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Machine Learning with Python

Master key skills in Machine Learning using the highly-popular Python programming language by enrolling for Machine Learning with Python course offered by IBM.

The highlights

Eligibility criteria, what you will learn.

• Admission Details

The syllabus

• Scholarship

How it helps

Similar courses, courses of your interest.

• More courses by provider

Intermediate

Quick facts, course overview.

We live in an era where data surrounds us in various forms. For businesses, data-driven decision-making assumes great significance. In order to gain a competitive edge in the global scenario, businesses make use of machine learning to unlock the premium value from the data available to them via various sources. Simply put, machine learning is the unique process of enabling machines to make intelligent decisions with the use of data provided.

In today’s highly digitalized world, almost every machine or system is embedded with machine learning algorithms, in one form of the other. Facebook, Google Search, LinkedIn, Amazon and similar systems are powered by machine learning algorithms to make them highly efficient and responsive. Python, a widely-used open-source programming language offers widespread libraries, low-level barrier, flexibility, versatility, ease in readability and ample visualisation tools; thereby making it one of the most preferred programming languages for ML developers.

The Machine Learning with Python course, offered by IBM via Coursera, offers the participants an opportunity to learn the basics of machine learning using Python. The course has been uniquely designed to assist the participants in culminating key skills in ML by making use of Python tools and techniques. The course can be completed within 6 weeks and upon completion, participants will receive the Certificate from Coursera, as well as the IBM Digital Badge.

• Offered by IBM
• 100% online mode of learning
• Learning with IBM instructors
• Approximately 12 hours of videos and readings
• Flexible deadlines in accordance with your schedule
• Shareable certificate for online profile/CV
• IBM Digital Badge upon completion
• English and Vietnamese subtitles
• The option of 7-day Free Trial and Financial Aid

Program offerings

• Online learning
• Assignments

Course and certificate fees

• Coursera provides the applicants with the option to enrol with a 7-day free trial for the Machine Learning with Python course. Similarly, participants can view the whole course content using the ‘audit mode’.
• However, participants will not have access to assignments, quizzes and project work, which is required to earn the Certificate. For this, participants will be required to “Upgrade to earn a Professional Certificate”. Participants opting for the upgrade will be given a 7-day free trial with unlimited access to all courses in this Certificate. 
• After the end of the 7-day free trial, participants will be required to pay Rs. 3,184 to continue learning and unlock the assignments required to avail of the Certificate.

Machine Learning with Python Course Fees Details

certificate availability

Certificate providing authority.

Certification Qualifying Details

Participants have to pass all the graded assignments to receive a shareable certificate. These will include quizzes, peer-graded assignments and final projects. The electronic certificate issued by Coursera will be added to the participants’ accomplishment page. They can take a printout of the certificate or attach it to their LinkedIn profile, CVs or resumes. Additionally, those participants who have successfully completed the course will also receive the IBM Digital Badge.

Upon completion, the participants of the Machine Learning with Python course will be able to:

• Apply Machine Learning to real-world scenarios
• Use Python libraries to implement ML models
• Acquire new skills like SciPy, Regression, Clustering, Classification and Sci-Kit
• Evaluate and calculate regression models
• Gain insights on classification technique and accuracy metrics
• Understand recommender systems including recommendation engines
• Efficiently apply various clustering approaches
• Improve programming skills
• Earn a shareable certificate and IBM Digital Badge highlighting your competency

Who it is for

Admission details.

To enrol for the Machine Learning with Python course, the participants will have to follow the below-mentioned steps.

Step 1: Log on to the course page

Step 2: Signup using your Email, Google or Facebook credentials.

Step 3: Applicants can opt for a 7-days free trial or opt for the financial aid option.

Step 4: After enrollment, participants will have access to most of the course material. However, they may not be able to access some of the assignments.

