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.
LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.
This course is part of the IBM Data Science Professional Certificate Specialization
Machine Learning with Python
Syllabus - What you will learn from this course
Week
11 hour to complete
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 (Total 24 min), 1 quiz
4 videos
Welcome3m
Introduction to Machine Learning8m
Python for Machine Learning6m
Supervised vs Unsupervised5m
1 practice exercise
Intro to Machine Learning10m
Week
25 hours to complete
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 (Total 50 min), 5 quizzes
6 videos
Simple Linear Regression12m
Model Evaluation in Regression Models8m
Evaluation Metrics in Regression Models3m
Multiple Linear Regression13m
Non-Linear Regression7m
1 practice exercise
Regression10m
Week
35 hours to complete
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 (Total 81 min), 5 quizzes
9 videos
K-Nearest Neighbours9m
Evaluation Metrics in Classification7m
Introduction to Decision Trees4m
Building Decision Trees10m
Intro to Logistic Regression7m
Logistic regression vs Linear regression15m
Logistic Regression Training13m
Support Vector Machine8m
1 practice exercise
Classification10m
Week
44 hours to complete
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 (Total 41 min), 1 reading, 4 quizzes
6 videos
Intro to k-Means9m
More on k-Means3m
Intro to Hierarchical Clustering6m
More on Hierarchical Clustering5m
DBSCAN6m
1 reading
IBM Digital Badge2m
1 practice exercise
Clustering10m
4.7
99 Reviews20%
started a new career after completing these courses
33%
got a tangible career benefit from this course
Top Reviews
By RC•Feb 7th 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.
By JJ•Dec 6th 2018
I am happy to have this online education, I drop out my nuclear engineering degree, I am happy to learn practical things with future... I work for IBM also...but I want to become a data scientis
Instructor
SAEED AGHABOZORGI
Ph.D., Sr. Data ScientistIBM Developer Skills Network
About 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.
About the IBM Data Science Professional Certificate Specialization
Data Science has been ranked as one of the hottest professions and the demand for data practitioners is booming. This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Data Science or Machine Learning.
This program consists of 9 courses providing you with latest job-ready skills and techniques covering a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets.
It is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning. No prior computer science or programming knowledge is necessary. We start small, re-enforce applied learning, and build up to more complex topics.
Upon successfully completing these courses you will have done several hands-on assignments and built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in Data Science. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Data Science.
LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
More questions? Visit the Learner Help Center.
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