses are free, A certificate is available for a nominal fee upon successful completion of the course. 

How long does it take to complete these free machine learning courses online?

Most of our high-impact modules range from 1.5 to 3 hours of video content. This "sprint-style" learning allows you to gain a specific, marketable skill, making these the best free machine learning courses for busy professionals.

How much do these free Machine Learning courses cost Online?

These are free courses; you can enroll and learn for free online.  

Are the free machine learning courses self-paced?

Yes. Every course in the academy is entirely self-paced. Once you sign up, you get lifetime access to the video lectures and reading materials. This flexibility is perfect for anyone looking for free machine learning courses online that can be completed alongside a full-time job or university studies.

Do these courses include hands-on projects?

Absolutely. Practical application is a core focus. You will work on real-world datasets to solve problems such as predicting house prices, detecting fraudulent transactions, and segmenting customers for marketing. This hands-on experience ensures that our free courses in machine learning provide more than just theoretical knowledge.

Is there a specific machine learning course for healthcare or finance?

While the foundational courses are broad, the techniques you learn, such as predictive modeling and anomaly detection, are directly applicable to these sectors. Many learners use these free ml courses as a springboard to specialized roles in medical diagnostics or financial risk analysis.

Can I take multiple free ml courses at the same time?

Yes. You can enroll in as many courses as you wish. Many students choose to take a Python course alongside a Linear Regression module to strengthen their programming and mathematical foundations simultaneously.

Why take Machine Learning free courses from Great Learning Academy?

Great Learning Academy offers a wide range of high-quality, completely free Machine Learning courses. From beginner to advanced level, these free courses are designed to help you improve your Machine Learning and technology-related skills and achieve your goals. All these courses come with a certificate of completion, so you can demonstrate your new skills to the world. Start learning today and discover the benefits of free Machine Learning courses!



 

Who are eligible to take these free Machine Learning courses?

These courses have no prerequisites. Anybody can learn from these courses for free online. 


 

What are the steps to enroll in these free Machine Learning courses?

To learn Machine Learning basics and advanced concepts from these courses, you need to,

  1. Go to the course page
  2. Click on the "Enroll for Free" button
  3. Start learning the Machine Learning course for free online.