Week 1: Introduction to Machine Learning

• Course Introduction
• Introduction to Machine Learning
• Python for Machine Learning
• Supervised vs Unsupervised

Practice Exercises

• Practice Quiz: Intro to Machine Learning
• Graded Quiz: Intro to Machine Learning

Week 2: Regression

• Introduction to Regression
• Simple Linear Regression
• Model Evaluation in Regression Models
• Evaluation Metrics in Regression Models
• Multiple Linear Regression
• Practice Quiz: Regression
• Graded Quiz: Regression

Week 3: Classification

• Introduction to Classification
• K-Nearest Neighbours
• Evaluation Metrics in Classification
• Introduction to Decision Trees
• Building Decision Trees
• Regression Trees
• Practice Quiz: Classification
• Graded Quiz: Classification

Week 4: Linear Classification

• Intro to Logistic Regression
• Logistic regression vs Linear regression
• Logistic Regression Training
• Support Vector Machine
• Multiclass Prediction
• Practice Quiz: Linear Classification
• Graded Quiz: Linear Classification

Week 5: Clustering

• Intro to Clustering
• Intro to k-Means
• More on k-Means
• Practice Quiz: Clustering
• Graded Quiz: Clustering

Week 6: Final Exam and Project

• Congratulations
• IBM Digital Badge
• Project Scenario

Scholarship Details

Participants who cannot afford to pay for a certificate can apply for sponsorship from the course homepage. If a scholarship or financial assistance is approved, participants will get access to all course content and assignments that are required to earn the course certificate. 

These are the steps to apply for financial aid/scholarship.

• Open the homepage of the course, click on ‘Financial aid available’ button.
• Enter your login credentials. You can also log in using your Google, Facebook or Apple credentials.
• Fill out the financial aid application form.
• The application should be of more than 150 words. If the application is less than 150 words, the chances of rejection of aid are more.
• The review can take up to 15 days and you will be able to begin the course via audit mode till the application is approved.
• Coursera will send you an email about the status of the application form.
• In case the application is accepted, you will be directly enrolled in this course.
• Applicants have 2 weeks to unenroll from the course, in case they decide not to continue.
• If you start a free trial of the course while your financial aid/scholarship application is being reviewed, your application will be cancelled.

With the rapid advancements in the field of technology and telecom sector at large, a vast amount of data is generated and stored every second. This raw data, structured or unstructured, is of great importance to businesses and large organisations to analyse and utilise it to generate important decisions to enhance their performance. By making use of Machine Learning (ML) algorithms, businesses are benefiting a great deal by gaining access to user information to sustain and scale their operations as well as revenue.

Machine Learning (ML) assists businesses in gaining a competitive edge and scaling their growth by facilitating intelligent decision-making. Through this course, the participants will gain useful insights and exposure on key skills of ML by making use of Python programming language. The participants will be able to apply these skills in real-world scenarios, thereby contributing to enhancing the scalability of their respective employer organisations.

The Machine Learning with Python course will benefit the participants by imparting them skills on fundamentals of ML algorithms, linear regression, decision trees, clustering as well as supervised v/s unsupervised learning. Upon completion of the course, the participants will receive a certificate from Coursera which can be shared on their online profiles and CVs. IBM Digital Badge will also be awarded to participants successfully completing the course. Additionally, participants completing this course will also be able to apply for professional certificate courses like IBM Data Science and IBM AI Engineering; and further, enhance their competency.

Instructors

Mr Joseph Santarcangelo Data Scientist IBM

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Machine Learning with Python by IBM on Coursera

Learn ml with this great ibm machine learning course on coursera.

This course will teach you the basics of Machine Learning and Data Science with the most used programming language for advanced analytics in the world: Python .

We really like this course, as it is not just a course that will teach you the main concepts of Machine Learning like regression, classification, clustering, or the different Machine Learning algorithms, but also show you the main purpose of the field and where it is succesfully applied in the real world.

Many times courses fail to teach the practical applications of the subjects they teach. This is not the case, as you will see real-life examples of Machine Learning and how the affect society.

It is a very easy to follow course, that explains complicated concepts in a simple manner that follows a very well defined flow covering the maths behind the main concepts and also how to implement them in Python.

Organised in a series of videos with quizzies, exercises or labs in Jupyter Notebooks , assessments and a final project (which could have been a little better defined in our opinion), this course will teach you the basics of Machine Learning and you will just need a little Python programming knowledge and some basic math skills.

Coursera as always provides great forums in case you have any questions or get stuck in a certain exercise (the quizzies are challenging for beginners but doable if you have absorbed well the main concetps). Lets see what it contains!

