Python Workshop
(PYTHON-WRK.AJ1)
/ ISBN: 978-1-64459-411-7
This course includes
Lessons
LiveLab
Python Workshop
The Python Workshop course focuses on building up your practical skills so that you can build up your machine learning skills as a data scientist, write scripts that help automate your life and save you time, or even create your own games and desktop applications. You'll learn from real examples that lead to real results. It contains interactive lessons with knowledge checks and quizzes, videos covering detailed exercises, activities, and their guided solutions, and hands-on labs to build and iterate on your code like a software developer.
Lessons
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12+ Lessons
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100+ Quizzes
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75+ Flashcards
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75+ Glossary of terms
LiveLab
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44+ LiveLab
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19+ Video tutorials
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01:06+ Hours
- About the Course
- Introduction
- Vital Python
- Numbers: Operations, Types, and Variables
- Python as a Calculator
- Strings: Concatenation, Methods, and input()
- String Interpolation
- String Indexing and Slicing
- Slicing
- Booleans and Conditionals
- Loops
- Summary
- Introduction
- The Power of Lists
- Matrix Operations
- List Methods
- Dictionary Keys and Values
- Dictionary Methods
- Tuples
- A Survey of Sets
- Choosing Types
- Summary
- Introduction
- Python Scripts and Modules
- Python Algorithms
- Basic Functions
- Iterative Functions
- Recursive Functions
- Dynamic Programming
- Helper Functions
- Variable Scope
- Lambda Functions
- Summary
- Introduction
- Reading Files
- Writing Files
- Preparing for Debugging (Defensive Code)
- Plotting Techniques
- The Don'ts of Plotting Graphs
- Summary
- Introduction
- Classes and Objects
- Defining Classes
- The __init__ method
- Methods
- Properties
- Inheritance
- Summary
- Introduction
- The Importance of the Standard Library
- Dates and Times
- Interacting with the OS
- Using the subprocess Module
- Logging
- Collections
- Functools
- Summary
- Introduction
- Using List Comprehensions
- Set and Dictionary Comprehensions
- Default Dictionary
- Iterators
- Itertools
- Generators
- Regular Expressions
- Summary
- Introduction
- Debugging
- Automated Testing
- Creating a PIP Package
- Creating Documentation the Easy Way
- Source Management
- Summary
- Introduction
- Developing Collaboratively
- Dependency Management
- Deploying Code into Production
- Multiprocessing
- Parsing Command-Line Arguments in Scripts
- Performance and Profiling
- Profiling
- Summary
- Introduction
- NumPy and Basic Stats
- Matrices
- The pandas Library
- Data
- Null Values
- Visual Analysis
- Summary
- Introduction
- Introduction to Linear Regression
- Cross-Validation
- Regularization: Ridge and Lasso
- K-Nearest Neighbors, Decision Trees, and Random Forests
- Classification Models
- Boosting Methods
- Summary
Hands on Activities (Live Labs)
- Assigning Values to a Variable
- Determining the Pythagorean Distance Between Three Points
- Displaying Strings
- Using the input() Function
- Using the if-else Syntax
- Finding the LCM (Least Common Multiple)
- Using the for Loop
- Using a Nested List to Store Employee Data
- Implementing Matrix Operations
- Accessing an Item from a List
- Adding Items to a List
- Storing Company Employee Table Data Using a List and a Dictionary
- Implementing Set Operations
- Writing and Executing a Script
- Finding the Maximum Number Using Pseudocode
- Using Bubble Sort in Python
- Implementing Linear Search in Python
- Implementing Binary Search in Python
- Checking Whether a Number is a Prime
- Finding the Factorial of a Number Using Recursion
- Reading a Text File Using Python
- Drawing a Scatter Plot to Study the Data
- Creating a Pie Chart
- Generating a Density Plot
- Visualizing the Titanic Dataset Using a Pie Chart and Bar Plot
- Creating a Class
- Using the init Method
- Implementing Inheritance
- Comparing datetime across Time Zones
- Calculating the Time Delta between Two datetime Objects
- Building a Scorecard Using Dictionary Comprehension and Multiple Lists
- Implementing the __iter__() Method
- Using Regular Expressions to Replace Text
- Using Regular Expressions to Find Winning Customers
- Debugging Sample Python Code for an Application
- Checking Sample Code with Unit Testing
- Using the Multiprocessing Package
- Introducing argparse to Accept Input from the User
- Finding the Mean and Median from a Collection of Income Data
- Using DataFrames to Manipulate Data
- Reading and Viewing the Boston Housing Dataset
- Performing Visual Data Analysis
- Using Linear Regression to Predict the Accuracy of the Median Values of a Dataset
- Using Machine Learning to Predict Customer Return Rate Accuracy
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