Introduction to Machine Learning
This course is part of multiple programs.
- Taught in English
- 19 Languages Available
- Some content may not be translated
Instructor:
Kirill Eremenko
Financial aid available
282,513 already enrolled
Course
Gain insight into a topic and learn the fundamentals
- 4.7
- (11,910 reviews)
- 96%
Beginner Level
Recommended experience
8 hours (approximately)
Flexible schedule
Learn at your own pace
What you'll learn
- Master Machine Learning on Python & R
- Make accurate predictions
- Make robust Machine Learning models
- Use Machine Learning for personal purpose
- Handle advanced techniques like Dimensionality Reduction
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem
- Have a great intuition of many Machine Learning models
- Make powerful analysis
- Create strong added value to your business
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Know which Machine Learning model to choose for each type of problem
Skills you'll gain
Artificial Intelligence, Data science, Machine Learning, Deep Learning
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
10 quizzes
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Learn more about IAEPT for Business
Build your subject-matter expertise
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
Over 1 Million students world-wide trust this course.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 3 modules in this course
This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:
- Part 1 – Data Preprocessing
- Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- How should we define AI?
- Related fields
- Philosophy of AI
- Search and problem solving
- Solving problems with AI
- Search and games
- Odds and probability
- The Bayes rule
- Naive Bayes classification
- The types of machine learning
- The nearest neighbor classifier
- Regression
- Neural network basics
- How neural networks are built
- Advanced neural network techniques
- About predicting the future
- The societal implications of AI
- Summary
Instructor Profile
- Instructor ratings
- 4.6 (3,466 ratings)
Instructor:
Kirill Eremenko
44 Courses, 2,095,931 learners
Offered by
Recommended if you're interested in Machine Learning
Why people choose IEAPT for their career
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Learner reviews
- 4.7 11,910 reviews
Reviewed on Sep 12, 2023
I found this course very approachable and informative. My background is in psychology and I was able to follow along and complete the course. I now feel much more aware of the current state of AI.