• Introduction to Machine Learning : Applications of Machine learning in different sectors like healthcare, banking and telco, and get a main overview of the fundamental concepts of Machine Learning.
• Regression : As most ML courses we start with Linear and multiple regression, along with their applications like for example house price estimation. In the labs you will apply regression in two different datasets and learn how to evaluate the quality of your regression models.
• Classification : Different classification algorithms like K-nearest neighbours, Decision tress, Logistic Regression and Support vector machines and how each method compares to the rest. Lastly you will see different classification metrics and learn about the Confusion Matrix .
• Clustering : Now it is the time for un-supervised learning, widely used for real life applications like customer segmentation for Marketing purposes. You will learn about Hierarchical and Density Based clustering .
• Recommender systems : one of the most famous applications of Machine Learning – Learn how Netflix knows what show you are going to watch next or how Amazon always spots that product that you were thinking about.
• Final project : apply everything you have learned and submit a project for peer evaluation. In our opinion this project could have been a little better defined, and more challenging, but it is the only but that we give to this course.

Who is IBM Machine Learning with Python on Coursera for?

In our opinion this course is for enthusiast that want to learn about Machine Learning with no previous experience in the field. To complete it easily and make the most out of it, we recommend some previous Python programming knowledge and some very basic math skills.

If you need to learn Python, don’t worry, we’ve got the resources for you to do so. Some beginner Python books that you might want to check out are:

• Python Crash Course .
• Automating the Boring Stuff with Python .
• Learning Python  by  Mark Lutz .

Some other great resources for Learning Python, after which you will be able to tackle this course easily are:

• Kaggle Learn Python Tutorials.
• Learning Python Course on Codeacademy.
• Real Python Website.

Course summary:

IBM Machine Learning with Python is a great course for those that want both, to learn the fundamental technical concepts underlying machine learning and the real world applications of the field.

You will learn how to turn this theoretical knowledge into practice by programming in Python, and test your skill with many quizzies, exercises and labs.

This is a great course to begin with before tackling books like the following, which for us is the perfect book to take your knowledge from begginer to expert:

• Géron, Aurélien (Author)
• English (Publication Language)
• 856 Pages - 10/15/2019 (Publication Date) - O'Reilly Media (Publisher)

Duration : The course says that on total it can be completed in about 25 hours, but we recommend taking it slowly, embracing the content, and taking the time to complete the exercises.

Cost : This course can be taken for free like most Coursera courses, auditing it , however you will not get a certificate, and you will not be able to access some of the exercises.

As always, we hope you enjoyed the post on this IBM Machine Learning course with Python on Coursera. Learn a lot and come back to read How to Learn Machine Learning! Until then, take care 🙂

Tags : Machine Learning with Python, Machine Learning course for beginners, IBM Machine learning Coursera .

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Coursera: Machine Learning - All weeks solutions [Assignment + Quiz] - Andrew NG

Recommended Machine Learning Courses: Coursera: Machine Learning    Coursera: Deep Learning Specialization Coursera: Machine Learning with Python Coursera: Advanced Machine Learning Specialization Udemy: Machine Learning LinkedIn: Machine Learning Eduonix: Machine Learning edX: Machine Learning Fast.ai: Introduction to Machine Learning for Coders

=== Week 1 ===

Assignments: .

• No Assignment for Week 1
• Machine Learning (Week 1) Quiz ▸  Introduction
• Machine Learning (Week 1) Quiz ▸  Linear Regression with One Variable
• Machine Learning (Week 1) Quiz ▸  Linear Algebra

=== Week 2 ===

Assignments:.

• Machine Learning (Week 2) [Assignment Solution] ▸ Linear regression and get to see it work on data.
• Machine Learning (Week 2) Quiz ▸  Linear Regression with Multiple Variables
• Machine Learning (Week 2) Quiz ▸  Octave / Matlab Tutorial

=== Week 3 ===

• Machine Learning (Week 3) [Assignment Solution] ▸ Logistic regression and apply it to two different datasets
• Machine Learning (Week 3) Quiz ▸  Logistic Regression
• Machine Learning (Week 3) Quiz ▸  Regularization

=== Week 4 ===

• Machine Learning (Week 4) [Assignment Solution] ▸ One-vs-all logistic regression and neural networks to recognize hand-written digits.
• Machine Learning (Week 4) Quiz ▸  Neural Networks: Representation

=== Week 5 ===

• Machine Learning (Week 5) [Assignment Solution] ▸ Back-propagation algorithm for neural networks to the task of hand-written digit recognition.
• Machine Learning (Week 5) Quiz ▸  Neural Networks: Learning

=== Week 6 ===

• Machine Learning (Week 6) [Assignment Solution] ▸ Regularized linear regression to study models with different bias-variance properties.
• Machine Learning (Week 6) Quiz ▸  Advice for Applying Machine Learning
• Machine Learning (Week 6) Quiz ▸  Machine Learning System Design

