Back to Machine Learning with Python

4.7

stars

9,725 ratings

•

1,581 reviews

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.
In this course, we will be reviewing two main components:
First, you will be learning about the purpose of Machine Learning and where it applies to the real world.
Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!
By just putting in a few hours a week for the next few weeks, this is what you’ll get.
1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy
2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.
3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course....

RC

Feb 07, 2019

The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.

RN

May 26, 2020

Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!

Filter by:

By MICHAEL K

•Sep 16, 2020

The Machine Learning course was made practical with hidden mathematics and applied to solve real world research problems. The instructor merged the theories with labs to simplify difficult part of Machine Learning. I recommend this course for any one interested in using predictive modelling to solve research questions.

By kalidindi s v

•Apr 04, 2020

Before joining this course I thought ML is so tough. But after this course I got a overview of some of the concepts of ML and not only overview, they also provided the lab sessions for every concept they teach. I suggest the beginners to join this because they get the complete overview of Machine learning. Thankyou..

By Rafael A C

•Jun 24, 2020

Presentations are very well designed. I have teaching experience and I can tell you that my style is great for illustrative purposes.

I learned to conceptually understand the mechanism and purpose of the models presented in Machine Learning. I feel like I can do things that were unthinkable for me before. Thanks IBM!

By Abhijit S

•Mar 09, 2020

This introductory course is really very good to understand the basics as well as methods to perform activity. Would recommend highly to anyone wish to learn ML in Python. The explanation, bit of maths and code were flawless and explained well in video as well as in code (most of code is explained in sample notepad).

By Daniel K

•Nov 01, 2019

The information in this course is laid out in a easily digestible format that makes it possible to fully own the knowledge that you gain and put it to the test. I appreciate that the videos are straight to the point and that the jupyter notebooks illustrate varying techniques for cleaning data. Tremendous value.

By Amy P

•Jul 25, 2019

Having worked my way through the IBM Data Science courses, this one was the "pay off" - it was so cool to finally apply more sophisticated techniques to real world data sets. The labs were fantastic. Highly recommend this course to anyone interested in learning about the most popular machine learning algorithms.

By V M R

•May 18, 2019

Complex concepts of machine learning algorithms are explained clearly with an illustration. Learner definitely have confidence in Machine learning after this course completion. A practical assignment work is really helped the learner to do the implementation of classifier model of their own and gain confidence.

By Shakshi N

•Jan 15, 2020

This course has been awesome. I have been doing ML Work for my college for quite some time, but never understood what goes in it, and kind of surfed through the net and just did the work. But this course has given me in depth knowledge of the logic that goes behind these algorithms and for that I am very glad.

By Mayank P

•May 21, 2019

This course offers a simple and effective experience. I learnt how to find the most accurate algorithms in the scenarios. Most importantly, the Jupyter notebooks provided are although optional, but you should study them thoroughly. They might seem difficult on an overview, but are very easy to understand.

By Surendrabikram T

•Jul 13, 2019

Great course.

It could be even better if programming assignment were provided in each week but still, final assignment was of great quality and I found it really engaging. The program introduces you to scikit learn which is again a wonderful advantage of taking this program. I am giving this course 5/5.

By Li G

•Aug 26, 2020

A very good course for beginners. It's quite practical and helpful. If it can go to more details of the machine learning modeling algorithms, it would be better. I get an overall picture of simple machine learning tasks but cannot handle real work task yet. The real world is much more complicated.

By Christopher A B

•Aug 19, 2020

The course was quite challenging. I especially appreciate how the labs required significant modification and deep understanding of the underlying motivation for the code in order to complete the final project for the course. Thanks to the lab authors and instructors for some high-quality demos!

By Luis M

•Jan 08, 2020

The course was thorough and a great introduction to machine learning. The capstone project was challenging and required me to have a good working knowledge of the various models. This has been the most intensive course, so far (course 8 of 9), in the IBM Data Science Professional Certificate.

By Priyansh S

•Jul 20, 2019

The course is really good for machine learning beginners. I would recommend everyone to take this course as it gives you all the basic knowledge and working of ML. It is fun to do with the Jupyter notebook tool which gives a great actual experience. Thanks a lot. This course helped me a lot.

