Machine Learning with Python Free Course

Learn Machine Learning Python and become a certified database professional for free!

  • Learn to leverage Python for solving problems using data models 
  • Excel as an ML expert through this course that perfectly blends theory and practice 
  • Boost your career with the Machine Learning with Python course for free
  • 450,000 + Professionals trained
  • 250 + Workshops every month
  • 100 + Countries and counting

Propel ML Skills with Python

Build on your foundational knowledge of Machine Learning and develop solutions for complex business problems using the capability of machine learning. Learn advanced concepts and understand them by practicing your learning in hands-on exercises. Create machine learning models and explore the right methods to evaluate such models.

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  • 24+ hours of Self-Paced Learning Content 

  • Practice with Guided Hands-On Exercises 

  • Learn-by-Doing with Immersive Learning 

  • Test Your Learning with Recall Quizzes 

  • Unlock Knowledge with Interactive eBooks 

  • Accelerate Progress with Auto-Graded Assessments 

Ready to get started?

Contact Learning Advisor

The KnowledgeHut Edge

Superior Outcomes

Focus on skilled-based outcomes with advanced insights from our state-of-the art learning platform. 

Immersive Learning

Go beyond just videos and learn with recall quizzes, interactive ebooks, case studies and more. 

World-Class Instructors

Course instructors and designers from top businesses including Google, Amazon, Twitter and IBM. 

Real-World Learning

Get an intimate, insider look at companies in the field through real-world case studies. 

Industry-Vetted Curriculum

Curriculum primed for industry relevance and developed with guidance from industry advisory boards. 

Continual Support

Learn better with support along the way. Get 24/7 help, stay unblocked and ramp up your skills. 

prerequisites for Machine Learning with Python Free Course


  • Familiarity with Python Programming Language 
  • Basic Mathematics and Statistics Knowledge 
  • Understanding of Probability and Statistics 
  • Basic Data Analysis and Data Manipulation Skills 

Who Should Attend

Software Engineers

Data Engineers

Data Analysts

Data Scientists

Frontend Developers

Backend Developers

Full Stack Developers





What You Will Learn

Linear Algebra

Learn how to use linear algebra in creating effective machine learning algorithms. 


Familiarize yourself with the Calculus techniques used in machine learning. 

Machine Learning Fundamentals

Master the foundational concepts in machine learning and how to apply them wisely. 

Linear Regression

Learn to use linear regression to make a machine learning model for predictive analysis. 

Lasso Regression

Leverage Lasso regression to build more accurate prediction models with Python. 

Ridge Regression

Learn how and when to use ridge regression and how it produces better results. 

Elastic-Net Regression

Learn to combine the Lasso and Ridge regression in using the Elastic-Net regression. 

Logistic Regression

Use logistic regression by using classification algorithm to make yes or no decisions.

Skills You Will Gain

Understand Machine Learning Concepts

Data Preprocessing

Data Exploration

Feature Engineering

Building Machine Learning Models

Model Evaluation

Supervised Learning Algorithms

Unsupervised Learning Algorithms

Model Optimization

Hyperparameter Tuning


Learning Objective: Learn how to use linear algebra to represent and manipulate data and in creating mathematical models. 

Learning Objective: Learn how you can use calculus to understand and optimize machine learning algorithms. 

Learning Objective: Understand the difference between statistical learning and machine learning and how ML approaches use statistical functions under the hood to find good models. Learn important machine learning concepts such as overfitting and cross-validation for overfitting detection and model evaluation. Additionally, understand bias, variance and the bias-variance tradeoffs in the context of machine learning models.    

  • The Training and Testing Paradigm / Introduction to scikit-learn 
  • Model Evaluation Methods (Subtopics: Deviance, AIC, Pseudo R-squared)
  • Model Performance Measures (Subtopics: Confusion Matrix and Cross Entropy)
  • Imbalanced Data Subtopics: What is Imbalanced Data and How to Deal with It - Oversampling, Undersampling and SMOTE)
  • Overfitting and Cross Validation (K-fold Cross Validation, LOOCV) 
  • Bias and Variance, Trade-offs
  • Hyper Parameters and Grid Search
  • Case Study on Linear Regression with scikit-learn 

Learning Objective: Cover the simplest form of statistical learning: linear regression, including simple linear regression and multiple linear regression. Understand topics of covariance, correlations and method of least squares. Significance of parameters, methods to assess fit and assumptions about the data are covered to understand the results and validity of applying linear regression.    

  • Covariance and Correlation 
  • Simple Linear Regression 
  • Slope and Intercept, Interpretation
  • OLS Method and Assumptions 
  • Introduction to Statsmodels 
  • Multiple Linear Regression
  • Beta Coefficient, Significance and Their Interpretation
  • Errors and Metrics Related to Errors, R^2 and Adj R^2
  • Linear Regression Diagnostic Plots 

Learning Objective: Understand the theory of classification in statistical learning and the logistic regression model. Learn about the technique of maximum likelihood estimation. Diagnostic methods, metrics, and techniques to deal with imbalanced datasets are also covered in this module. A practical example combining all the mentioned is included in this section.  

  • Probability, Odds, Log Odds 
  • Sigmoid and Logit 
  • Maximum Likelihood Estimation 
  • Significance of Beta Coefficients and Their Interpretation 
  • Case Study on Logistic Regression 

Frequently Asked Questions

What Learners Are Saying

João Santos Senior Software Engineer

I identified that machine learning is what I needed to get better at to progress in my career. Since I’ve been working on and off with machine learning models, this course made sense to me. It helped me get a much more comprehensive and practical understanding of how it works.  

Ethan Miller Python Developer

Python is a language I enjoy using and it’s something I am comfortable with. That was one of the reasons why I signed up for this course. It was a great decision; it has helped me use my strength in Python to build advanced machine learning models and even to optimize them in a way that almost came naturally to me. I’ve been talking about this experience to every colleague.   

Dimas Budiman Data Scientist

Machine Learning has always been a weak area for me. I have tried getting good at it for a while, but I never found a way to make it work even though I had built a decent understanding around it. This course changed all that for me. The videos helped me get clarity on the concepts I had trouble understanding and practicing them in the lab made it practical. 

Jackson Moore Machine Learning Engineer

I have done a few courses on Machine Learning before, so I had certain expectations getting into this course. The whole content felt fresh and very practical. As an experience the course revitalized me and gave me a lot of fresh ideas to drive new projects at work. I loved the format and I’m definitely enrolling for more courses. 

Isabella Lopez Associate – Python and Machine Learning

With this course the way I work has changed quite a lot. The videos were engaging and informative, the exercises that we got to do helped me figure out how to use these new learnings in my job. After this course I have immensely expanded the scope of my work and I have been recognized for that as well. 

Chloe Walker Machine Learning Scientist

I signed up for this course only because I was curious. I was pleasantly surprised with the amount of learnings that were packed into it.