Fractal Analytics
Foundations of Machine Learning
Fractal Analytics

Foundations of Machine Learning

This course is part of Fractal Data Science Professional Certificate

Taught in English

Some content may not be translated

Analytics Vidhya

Instructor: Analytics Vidhya

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Beginner level

Recommended experience

25 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Construct Machine Learning models using the various steps of a typical Machine Learning Workflow

  • Apply appropriate metrics for various business problems to assess the performance of Machine Learning models

  • Develop regression and tree based Machine learning  Models to make predictions on relevant business problems

  • Analyze  business problems where unsupervised Machine Learning models  could be used to derive value from data

Details to know

Shareable certificate

Add to your LinkedIn profile

Recently updated!

January 2024

Assessments

5 quizzes, 7 assignments

Course

Gain insight into a topic and learn the fundamentals

Beginner level

Recommended experience

25 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

Placeholder

Build your Data Analysis expertise

This course is part of the Fractal Data Science Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
  • 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 from Fractal Analytics
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 6 modules in this course

In this module, learners will unravel the magic of machine learning as they explore the significance of making predictions in various domains. They will gain a solid introduction to machine learning and its applications in different industries. The module will also cover essential concepts such as rule-based prediction and evaluation metrics, providing learners with a strong foundation for the rest of the course.

What's included

11 videos2 readings1 assignment1 discussion prompt

This module focuses on guiding learners through the complete workflow of building their first machine learning model. Learners will dive into data preparation, exploratory data analysis (EDA), and feature engineering techniques. They will learn to build a K-Nearest Neighbors (KNN) model, understand model evaluation, and explore crucial considerations for deploying an ML model in real-world applications.

What's included

19 videos1 quiz1 assignment1 programming assignment

In this module, learners will delve into the intricacies of prediction models. They will explore evaluation metrics for both regression and classification models, gaining hands-on experience with practical implementations. The module will also cover data division techniques and benchmark performance, providing learners with a comprehensive understanding of how to effectively evaluate prediction models.

What's included

10 videos1 quiz1 assignment1 programming assignment

In this module, learners will embark on a comprehensive exploration of regression techniques. From understanding the principles of linear and logistic regression to their practical application, they will gain valuable insights into predictive modeling. With a focus on real-world scenarios, they will learn how to make predictions, interpret results, and optimize models.

What's included

13 videos1 quiz2 assignments1 programming assignment

In this module, learners will navigate the intricate paths of decision trees. Decision trees offer a transparent yet powerful approach to classification and regression tasks. Learners will delve into the mechanisms of decision tree construction, learn to handle overfitting through pruning and regularization, and discover the art of fine-tuning decision trees for optimal results.

What's included

10 videos1 quiz1 assignment1 programming assignment

In this module, learners will unlock the mysteries of unsupervised machine learning as they dive into clustering techniques. They will discover the power of KMeans and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) in grouping similar data points together. They will also explore how unsupervised learning revolutionizes data exploration, customer segmentation, and anomaly detection.

What's included

11 videos1 reading1 quiz1 assignment1 programming assignment

Instructor

Analytics Vidhya
Fractal Analytics
4 Courses2,652 learners

Offered by

Recommended if you're interested in Data Analysis

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

New to Data Analysis? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions