Python is one of the **most widely used** programming languages in the Machine Learning field. Python has many packages and libraries that are specifically tailored for certain functions, including **pandas, NumPy, scikit-learn, Matplotlib, and SciPy**. So if you want to learn Machine Learning with Python, this article is for you. In this article, you will find the **12** **Best Online Courses for Machine Learning with Python**.

These courses are filtered out on the following criteria-

**Criteria-**

- Rating of these Courses.
- Coverage of Topics.
- Engaging trainer and Interesting lectures.
- Number of Students Benefitted.
- Good Reviews from various aggregators and forums.

Now, without wasting your time, let’s start finding the **Best Online Courses for Machine Learning with Python**.

**Best Online Courses for Machine Learning with Python**

**Best Online Courses for Machine Learning with Python**

- 1. Machine Learning with Python- Coursera
- 2. Intro to Machine Learning with TensorFlow- Udacity
- 3. Machine Learning Scientist with Python- Datacamp
- 4. Machine Learning A-Z™: Hands-On Python & R In Data Science- Udemy
- 5. Become a Machine Learning Engineer- Udacity
- 6. Applied Machine Learning in Python- Coursera
- 7. Understanding Machine Learning with Python- Pluralsight
- 8. Python for Data Science and Machine Learning Bootcamp- Udemy
- 9. Intro to Machine Learning with PyTorch- Udacity
- 10. Machine Learning, Data Science, and Deep Learning with Python- Udemy
- 11. Learn NumPy Fundamentals (Python Library for Data Science)– Udemy
- 12. Foundations of Data Science: K-Means Clustering in Python– Coursera

**1. ****Machine Learning with Python**– Coursera

**Machine Learning with Python**– Coursera

Provider- | IBM (Coursera) |

Rating- | 4.7/5 |

Time to Complete- | 22 hours |

This course has a** 6-week study plan. **In the first week, you will understand the **basics of machine learning** and in the next week, you will learn **Regression, simple linear regression, and multiple linear regression.**

The third and fourth week is all about **classification** where you will learn **K-Nearest Neighbours**, **Decision Trees**, **Logistic Regression**, **Logistic regression vs Linear regression**, **and** **Support Vector Machine**.

After that, you will learn** clustering and k-Means clustering. **The complete course has** various quizzes and exercises. **To excel in this course, it is good to have some** previous math knowledge.**

**Extra Benefits-**

- You will get a
**Shareable Certificate**. Along with that, you will earn an I**BM digital badge**. - Along with this, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, Graded Programming Assignments**

**Who Should Enroll?**

- This course is
**good for beginners**in Machine Learning, who wanna learn Machine Learning with**Python**.

**Interested to Enroll?**

If yes, then check out the details here- **Machine Learning with Python**

**2. Intro to Machine Learning with TensorFlow– Udacity**

Provider- | Udacity |

Rating- | 4.7/5 |

Time to Complete- | 3 months (if you spend 10 hrs/week) |

This is a Nano-Degree Program. There are **3 courses** in this program. In the first course, you will learn supervised learning and the algorithms of supervised learning such as **Regression**, **Perceptron Algorithm, Decision Trees, Naive Bayes**, **SVM, etc. **Along with the supervised algorithms, you will also understand the **training and testing procedure and data visualization basics.**

In the next course, you will understand** neural network basics **and learn **how to implement gradient descent and backpropagation in Python.**

The last course is all about unsupervised learning and covers the **K-means algorithm**, **Single Linkage Clustering**, **Gaussian Mixture Models, etc.**

All three courses have one project. Along with the projects, there are **quizzes and practice** sets throughout the program.

**Extra Benefits-**

- You will get a chance to work on
**Real-world projects.** - You will get
**Technical mentor support.** - Along with that, you will get
**Career services.**

**Who Should Enroll?**

- Those who have
**Intermediate Python programming**knowledge and familiar with data structures like dictionaries and lists. - And those who have
**basic knowledge of probability and statistics.** - This program is especially good
**for those who have experience in Python but have not yet studied Machine Learning topics.**

**Interested to Enroll?**

If yes, then check it out here- **Intro to Machine Learning with TensorFlow** **(Udacity)**

**3. Machine Learning Scientist with Python– Datacamp**

Provider- | Datacamp |

Rating- | NA |

Time to Complete- | 93 hours |

This is a career track offered by **Datacamp**. There are **23 courses** in this career track. The course starts with the** basics of supervised learning, unsupervised learning, linear classifiers, etc. **

You will also learn the **basics of gradient boosting with XGBoost. ** There are separate courses on **dimensionality reduction, time-series data in Python, NLP, feature engineering, and deep learning using Tensorflow and Keras.**

After that, you will learn **PySpark, Image processing, etc.** In the end, you will understand how to **win competitions on Kaggle.** Overall, this is a detailed career track for machine learning combined with practical exercises.

**Who Should Enroll?**

- Those who are a beginner in Machine learning and looking for step-by-step career guidance.

