11.12.2024
The Machine Learning Algorithms A-Z
Master the core concepts of popular ML algorithms: understand when and how to apply different machine learning techniques effectively
5 hours of content
8276 students
$99.00
14-Day Money-Back Guarantee
What you get:
- 5 hours of content
- 108 Interactive exercises
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
$99.00
$99.00
14-Day Money-Back Guarantee
What you get:
- 5 hours of content
- 108 Interactive exercises
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
What You Learn
- Acquire machine learning skills that bridge theoretical knowledge and practical application
- Gain insight into the strength and limitations of various machine learning models
- Build confidence in selecting the optimal machine learning algorithm needed for specific use cases
- Understand fundamental machine learning theory that will help you achieve outstanding real-world results
- Acquire the business intuition to discern whether a problem requires machine learning or can be solved through simpler analytical methods
- Improve your career prospects with in-demand machine learning skills, essential for your success in an AI-driven world
Top Choice of Leading Companies Worldwide
Industry leaders and professionals globally rely on this top-rated course to enhance their skills.
Course Description
Looking to break into machine learning? Need to review the ins and outs of each algorithm? Preparing for an interview? Curious to see how these algorithms are applied to business? As ML practitioners, the true value of ML is not in memorizing complicated formulas. It’s not using the latest, greatest deep learning architecture. It’s knowing when to use an algorithm and how to maximize the impact of that model. It’s knowing how to use them to solve REAL business problems. The true value of ML is not ML. It’s solving important business problems. Whether you need to build a forecasting model that forms the backbone of the ads business, builld a recommender system powering millions of purchases or build a fraud detection system that catches bad apples, this course will arm you with both the ML knowledge AND the know-how on how to apply it to your business problem. We don’t want you to leave this course just knowing ML. We want you to leave this course to leave as ML practitioners.
Learn for Free
1.1 Introduction
1.2 ML Algorithms course - GitHub repository
1.3 How to Use this Course
1.4 Types of ML Problems
1.6 Additional Resources
2.1 Linear Regression
Interactive Exercises
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Curriculum
Topics
Course Requirements
- You need to complete an introduction to Python before taking this course
- Basic skills in statistics, probability, and linear algebra are required
- It is highly recommended to take the Machine Learning in Python course first
- You will need to install the Anaconda package, which includes Jupyter Notebook
Who Should Take This Course?
Level of difficulty: Advanced
- Aspiring data scientists and ML engineers
- Existing data scientists and ML engineers who want to boost their skills and learn from world-class experts
Exams and Certification
A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.
Meet Your Instructor
Jeff is a senior data scientist at a large music streaming platform and is focused on forecasting problems surrounding ads. He got into data science by trying to earn a living playing poker and previously spent two years at Door Dash on their core ML team (he was working on a wide variety of problems such as improving experimentation power, personalization, and supply/demand forecasting). In his courses with 365, Jeff is willing to share valuable practical insights he learned on the job. Prior to starting his data science career, Jeff worked in technology consulting. He graduated the University of Southern California.
What Our Learners Say
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