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Object Oriented Program

(PS-PYTHON-R.AE1)
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1

Starting with Python

  • Why Python Is Hot
  • Choosing the Right Python
  • Tools for Success
  • Writing Python in VS Code
  • Using Jupyter Notebook for Coding
2

Interactive Mode, Getting Help, and Writing Apps

  • Using Python's Interactive Mode
  • Creating a Python Development Workspace
  • Creating a Folder for Your Python Code
  • Typing, Editing, and Debugging Python Code
  • Writing Code in a Jupyter Notebook
3

Python Elements and Syntax

  • The Zen of Python
  • Introducing Object-Oriented Programming
  • Discovering Why Indentations Count, Big Time
  • Using Python Modules
4

Building Your First Python Application

  • Opening the Python App File
  • Typing and Using Python Comments
  • Understanding Python Data Types
  • Working with Python Operators
  • Creating and Using Variables
  • Understanding What Syntax Is and Why It Matters
  • Putting Code Together
5

Working with Numbers, Text, and Dates

  • Calculating Numbers with Functions
  • Still More Math Functions
  • Formatting Numbers
  • Grappling with Weirder Numbers
  • Manipulating Strings
  • Uncovering Dates and Times
  • Accounting for Time Zones
  • Working with Time Zones
6

Controlling the Action

  • Main Operators for Controlling the Action
  • Making Decisions with if
  • Repeating a Process with for
  • Looping with while
7

Speeding Along with Lists and Tuples

  • Defining and Using Lists
  • What's a Tuple and Who Cares?
  • Working with Sets
8

Cruising Massive Data with Dictionaries

  • Understanding Data Dictionaries
  • Creating a Data Dictionary
  • Looping through a Dictionary
  • Data Dictionary Methods
  • Copying a Dictionary
  • Deleting Dictionary Items
  • Having Fun with Multi-Key Dictionaries
9

Wrangling Bigger Chunks of Code

  • Creating a Function
  • Commenting a Function
  • Passing Information to a Function
  • Returning Values from Functions
  • Unmasking Anonymous Functions
10

Doing Python with Class

  • Mastering Classes and Objects
  • Creating a Class
  • Creating an Instance from a Class
  • Giving an Object Its Attributes
  • Giving a Class Methods
  • Understanding Class Inheritance
11

Sidestepping Errors

  • Understanding Exceptions
  • Handling Errors Gracefully
  • Being Specific about Exceptions
  • Keeping Your App from Crashing
  • Adding an else to the Mix
  • Using try … except … else … finally
  • Raising Your Own Exceptions
12

Working with External Files

  • Understanding Text and Binary Files
  • Opening and Closing Files
  • Reading a File’s Contents
  • Looping through a File
  • Reading and Copying a Binary File
  • Conquering CSV Files
  • Converting from CSV to Objects and Dictionaries
13

Juggling JSON Data

  • Organizing JSON Data
  • Understanding Serialization
  • Loading Data from JSON Files
  • Dumping Python Data to JSON
14

Interacting with the Internet

  • Seeing How the Web Works
15

Libraries, Packages, and Modules

  • Understanding the Python Standard Library
  • Exploring Python Packages
  • Importing Python Modules
  • Making Your Own Modules
16

Exploring Artificial Intelligence

  • AI Is a Collection of Techniques
  • Current Limitations of AI
17

Building a Neural Network

  • Understanding Neural Networks
  • Building a Simple Neural Network in Python
  • Building a Python Neural Network in TensorFlow
18

Doing Machine Learning

  • Learning by Looking for Solutions in All the Wrong Places
  • Creating a Machine-Learning Network for Detecting Clothes Types
  • Visualizing with MatPlotLib
  • Learning More Machine Learning
19

Exploring AI

  • Limitations of the Raspberry Pi and AI
  • Adding Hardware AI to the Raspberry Pi
  • AI in the Cloud
  • AI on a Graphics Card
  • Where to Go for More AI Fun in Python
20