**Interested to Enroll?**

If yes, then check out the course details here- **Machine Learning Scientist with Python**

**4. Machine Learning A-Z™: Hands-On Python & R In Data Science– Udemy**

Provider- | Udemy (SuperDataScience Team) |

Rating- | 4.5/5 |

Time to Complete- | 44 hours |

This is the **Bestseller Course at Udemy**. I love this course. This course not only teaches you the theory related to Machine Learning but also provides the implementation of each Machine Learning algorithm.

The best part of this course is that you will find implementation in Both Languages **Python and R**. If you are a complete beginner in Machine Learning, then this course is best for you.

This course **doesn’t cover advanced topics **but covers all basic topics of Machine Learning. You will also learn the basics of Deep Learning and Natural Language Processing.

**Extra Benefits-**

- You will get a
**Certificate of Completion**. - You will also get
**74 articles and 38 downloadable resources.** - Along with that, you will get lifetime access to the course material.

**Who Should Enroll?**

- This course is for anyone who wants to learn Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.

**Interested to Enroll?**

If yes, then check out the details here- **Machine Learning A-Z™: Hands-On Python & R In Data Science**

**5. ****Become a Machine Learning Engineer**– Udacity

**Become a Machine Learning Engineer**– Udacity

Provider- | Udacity |

Rating- | 4.6/5 |

Time to Complete- | 3 months (If you spend 10 hours per week) |

This is another Nano-Degree program but** not for beginners**. This program is designed for those who have a basic understanding of **Machine Learning concepts and Python programming.**

In this program, you will learn **advanced concepts of Machine Learning** such as **XGBoost and AutoGluon**.

There are **4 courses and 5 projects** in this Nanodegree Program. That means, this program is practical in nature, which is a positive part of this Nanodegree. But the Nanodegree program is **expensive** compared to other platform courses. And it is worth it for **intermediate learners not for beginners.**

**Extra Benefits-**

- You will get a chance to work on
**Real-world projects.** - You will get
**Technical mentor support.** - Along with that, you will get
**Career services.**

**Who Should Enroll?**

- Those who have
**Intermediate Python programming knowledge and Intermediate knowledge of machine learning algorithms**.

**Interested to Enroll?**

If yes, then check it out **here**– **Become a Machine Learning Engineer (Udacity)**

**6. Applied Machine Learning in Python– Coursera**

Provider- | University of Michigan (Coursera) |

Rating- | 4.6/5 |

Time to Complete- | 34 hours |

This is another **machine learning course with Python**. This course has a 4-week study plan. In the first week, you will learn machine learning basics such as tools used in **Python for machine learning, K-Nearest Neighbors Classification, etc.**

Next, you will learn supervised machine-learning algorithms and understand **Overfitting and Underfitting**, **K-Nearest Neighbors**, **Linear Regression**, **Logistic Regression**, **Support Vector Machines**, **Cross-Validation, and Decision Trees**.

Model evaluation is the essential step in machine learning and you will understand **Confusion Matrices & Basic Evaluation Metrics**, **Precision-recall and ROC curves, Multi-Class Evaluation**, **etc.**

The last part of this course covers some advanced concepts of supervised learning such as **Naive Bayes Classifiers, Random Forest**, **Gradient Boosted Decision Trees**, **Neural Networks**, **Dimensionality Reduction, and Manifold Learning**.

**Extra Benefits-**

- You will get a
**Shareable Certificate**upon completion. - Along with this, you will get
**Course Videos & Readings, Practice Quizzes, Graded Assignments with Peer Feedback, Graded Quizzes with Feedback, and Graded Programming Assignments.**

**Who Should Enroll?**

- Those who have previous knowledge in
**Data Visualization using Python.**

**Interested to Enroll?**

If yes, then check it out **here**– **Applied Machine Learning in Python**

**7. ****Understanding Machine Learning with Python**– Pluralsight

**Understanding Machine Learning with Python**– Pluralsight

Provider- | Pluralsight |

Rating- | NA |

Time to Complete- | 1 hour 53 minutes |

In this course, you will learn how to perform **Machine Learning with Python.** This course covers **machine learning basics, machine learning workflow, data preparation, how to select the algorithm for a certain problem, and how to train and test the machine learning model.**

You will also learn **how to evaluate the model performance** and understand the **overfitting problems.**

**Who Should Enroll?**

- Those who want to learn the basics of
**machine learning with Python**. And who is familiar with software development in general and**basic statistics.**

**Interested to Enroll?**

If yes, then check out the details here- **Understanding Machine Learning with Python**

**8. ****Python for Data Science and Machine Learning Bootcamp**– Udemy

**Python for Data Science and Machine Learning Bootcamp**– Udemy

Provider- | Udemy (Jose Portilla) |

Rating- | 4.6/5 |

Time to Complete- | 25 hours |

This is also one of the most popular courses available at Udemy. This course starts with **data analysis and data visualization using Python.** After that, you will learn **machine learning basics, Linear Regression, K-Nearest Neighbors, Decision Tree, Random Forest, SVM, PCA, NLP, etc.**

You will also learn **neural networks and deep learning basics in this course. **Overall, this is a good course for learning machine learning and data science using Python.

