Machine Learning Deep Dive: Business Applications and Coding Walkthroughs
Build the bridge between theoretical ML knowledge and its practical application: apply machine learning to solve complex business problems
Start for free
What you get:
- 3 hours of content
- 9 Interactive exercises
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
Machine Learning Deep Dive: Business Applications and Coding Walkthroughs
Start for free
What you get:
- 3 hours of content
- 9 Interactive exercises
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
Start for free
What you get:
- 3 hours of content
- 9 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 seamlessly connect theoretical concepts with real-world applications
- Learn business applications of various algorithms—from linear regression to neural networks
- Improve your understanding of theoretical ML concepts and get ready for advanced studies and applications
- Boost your coding skills by going through the complete coding walkthroughs prepared by a world-class senior data scientist
- Acquire insights on selecting the appropriate ML algorithms for specific business scenarios
- 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
Do you wish to learn how companies and non-profit organizations use Machine Learning? Are you interested in the practical coding aspects of building a Machine Learning model? If so, this is the perfect course for you. This course wraps up Ken Jee and Jeff Li’s series on Machine Learning. First, they showed you how the ML Process works in practice; then, they explained the fundamentals of the most popular Machine Learning algorithms used in the data science world. Now it’s time to consider the practical application of ML models and learn how to build them independently. In this course, Ken and Jeff will help you understand how big tech firms and small businesses can use different ML algorithms to boost their results. They’ll consider more straightforward methods which don’t require much data and computational power, such as linear regression, logistic regression, and SVMs. Still, they’ll also discuss several advanced use cases of neural networks and collaborative filtering. The coding walkthroughs section is a rare opportunity to gain practical intuition and see how an experienced data scientist builds an algorithm from scratch. This allows you to better grasp the theoretical concepts and logic studied earlier. You’ll learn to tackle real-world challenges when working on ML problems and develop essential debugging and problem-solving techniques.
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1.1 Linear Regression
3 min
1.2 Logistic Regression
6 min
1.3 Random Forest
3 min
1.4 K-Means Clustering
2 min
1.5 K-Nearest Neighbors
2 min
1.6 Hierarchical Clustering
2 min
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
- 1. ML Business Use Cases9 Lessons 27 Min
In this section, Ken and Jeff walk you through the real-world application of several ML algorithms. You’ll learn how companies use them to improve performance and why specific algorithms work better in particular situations.
Linear Regression3 minLogistic Regression6 minRandom Forest3 minK-Means Clustering2 minK-Nearest Neighbors2 minHierarchical Clustering2 minSupport Vector Machines2 minArtificial Neural Networks4 minCollaborative Filtering3 min - 2. Coding Walkthroughs25 Lessons 162 Min
This is an opportunity to witness how an experienced data scientist builds ML algorithms from scratch. In the process, you can improve your coding skills and build a bridge between theoretical understanding and practical applications of ML algorithms.
Introduction1 minLinear Regression - First Part1 minLinear Regression - Second Part4 minLinear Regression - Third Part8 minLogistic Regression10 minDecision Trees - First Part3 minDecision Trees - Second Part23 minDecision Trees - Third Part13 minRandom Forest - First Part6 minRandom Forest - Second Part3 minGradient Boost - First Part4 minGradient Boost - Second Part5 minKNN - First Part5 minKNN - Second Part6 minK-Means Clustering - First Part2 minK-Means Clustering - Second Part11 minHierarchical Clustering - First Part4 minHierarchical Clustering - Second Part10 minSVM9 minNeural Network - First Part7 minNeural Network - Second Part6 minNeural Network - Third Part2 minNMF - First Part6 minNMF - Second Part3 minNaïve Bayes10 min
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 Algorithms A-Z and Machine Learning Process A-Z courses first
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.
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