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Data for Machine Learning

Approx. 12 hours to complete

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Course Summary

This course teaches the fundamentals of machine learning and data analysis. It covers topics such as linear regression, classification, clustering, and neural networks, and provides hands-on experience with real-world datasets.

Key Learning Points

Learn the basics of machine learning and data analysis
Gain hands-on experience with real-world datasets
Understand linear regression, classification, clustering, and neural networks

Job Positions & Salaries of people who have taken this course might have

- USA: $62,453
- India: ₹4,69,237
- Spain: €36,310
- USA: $62,453
- India: ₹4,69,237
- Spain: €36,310
- USA: $120,931
- India: ₹15,00,000
- Spain: €50,000
- USA: $62,453
- India: ₹4,69,237
- Spain: €36,310
- USA: $120,931
- India: ₹15,00,000
- Spain: €50,000
- USA: $113,309
- India: ₹12,00,000
- Spain: €38,000

Learning Outcomes

Understand the fundamentals of machine learning and data analysis
Gain hands-on experience with real-world datasets
Build and evaluate machine learning models

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming in Python
Familiarity with linear algebra and statistics

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures
Hands-on projects

Similar Courses

Applied Data Science with Python
Machine Learning
Data Mining

Related Education Paths

Notable People in This Field

Andrew Ng
Yann LeCun
Fei-Fei Li

Related Books

Description

This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to:

Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

Outline

What Does Good Data look like?
Introduction to the Course
Business Understanding and Problem Discovery
No Free Lunch Theorem
Exploring the process of problem definition
Data Acquisition and Understanding
Metadata Matters
Dealing with Multimodal Data
Features and transformations of raw data
Identifying Data from Problem
Case Study: Problem from Data
Weekly Summary What does good data look like?
Machine Learning Process Lifecycle Review
Match Data to the needs of the learning Algorithm
Business Understanding and Problem Discovery (BUPD) Review
Data Acquisition and Understanding Review
Module 1 Quiz

Preparing your Data for Machine Learning Success
Data Warehousing
Converting to Useful Forms
Data Quality
How Much Data Do I Need?
Everything has to be Numbers
Types of Data
Aligning Similar Data
Imputing Missing Values
Data Transformations
Weekly Summary: Preparing your Data for Machine Learning Success
Data Cleaning: Everybody's favourite task
Data Warehousing Review
Everything has to be Numbers Review
Types of Data Review
Module 2 Quiz

Feature Engineering for MORE Fun & Profit
What are the simplest Features to try
Useful/Useless Features
How Many Features?
What is Unsupervised Learning
Feature Selection
Feature Extraction
Transfer Learning
Weekly Summary: Feature Engineering for MORE Fun & Profit
Possibilities for Text Features
Word Embeddings
Understanding Features
Building Good Features
Understanding Transfer Learning

Bad Data
Imbalanced Data
Generalization and how machines actually learn
Bias in Data Sources
Bias and variance tradeoff
Outliers
Skewed Distributions
Badness Multipliers
Live Data Danger
Weekly Summary: Bad Data
Mistakes Computers Make
Data: Skewed Distributions
Live Data Dangers
Module 4 Quiz

Summary of User Reviews

Discover the world of data and machine learning with this comprehensive course from Coursera. Users have praised the course for its in-depth content and practical approach. With an overall positive rating from many users, this course is definitely worth checking out.

Key Aspect Users Liked About This Course

in-depth content

Pros from User Reviews

Practical approach to learning
Many real-world examples
Great for beginners
Good pacing
Engaging lectures

Cons from User Reviews

Some technical issues reported
Not suitable for advanced learners
Lack of interaction with instructors
Limited scope in certain areas
Requires a strong foundation in math

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