**Extra Benefits-**

- You will get a
**Certificate of Completion.** - You will also get 13 articles and 5 downloadable resources.
- Along with that, you will get lifetime access to the course material.

**Who Should Enroll?-**

- Those who have at least some programming experience.

**Interested to Enroll?**

If yes, then check out the details here- **Python for Data Science and Machine Learning Bootcamp**

**9. Intro to Machine Learning with PyTorch– Udacity**

Provider- | Udacity |

Rating- | 4.7/5 |

Time to Complete- | 3 months (If you spend 10 hours per week) |

This is a Nano Degree Program offered by Udacity. In the first course, you will learn supervised learning and the algorithms of supervised learning such as **Regression**, **Perceptron Algorithm, Decision Trees, Naive Bayes**, **SVM, etc. **Along with the supervised algorithms, you will also understand the **training and testing procedure and data visualization basics.**

In the next course, you will understand** neural network basics **and learn **how to implement gradient descent and backpropagation in Python.**

The last course is all about unsupervised learning and covers the **K-means algorithm**, **Single Linkage Clustering**, **Gaussian Mixture Models, etc.**

**Extra Benefits-**

- You will get a chance to work on
**real-world projects with industry experts.** - You will get
**Project feedback from experienced reviewers**and you will also get**Technical mentor support.** - Along with that, you will get
**Resume services, a**Github review, and a**LinkedIn profile review.**

**Who Should Enroll?**

- Those who have
**intermediate-level experience in Python**and**basic knowledge of probability and statistics.**

**Interested to Enroll?**

If yes, then check out all details here- **Intro to Machine Learning with PyTorch**

**10. Machine Learning, Data Science, and Deep Learning with Python– Udemy**

Provider- | Udemy (Sundog Education by Frank Kane) |

Rating- | 4.6/5 |

Time to Complete- | 14.5 hours |

This is another **Python-oriented** course for machine learning. In this course, first, you will learn** statistics and probability basics.** Next, you will learn machine learning algorithms such as **K-Means Clustering**, **Decision Tress, SVM, etc. **

The course also covers **dimensionality reduction techniques, bias/variance tradeoffs, feature engineering, Apache Spark, Deep Learning, Neural Networks, and Generative models.**

If you are a beginner in Python, then you will learn Python basics too. After completing this course, there is one final project, which you need to complete.

**Extra Benefits-**

- You will get a
**Certificate of Completion.** - You will also get 6 articles and 1 downloadable resource.
- Along with that, you will get lifetime access to the course material.

**Who Should Enroll?-**

- Those who know at least high school level math and have some prior coding experience.

**Interested to Enroll?**

If yes, then check out the details here- **Machine Learning, Data Science, and Deep Learning with Python**

**11. ****Learn NumPy Fundamentals (Python Library for Data Science)– Udemy**

**Learn NumPy Fundamentals (Python Library for Data Science)– Udemy**

Provider- | Udemy |

Rating- | 4.6/5 |

Time to Complete- | 1hr 49min |

This is a short course but very concise course to understand the **Python library-Numpy**. In this course, there are 3 sections. In the first section, there is nothing but an only** introduction to the course. **In the second section, you will learn **Numpy basics, array creation, reshaping, indexing, advanced indexing, array maths, and broadcasting.**

The last section covers** Python basics** for those who don’t know Python before. This course is good for those who are beginners in Python and want to learn Numpy.

**Interested to Enroll?**

If yes, then check out all details here- **Learn NumPy Fundamentals (Python Library for Data Science)**

**12. ****Foundations of Data Science: K-Means Clustering in Python**– Coursera

**Foundations of Data Science: K-Means Clustering in Python**– CourseraProvider- | Udemy |

Rating- | 4.6/5 |

Time to Complete- | 29 hours |

This is a **free** course offered by Coursera. To access this course for free, click on the **“Enroll for FREE” **option. A new popup window will appear where there are two options- “**Purchase Course**” and “**Full Course, No Certificate**“. Choose the second option** “Full Course, No Certificate”**. And you will be redirected to the course material.

This course has a **5-week study plan. **In the first week, you will learn the** data science and machine learning basics.** Along with that, you will learn** k-means clustering.**

Week 2 covers the **mathematical concepts of machine learning** such as **mean, variance, and standard deviation.** You will also learn **basic statistics in Python.**

Week 3 is a practical week where you will learn how to work with **multidimensional data in Python** and learn the **matplotlib library.** In week 4, you will work on **Pandas library for reading, sorting, and filtering the data.** Week 5 has one **Capstone project.**

**Interested to Enroll?**

If yes, then check out all details here- **Foundations of Data Science: K-Means Clustering in Python**

And here the list ends. I hope these Best Online Courses for Machine Learning with Python will help you. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up this **Best Online Courses for Machine Learning with Python**.

**Conclusion**

In this article, I tried to cover the **12 Best Online Courses for Machine Learning with Python**. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

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