By Srikanth G

•May 07, 2020

It is indeed a very thorough course, yet easy to understand. The animations and visual graphics made it an engaging and pleasurable experience. Learning classification, clustering and regression was made easy in such a way, that I could do it all over again without hesitation. Keep it up!

By Jeffrey P

•Jun 17, 2019

I think it would be beneficial to talk about neural networks somewhere after the gradient of steepest descent section. I did appreciate the course talked about many other ML algorithms that are not typically covered by other programs - and the lab notebooks are extremely valuable.

By XFAN

•Apr 17, 2020

If more knowledge on 1) how to find the optimal depth value for decision trees and variables for other models; 2) explanations on parameters used, will be elaborated in hands-on lab notebooks, it would be better. Those are important to new beginners with zero idea on ml models.

By Yi Y

•Oct 03, 2018

It is one of the best introduction course to Machine Learning.

The material is well explained to someone with a beginner level of understanding to Statistics and Machine Learning.

All the material is presented in a way that is easy to understand, without leaving out the details.

By Shiva S

•Mar 19, 2020

This course is a good chance to start python programming and reviewing ML concepts with deeper insights. I would suggest it for those who are familiar with ML and its algorithms. For those ones who want to start learning ML, it is better to take ML courses with Basic level.

By Omri

•Nov 27, 2019

Great course, cover many important aspects of classical machine learning algorithms. The lectures are very focused and not tedious. Labs are excellent, and can serve as a starting point for every data science project in the future. I definitely recommend taking the course.

By Pankaj Z

•May 03, 2020

This is one of the finest courses for anyone who wishes to transform his/her career into Machine Learning. It has optional external tool assignments after each chapter to help you understand and try out code and the concept. I would highly recommend this course to anyone.

By Dominique D

•Apr 16, 2020

If you put your heart to it, there is really a lot to learn in the course. The course touches quite some ML topics and gives a good introduction to it. I feel I got a whole new set of tools to use, and i am hungry to learn and experiment more.

Really enjoyed the course!

By Benedict A

•Apr 02, 2020

The videos and labs were remarkable in that it was able to concisely communicated vast and complex information.

I did have to do additional research to fully understand and appreciate the material because I am not coming from a programming or statistical background.

By Toan L T

•Oct 28, 2018

Great course.

Knowledge wise, just like Prof. Ng's, minus the mathematics foundation.

Practical wise, carefully designed labs really help learners understand the data cleaning processes, understanding data through visualization, ML algorithms and evaluation metrics.

By Roger T

•Mar 28, 2020

It's a very precise and practical course. It focuses on the main ideas and application aspects of M.L., without drilling too deep into the math rationale behind.

To get the most from this course, it's good to equip yourself with basic knowledge in numpy and pandas.

- AI for Everyone
- Introduction to TensorFlow
- Neural Networks and Deep Learning
- Algorithms, Part 1
- Algorithms, Part 2
- Machine Learning
- Machine Learning with Python
- Machine Learning Using Sas Viya
- R Programming
- Intro to Programming with Matlab
- Data Analysis with Python
- AWS Fundamentals: Going Cloud Native
- Google Cloud Platform Fundamentals
- Site Reliability Engineering
- Speak English Professionally
- The Science of Well Being
- Learning How to Learn
- Financial Markets
- Hypothesis Testing in Public Health
- Foundations of Everyday Leadership

- Deep Learning
- Python for Everybody
- Data Science
- Applied Data Science with Python
- Business Foundations
- Architecting with Google Cloud Platform
- Data Engineering on Google Cloud Platform
- Excel to MySQL
- Advanced Machine Learning
- Mathematics for Machine Learning
- Self-Driving Cars
- Blockchain Revolution for the Enterprise
- Business Analytics
- Excel Skills for Business
- Digital Marketing
- Statistical Analysis with R for Public Health
- Fundamentals of Immunology
- Anatomy
- Managing Innovation and Design Thinking
- Foundations of Positive Psychology