There are no specific requirements needed to attend this course.
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Introduction to Python
Introduction
- Installing Python
- Numbers
- Strings
- Slicing up Strings
- Lists
- Installing PyCharm
Conditional Statements
- if elif else
Iterations
- for
- Range and While
- Comments and Break
- Continue
Functions
- Functions
- Return Values
- Default Values for Arguments
- Variable Scope
- Keyword Arguments
- Flexible Number of Arguments
- Unpacking Arguments
- My trip to Walmart and Sets
- Dictionary
- Modules
Playing with Requests and Files
- Download an Image from the Web
- How to Read and Write Files
- Downloading Files from the Web
Exceptions
Object Oriented Programs
- Classes and Objects
- init
- Class vs Instance Variables
- Inheritance
- Multiple Inheritance
- threading
Playing around with Python
- Unpack List or Tuples
- Zip (and yeast infection story)
- Lamdba
- Min, Max, and Sorting Dictionaries
- Pillow
- Cropping Images
- Combine Images Together
- Getting Individual Channels
- Awesome Merge Effect
- Basic Transformations
- Modes and Filters
- struct
- map
- Bitwise Operators
- Finding Largest or Smallest Items
- Dictionary Calculations
- Finding Most Frequent Items
- Dictionary Multiple Key Sort
- Sorting Custom Objects
Add Ons:
- Database Connectivity and Querying for MySQL
- Quick look into Regular Expressions
- Playing around with REST API
Writing a Web Crawler
Machine Learning Concepts
Introduction to Applied Machine Learning
- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off
Machine Learning with Python
- Choice of libraries
- Add-on tools
Regression
- Linear regression
- Generalizations and Nonlinearity
- Exercises
Classification
- Bayesian refresher
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
- Exercises
Cross-validation and Resampling
- Cross-validation approaches
- Bootstrap
- Exercises
Unsupervised Learning
- K-means clustering
- Examples
- Challenges of unsupervised learning and beyond K-means