Machine Learning Deep Dive: Business Applications and Coding Walkthroughs

with Jeff Li and Ken Jee
4.8/5
(150)

Build the bridge between theoretical ML knowledge and its practical application: apply machine learning to solve complex business problems

3 hours of content 1545 students

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 3 hours of content
  • 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

A course by Jeff Li and Ken Jee

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 3 hours of content
  • Interactive exercises
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 3 hours of content
  • 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.

Learn for Free

Linear Regression

1.1 Linear Regression

3 min

Logistic Regression

1.2 Logistic Regression

6 min

Random Forest

1.3 Random Forest

3 min

K-Means Clustering

1.4 K-Means Clustering

2 min

Curriculum

  • 1. ML Business Use Cases
    9 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 Regression
    3 min
    Logistic Regression
    6 min
    Random Forest
    3 min
    K-Means Clustering
    2 min
    K-Nearest Neighbors
    2 min
    Hierarchical Clustering
    2 min
    Support Vector Machines
    2 min
    Artificial Neural Networks
    4 min
    Collaborative Filtering
    3 min
  • 2. Coding Walkthroughs
    25 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.

    Introduction
    1 min
    Linear Regression - First Part
    1 min
    Linear Regression - Second Part
    4 min
    Linear Regression - Third Part
    8 min
    Logistic Regression
    10 min
    Decision Trees - First Part
    3 min
    Decision Trees - Second Part
    23 min
    Decision Trees - Third Part
    13 min
    Random Forest - First Part
    6 min
    Random Forest - Second Part
    3 min
    Gradient Boost - First Part
    4 min
    Gradient Boost - Second Part
    5 min
    KNN - First Part
    5 min
    KNN - Second Part
    6 min
    K-Means Clustering - First Part
    2 min
    K-Means Clustering - Second Part
    11 min
    Hierarchical Clustering - First Part
    4 min
    Hierarchical Clustering - Second Part
    10 min
    SVM
    9 min
    Neural Network - First Part
    7 min
    Neural Network - Second Part
    6 min
    Neural Network - Third Part
    2 min
    NMF - First Part
    6 min
    NMF - Second Part
    3 min
    Naïve Bayes
    10 min

Topics

machine learningdata preprocessingData modelingMachine Learning ProcessModel EvaluationDealing with Imbalanced DataExploratory Data AnalysisCross ValidationFeature Engineering

Tools & Technologies

python

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.

Exams and certification

Meet Your Instructor

Jeff Li

Jeff Li

Senior Data Scientist at

3 Courses

1104 Reviews

17451 Students

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

365 Data Science Is Featured at

Our top-rated courses are trusted by business worldwide.