IIRF Online > Development > Data Science > Machine Learning > Introduction to Machine Learning for Data Science
Introduction to Machine Learning for Data Science by Udemy
A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.
Course Highlights
- Genuinely understand what Computer Science, Algorithms, Programming, Data, Big Data, Artificial Intelligence, Machine Learning, and Data Science is.
- To understand how these different domains fit together, how they are different, and how to avoid the marketing fluff.
- The Impacts Machine Learning and Data Science is having on society.
- To really understand computer technology has changed the world, with an appreciation of scale.
- To know what problems Machine Learning can solve, and how the Machine Learning Process works.
- How to avoid problems with Machine Learning, to successfully implement it without losing your mind!
Skills you will learn!
Curriculum
7 Topics
Course Promotion Video
A special message for hard of hearing and ESL students
Thank you for investing in this Course!
Course Overview
Secret sauce inside!: How to get the most out of this course.
Course Links Reference Guide and Lecture Resources
Course Survey
13 Topics
Core Concepts Overview
Computer Science - the `Train Wreck' Definition
What's Data / "I can see data everywhere!"
Structured vs Unstructured Data
Structured and Unstructured Data
Computer Science - Definition Revisited & The Greatest "lie" ever SOLD....
What's big data?
Big Data - Quiz
What is Artificial Intelligence (AI)
What is Machine Learning? - Part 1 - The ideas
What is Machine Learning? - Part 2 - An Example
What is data science?
Recap & How do these relate to each other?
5 Topics
Impacts Importance and examples - Overview
Why is this important now?
Computers exploding! - The explosive growth of computer power explained.
What problems does Machine Learning Solve?
Where it's transforming our lives
8 Topics
The Machine Learning Process - Overview
5 Step Machine Learning Process Overview
1 - Asking the right question
2 - Identifying obtaining and preparing the right data
3 - Identifying and applying a ML Algorithm
4 - Evaluating the performance of the model and adjusting
5 - Using and presenting the model
Machine Learning - Process
9 Topics
How to apply Machine Learning for Data Science - Overview
Where to begin your journey
Common platforms and tools for Data Science
Data Science using - R
Data Science using - Python
Data Science using SQL
Data Science using Excel
Data Science using RapidMiner
Cautionary Tales
1 Topic
All done! What's next?
3 Topics
Introduction and Anaconda Installation
What will we cover!
Introduction and Setup
4 Topics
Crash course in Python - Beginning concepts
Crash course in Python - Strings Slices and Lists!
Crash course in Python - Expressions Operators Conditions and Loops
Crash course in Python - Functions Scope Dictionaries and more!
1 Topic
Hands on Running Python
3 Topics
Foundations of Machine Learning and Data Science - Definitions and concepts.
Foundations of Machine Learning and Data Science - Machine Learning Workflow
Foundations of Machine Learning and Data Science - Algorithms concepts and more
3 Topics
Introducing the essential modules for Machine Learning and NumPy Basics
Pandas and Matplotlib
Analysis using Pandas plotting in Matplotlib intro to SciPy and Scikit-learn
6 Topics
A Titanic Example - Getting our start.
A Titanic Example - Understanding the data set.
A Titanic Example - Understanding the data set in regards to survival
A Titanic Example - Preparing the right data and applying a basic algorithm
A Titanic Example - Applying regression algorithms.
A Titanic Example - Applying Decision Trees (example of overfit and underfit)
1 Topic
Conclusions - for our Titanic Example important concepts and where to go next!
1 Topic
Bonus Article - The startling breakthrough in Machine Learning from 2016.
Introduction to Machine Learning for Data Science
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