About this course: 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 in 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. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.
Who is this class for: This course is primarily for individuals who are passionate about the field of data science and who are aspiring to apply machine learning in their business, industry or research.
Created by: IBM
Taught by: SAEED AGHABOZORGI, Ph.D., Sr. Data Scientist
IBM Developer Skills Network
| Basic Info | Course 8 of 9 in the IBM Data Science Professional Certificate Specialization |
| Level | Intermediate |
| Commitment | 5-6 weeks of study, 3-6 hours per week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Introduction to Machine Learning
In this week, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You’ll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.
4 videos
- Video: Introduction to Machine Learning
- Video: Python for Machine Learning
- Video: Supervised vs Unsupervised
Graded: Intro to Machine Learning
WEEK 2
Regression
In this week, you will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy.
6 videos
- Video: Simple Linear Regression
- Video: Model Evaluation in Regression Models
- Video: Evaluation Metrics in Regression Models
- LTI Item: Lab: Simple Linear Regression
- Video: Multiple Linear Regression
- LTI Item: Lab: Multiple Linear Regression
- Video: Non-Linear Regression
- LTI Item: Lab: Polynomial Regression
- LTI Item: Lab: Non-linear Regression
Graded: Regression
WEEK 3
Classification
In this week, you will learn about classification technique. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, you learn about pros and cons of each method, and different classification accuracy metrics.
9 videos
- Video: K-Nearest Neighbours
- Video: Evaluation Metrics in Classification
- LTI Item: Lab: KNN
- Video: Introduction to Decision Trees
- Video: Building Decision Trees
- LTI Item: Lab: Decision Trees
- Video: Intro to Logistic Regression
- Video: Logistic regression vs Linear regression
- Video: Logistic Regression Training
- LTI Item: Lab: Logistic Regression
- Video: Support Vector Machine
- LTI Item: Lab: SVM (Support Vector Machines)
Graded: Classification
WEEK 4
Clustering
In this section, you will learn about different clustering approaches. You learn how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations. You understand 3 main types of clustering, including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering.
6 videos, 1 reading
- Video: Intro to k-Means
- Video: More on k-Means
- LTI Item: Lab: k-Means
- Video: Intro to Hierarchical Clustering
- Video: More on Hierarchical Clustering
- LTI Item: Lab: Agglomerative clustering
- Video: DBSCAN
- LTI Item: Lab: DBSCAN Clustering
- Reading: IBM Digital Badge
Graded: Clustering
WEEK 5
Recommender Systems
In this module, you will learn about recommender systems. First, you will get introduced with main idea behind recommendation engines, then you understand two main types of recommendation engines, namely, content-based and collaborative filtering.
3 videos
- Video: Content-based Recommender Systems
- LTI Item: Lab: Content-based Recommendation Systems
- Video: Collaborative Filtering
- LTI Item: Lab: Collaborative Filtering on Movies
Graded: Recommender System
WEEK 6
Final Project
In this module, you will do a project based of what you have learned so far. You will submit a report of your project for peer evaluation.
2 videos, 2 readings
- Reading: How to do the final project?
- LTI Item: OPTIONAL: Applying IBM Cloud Promo Code
- Video: OPTIONAL: Sharing Notebooks on Watson Studio
- Reading: IBM Digital Badge
Graded: The best classifier
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creators
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
Pricing
| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Ratings and Reviews
very good
very nice course .
Very good course...
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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.