Understanding the Five Areas of Data Science

  • Working with Big, Big Data
  • Cooking with Gas: The Five-Step Process of Data Science
21

Exploring Big Data

  • Introducing NumPy, Pandas, and MatPlotLib
  • Doing Your First Data Science Project
22

Using Big Data from Google Cloud

  • What Is Big Data?
  • Understanding Google Cloud and BigQuery
  • Reading the Medicare Big Data
  • Looking for the Most Polluted City in the World on an Hourly Basis
23

Introducing Physical Computing

  • Physical Computing Is Fun
  • What Is a Raspberry Pi?
  • Building Projects That Move and Sense the Environment
  • Sensing the Environment with the Raspberry Pi
  • Controlling an LED with Python
  • But Wait, There's More
24

No Soldering! Using Grove Connectors for Building

  • Working with the Grove System
  • Grove Connectors
  • Connecting with Grove Cables
25

Sensing the World

  • Understanding I2C
  • Measuring Oxygen and a Flame
  • Building a Dashboard on Your Phone with Blynk
  • Where to Go from Here
26

Making Things Move

  • Exploring Electric Motors
  • Controlling a DC Motor
  • Running a Servo Motor
  • Making a Stepper Motor Step
27

Introducing Robotics

  • A Robot Is Not Always Like a Human
  • Not Every Robot Has Arms or Wheels
  • Understanding the Main Parts of a Robot
  • Programming Robots
28

Building Your First Python Robot

  • Introducing the Mars Rover PiCar-B
  • Assembling the Robot
  • Testing Your Robot
29

Programming Your Robot Rover

  • Building a Simple, High-Level Python Interface
  • Making a Single Move with Python
  • Functions of the RobotInterface Class
  • The Python Robot Interface Test
  • Coordinating Motor Movements with Sensors
  • Making a Python Brain for Our Robot
  • Overview of the Included Adeept Software
  • Where to Go from Here
30

Using Artificial Intelligence in Robotics

  • This Lesson’s Projects: Going to the Dogs
  • Setting Up the First Project
  • Machine Learning Using TensorFlow
  • Testing the Trained Network
  • Taking Cats and Dogs to Our Robot
  • Setting Up the Second Project
  • The FindAndChaseTheBall.py Python Program
  • The Main Program
  • AI and the Future of Robotics
31

Data Mining Patterns

  • Cluster analysis
  • Anomaly detection
  • Association rules
  • Questions
  • Summary
32

Data Mining Sequences

  • Patterns
  • Questions
  • Summary
33

Text Mining

  • Packages
  • Questions
  • Summary
34

Data Analysis – Regression Analysis

  • Packages
  • Questions
  • Summary
35

Data Analysis – Correlation

  • Packages
  • Questions
  • Summary
36

Data Analysis – Clustering

  • Packages
  • K-means clustering
  • Questions
  • Summary
37

Data Visualization – R Graphics

  • Packages
  • Questions
  • Summary
38

Data Visualization – Plotting

  • Packages
  • Scatter plots
  • Bar charts and plots
  • Questions
  • Summary
39

Data Visualization – 3D

  • Packages
  • Generating 3D graphics
  • Questions
  • Summary
40

Machine Learning in Action

  • Packages
  • Dataset
  • Questions
  • Summary
41

Predicting Events with Machine Learning

  • Automatic forecasting packages
  • Questions
  • Summary
42

Supervised and Unsupervised Learning

  • Packages
  • Questions
  • Summary

1

Interactive Mode, Getting Help, and Writing Apps

  • Getting Started with Visual Studio Code
  • Getting Started with Jupyter Notebook
2

Python Elements and Syntax

  • Generating a Random Integer
3

Building Your First Python Application

  • Using Boolean Operators
  • Working with Escape Characters
  • Using Arithmetic Operators
  • Using Variable Assignment
4