=== Week 7 ===

• Machine Learning (Week 7) [Assignment Solution] ▸ Support vector machines (SVMs) to build a spam classifier.
• Machine Learning (Week 7) Quiz ▸  Support Vector Machines

=== Week 8 ===

• Machine Learning (Week 8) [Assignment Solution] ▸ K-means clustering algorithm to compress an image. ▸ Principal component analysis to find a low-dimensional representation of face images.
• Machine Learning (Week 8) Quiz ▸  Unsupervised Learning
• Machine Learning (Week 8) Quiz ▸  Principal Component Analysis

=== Week 9 ===

• Machine Learning (Week 9) [Assignment Solution] ▸ Anomaly detection algorithm to detect failing servers on a network. ▸ Collaborative filtering to build a recommender system for movies.
• Machine Learning (Week 9) Quiz ▸  Anomaly Detection
• Machine Learning (Week 9) Quiz ▸  Recommender Systems

=== Week 10 ===

• No Assignment for Week 10
• Machine Learning (Week 10) Quiz ▸  Large Scale Machine Learning

=== Week 11 ===

• No Assignment for Week 11
• Machine Learning (Week 11) Quiz ▸  Application: Photo OCR Variables

Question 5 Your friend in the U.S. gives you a simple regression fit for predicting house prices from square feet. The estimated intercept is -44850 and the estimated slope is 280.76. You believe that your housing market behaves very similarly, but houses are measured in square meters. To make predictions for inputs in square meters, what intercept must you use? Hint: there are 0.092903 square meters in 1 square foot. You do not need to round your answer. (Note: the next quiz question will ask for the slope of the new model.) i dint get answer for this could any one plz help me with it

Please comment below specific week's quiz blog post. So that I can keep on updating that blog post with updated questions and answers.

This comment has been removed by the author.

Good day Akshay, I trust that you are doing well. I am struggling to pass week 2 assignment, can you please assist me. I am desperate to pass this module and I am only getting 0%... Thank you, I would really appreat your help.

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Programming assignments, learner help center dec 5, 2022 • knowledge, article details.

Programming assignments require you to write and run a computer program to solve a problem.

Some programming assignments count toward your final course grade, while others are just for practice.

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Programming assignments include both assignment instructions and assignment parts.

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Applied Machine Learning in Python

This course is part of Applied Data Science with Python Specialization

Taught in English

Some content may not be translated

Instructor: Kevyn Collins-Thompson

Sponsored by Morocco Digital Academy by UM6P

293,179 already enrolled

(8,462 reviews)

What you'll learn

Describe how machine learning is different than descriptive statistics

Create and evaluate data clusters

Explain different approaches for creating predictive models

Build features that meet analysis needs

Skills you'll gain

• Machine Learning
• Machine Learning Algorithms
• Python Programming
• Applied Machine Learning
• Data Analysis
• Human Learning
• Statistical Programming
• Computer Programming

Details to know

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There are 4 modules in this course

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn

This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.

What's included

7 videos 4 readings 1 quiz 1 programming assignment 1 ungraded lab

7 videos • Total 75 minutes

• Introduction • 11 minutes • Preview module
• What's New? • 0 minutes
• Key Concepts in Machine Learning • 13 minutes
• Python Tools for Machine Learning • 4 minutes
• An Example Machine Learning Problem • 12 minutes
• Examining the Data • 9 minutes
• K-Nearest Neighbors Classification • 23 minutes

4 readings • Total 60 minutes

• Syllabus • 10 minutes
• Help us learn more about you! • 10 minutes
• Notice for Auditing Learners: Assignment Submission • 10 minutes
• Zachary Lipton: The Foundations of Algorithmic Bias (optional) • 30 minutes

1 quiz • Total 20 minutes

• Module 1 Quiz • 20 minutes

1 programming assignment • Total 180 minutes

• Assignment 1 • 180 minutes

1 ungraded lab • Total 60 minutes

• Module 1 Notebook • 60 minutes

Module 2: Supervised Machine Learning - Part 1

This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.