Working with Numbers, Text, and Dates

  • Using the round() Function
  • Printing the Absolute Value
  • Calculating the Square Root
  • Converting the Decimal Number to Binary, Octal, and Hexadecimal
  • Manipulating Strings Using the strip() Method
5

Controlling the Action

  • Using Conditional Statements
  • Using the for Loop
  • Using Nested Loops
  • Using the while Loop
6

Speeding Along with Lists and Tuples

  • Working with Lists
  • Working with Tuples
7

Cruising Massive Data with Dictionaries

  • Working with Dictionaries
8

Wrangling Bigger Chunks of Code

  • Working with Functions
  • Using a lambda Expression
9

Doing Python with Class

  • Working with Classes
10

Sidestepping Errors

  • Creating the Custom Exception Class
11

Working with External Files

  • Opening and Reading a File
  • Opening a CSV File
12

Juggling JSON Data

  • Loading Data from JSON Files
  • Dumping Python Data to JSON
13

Interacting with the Internet

  • Opening a URL
14

Libraries, Packages, and Modules

  • Using the math Module
15

Building a Neural Network

  • Creating an Activation Function
  • Exploring a Two-Layer Neural Network using NumPy
  • Training and Evaluating a Model in TensorFlow
  • Exploring a Two-Layer Neural Network using TensorFlow
16

Doing Machine Learning

  • Creating Convolutional Neural Network
  • Training and Evaluating a Network in Machine Learning
17

Exploring Big Data

  • Visualizing the Diamonds Dataset using MatPlotLib Library
  • Working with the Diamonds Dataset
  • Using the Pandas Library
  • Creating a Correlation Heat Chart
18

Introducing Physical Computing

  • Controlling an LED
19

No Soldering! Using Grove Connectors for Building

  • Simulating Traffic Light System Optimization
20

Making Things Move

  • Controlling a Stepper Motor
  • Controlling a DC Motor
21

Building Your First Python Robot

  • Controlling a Servo Motor
22

Using Artificial Intelligence in Robotics

  • Training a CNN Model Using the Image Dataset
23

Data Mining Patterns

  • Plotting a Graph by Performing k-means Clustering
  • Calculating K-medoids Clustering
  • Displaying the Hierarchical Cluster
  • Plotting Graphs By Performing Expectation-Maximization
  • Plotting the Density Values
  • Computing the Outliers for a Set
  • Calculating Anomalies
  • Using the apriori Rules Library
24

Data Mining Sequences

  • Using eclat to Find Similarities in Adult Behavior
  • Finding Frequent Items in a Dataset
  • Evaluating Associations in a Shopping Basket
  • Determining and Visualizing Sequences
  • Computing LCP, LCS, and OMD
25

Text Mining

  • Manipulating Text
  • Analyzing the XML Text
26

Data Analysis – Regression Analysis

  • Performing Simple Regression
  • Performing Multiple Regression
  • Performing Multivariate Regression Analysis
27

Data Analysis – Correlation

  • Performing Tetrachoric Correlation
28

Data Analysis – Clustering

  • Estimating the Number of Clusters Using Medoids
  • Performing Affinity Propagation Clustering
29

Data Visualization – R Graphics

  • Grouping and Organizing Bivariate Data
  • Plotting Points on a Map
30

Data Visualization – Plotting

  • Displaying a Histogram of Scatter Plots
  • Creating an Enhanced Scatter Plot
  • Constructing a Bar Plot
  • Producing a Word Cloud
31

Data Visualization – 3D

  • Generating a 3D Graphic
  • Producing a 3D Scatterplot
32

Machine Learning in Action

  • Finding a Dataset
  • Making a Prediction
33

Predicting Events with Machine Learning

  • Using Holt Exponential Smoothing
34

Supervised and Unsupervised Learning

  • Developing a Decision Tree
  • Producing a Regression Model
  • Understanding Instance-Based Learning
  • Performing Cluster Analysis
  • Constructing a Multitude of Decision Trees

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