13 videos 2 readings 1 quiz 1 programming assignment 2 ungraded labs

13 videos • Total 190 minutes

• Introduction to Supervised Machine Learning • 17 minutes • Preview module
• Overfitting and Underfitting • 12 minutes
• Supervised Learning: Datasets • 4 minutes
• K-Nearest Neighbors: Classification and Regression • 13 minutes
• Linear Regression: Least-Squares • 17 minutes
• Linear Regression: Ridge, Lasso, and Polynomial Regression • 26 minutes
• Logistic Regression • 12 minutes
• Linear Classifiers: Support Vector Machines • 13 minutes
• Multi-Class Classification • 6 minutes
• Kernelized Support Vector Machines • 18 minutes
• Cross-Validation • 12 minutes
• Decision Trees • 19 minutes
• One-Hot Encoding (Optional) • 13 minutes

2 readings • Total 20 minutes

• A Few Useful Things to Know about Machine Learning • 10 minutes
• Ed Yong: Genetic Test for Autism Refuted (optional) • 10 minutes

1 quiz • Total 30 minutes

• Module 2 Quiz • 30 minutes
• Assignment 2 • 180 minutes

2 ungraded labs • Total 120 minutes

• Module 2 Notebook • 60 minutes
• Classifier Visualization Playspace • 60 minutes

Module 3: Evaluation

This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.

8 videos 2 readings 1 quiz 1 programming assignment 1 ungraded lab

8 videos • Total 112 minutes

• Model Evaluation & Selection • 22 minutes • Preview module
• Confusion Matrices & Basic Evaluation Metrics • 14 minutes
• Classifier Decision Functions • 7 minutes
• Precision-Recall and ROC Curves • 7 minutes
• Multi-Class Evaluation • 10 minutes
• Regression Evaluation • 6 minutes
• Model Selection: Optimizing Classifiers for Different Evaluation Metrics • 13 minutes
• Model Calibration (Optional) • 31 minutes
• Practical Guide to Controlled Experiments on the Web (optional) • 10 minutes
• Note on Assignment 3 • 10 minutes

1 quiz • Total 28 minutes

• Module 3 Quiz • 28 minutes
• Assignment 3 • 180 minutes
• Module 3 Notebook • 60 minutes

Module 4: Supervised Machine Learning - Part 2

This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.

10 videos 13 readings 1 quiz 1 programming assignment 2 ungraded labs

10 videos • Total 103 minutes

• Naive Bayes Classifiers • 8 minutes • Preview module
• Random Forests • 11 minutes
• Gradient Boosted Decision Trees • 5 minutes
• Neural Networks • 18 minutes
• Deep Learning (Optional) • 14 minutes
• Data Leakage • 13 minutes
• Introduction • 4 minutes
• Dimensionality Reduction and Manifold Learning • 9 minutes
• Clustering • 14 minutes
• Conclusion • 2 minutes

13 readings • Total 123 minutes

• Neural Networks Made Easy (optional) • 10 minutes
• Play with Neural Networks: TensorFlow Playground (optional) • 10 minutes
• Deep Learning in a Nutshell: Core Concepts (optional) • 10 minutes
• Assisting Pathologists in Detecting Cancer with Deep Learning (optional) • 10 minutes
• The Treachery of Leakage (optional) • 10 minutes
• Leakage in Data Mining: Formulation, Detection, and Avoidance (optional) • 10 minutes
• Data Leakage Example: The ICML 2013 Whale Challenge (optional) • 10 minutes
• Rules of Machine Learning: Best Practices for ML Engineering (optional) • 10 minutes
• How to Use t-SNE Effectively • 10 minutes
• How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms • 10 minutes
• Post-course Survey • 10 minutes
• Keep Learning with Michigan Online • 10 minutes
• Admissions Team alert about fee waiver • 3 minutes
• Module 4 Quiz • 20 minutes
• Assignment 4 • 180 minutes
• Module 4 Notebook • 60 minutes
• Unsupervised Learning Notebook • 60 minutes

Instructor ratings

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Why people choose Coursera for their career

Learner reviews

Showing 3 of 8462

8,462 reviews

Reviewed on Jun 19, 2017

Not for the faint of heart and some experience with Python, in particular Pandas, is preferred. Great overview of the different methods used in machine learning. One of the better courses imo.

Reviewed on Aug 15, 2019

- more technical materials, comparisons and better classified details should've been provided, especially to be more proportional to the assignments.

-again, subtitles were full of typos

Reviewed on Jul 7, 2020

assignments were so good. I think there was not enough information given for the quiz tests. And also the code given was not properly explained. But the materials were so good for practice

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