Python From Basics To Deep Learning & AI

Python From Basics To Deep Learning & AI | Bootcamp 2023

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Outcome Highlights

You can expect to master the following topics:

Basics

Learn All About If Statements, Loops, Functions, Lists and DictionariesO
Object Oriented Programming (OOP)

Break the barrier to OOP with super simplified examples. Learn all about Methods, Objects, Inheritance, Super Keywords, and follow along all the step by step real project examples.
Build Games

Build Real RPG Games using OOP. Learn about sprite sheets, Animation, Player and Enemy Logic, Power Ups, Collisions and get your self familiarized with Real Python Games Development.
User Interfaces and GUI

No Program is useful for the basic end user without a User Interface. Build User Interfaces using various libraries like Flet, and PySimpleGUI.
Machine Learning & AI

Enjoy the overly simplified Explanation of Complex ML Algorithms from Regression, To Classification, To Reinforcement learning, and NLP. Tons of Hands On Step By Step Examples are there to help you master this topic.
Deep Learning

Enjoy The Simple Explanation of Neural Networks, and build your own Classifiers using various datasets.
Database

No Professional App is possible without Database. Learn all about SQL and ORM from scratch. And See How a simplified Social Network works, using only those concepts.
Image/Video Processing and OCR

Reading Data From Images is no easy task. Learn to Use Image Processing and OCR techniques to analyze financial documents and extract all the numbers out of it.

Course curriculum

1. Intro
  FREE PREVIEW
1. Installing Environment
2. Opening IDE
3. Resources Note
1. Overview
2. Resources
3. Creating Variables in Python
4. Taking Input from user via Command Line
5. Performing Arithmetic Operation
6. What is a Random Number Generator
7. Let's Build a Game!
1. Overview
2. Resources
3. How does a Car Utilize The IF Statement
4. IF-ELSE To Which City Can Our Adventurer Go?
5. Controlling an RC Toy With ELIF Statement
6. What is my Grade Letter?
1. Overview
2. Resources
3. Storing Names-Ages Using Lists
4. Lists Append & Adding To A Shopping Cart
5. list Delete & Removing Items from Shopping Cart
6. List Empty & Preventing A Shopping Cart Crash
7. Nested Lists
8. Tuples
9. While Statement and Repeating The Program Forever
1. Introduction To Finite State Machines
2. Resources
1. Overview
2. Resources
3. Saving Code Space By Using FOR loops
4. Enumeration
5. Zip
6. Advanced Loops with List Comprehension
7. Image Coordinates Shift Using For Loop
1. Overview
2. Resources
3. List Entries Using Functions
4. Global Variables & What Can't My Function See
5. Returning A Value After Function Execution
1. Window Creation
2. Resources
3. Creating Ping Pong Paddles
4. Keyboard Commands
5. Paddle Motion
6. Ball Motion
7. Collision Detection
8. Scoring
1. Dictionaries
2. Resources
3. Dictionary Introduction
4. Dictionary & Lists as a Mini Database
5. Adding Entries to Dictionary
6. Does this Key Even Exist?
7. Deleting From a Dic/List
8. Iterating a Dictionary
1. Overview
2. Resources
3. Exceptions
4. Exception Types
5. Multi Exception Statements
6. What is Finally?
7. Collecting Exception Information
8. Priority
9. Customizing Exceptions
1. Resources
2. Lambda, a Function With no Definition
3. Map and Filter Built-in Function
4. Overview
1. Overview
2. Resources
3. Objects & Motor Functions
4. Controlling a Motor With Class Methods and Class Variables
5. Inheritance and Passing On Functionality
6. Super Keyword
1. Decorators Intuition
2. Resources
3. Decorating To Create a Function Check-Point
4. More About Decorators
5. Passing Arguments
1. Resources
2. Sprite Sheets
3. Tilemaps
4. Dependency Injection and Game Architecture
5. Project File Structure
6. Main Class Skeleton
7. The Blocks Class
8. The Crop/Surface/Blit Process
9. Sprites Layering
10. Instantiating Tilemap
11. Draw And Update
12. Debugging
13. Player Class
14. Enemy Class
15. Control & Coordinates
16. Enemy Motion Planning
17. Enemy Motion State Machine
18. Player Animation
19. Enemy Animation And More Motion Planning
20. Player Collision
21. Partial Collision
22. Enemy Collision
23. Water Animation
24. Camera
25. Enemy One Hit Kill
26. Player Health Bar
27. Enemy Health Bar
28. Weapon Equipment
29. Power Ball Bullet
30. Shooting Power Ball
31. Bullets Collision
32. Damage System
33. Player Damage System
34. Enemy Shooting System Part 1
35. Enemy Shooting System Part 2
36. Debugging Camera
37. Particle System
1. Resources
2. Reading .txt Files
3. Writing .txt Files
4. Reading With Split
5. Unzipping .zip Files
6. Zipping Files
1. Resources
2. Subplots
3. Data Visualization
4. Live Trend Animation
5. Trend Animation Coding
1. Overview
2. Intuition
3. Resources
4. Adding Directories and Directory Trees
5. Removing Directories and Directory Trees
6. Path Formatting
7. Subfolders
8. Exploring Directory
9. Changing Directory in Script
10. Bulk Data Rename
11. Filtered Search
1. Command Line Interface Intuition
2. Resources
3. Creating CLI with Arguments
4. Manipulating Arguments Passed
5. Argument Types
6. External Arguments
7. Optional Arguments
8. Actions
1. Overview
2. Resources
3. One Dimensional Arrays
4. 154. 1-D array programming
5. N-Dimensional Arrays
6. Searching For Elements
7. Getting Max/Min Element
8. Sorting
9. Combining Arrays
10. Splitting Arrays
11. Random Array Generation
12. Changing Array Shape
1. Overview
2. Image Processing Intuition
3. RGB Color Space
4. Installing Libraries
5. Resources
6. Read/Write Images .png/.jpg
7. Displaying Image in a Window
8. Grayscalling Images
9. Binarizing Images
10. Rotation
11. Numpy Images and OpenCV conversion
12. Stacking Display
13. Contours Intuition
14. Contours Pre-processing
15. Drawing Contours
16. Extracting Contours
17. Contour Area
18. Masking Intuition
19. Masking Binarized Images
1. Video Processing Intuition
2. Resources
3. Reading Video
4. GrayScale Video
5. Video Rotation
6. HSV adjusting Algorithm
7. Modifying Video HSV
8. Saving Video
1. Resources
2. Object Tracking Algorithms
3. Initial ROI
4. Tracking the Object in Video
5. Multiple Object Tracking
1. How to set it up
2. Shortcuts
1. Overview
2. Resources
3. Creating Data Frames
4. Exporting .csv Files
5. Reading .csv Files
6. Summing Data
7. Adding Rows
8. Removing Columns
9. Renaming A Header
10. Removing Rows
11. Indexing
12. Built-in plotting
13. Filtering
1. Overview
2. Intuition
3. Resources
4. Let's Create a Form GUI App
5. Buttons And Text Fields
6. Keys
7. Data Holders Part 1
8. Data Holders Part 2
9. Menus
10. Icons
11. Text Areas
12. Fitting Elements
13. Dividing Canvas Part 1
14. Dividing Canvas Part 2
1. Project Overview
2. Resources
3. Implementation Part 1
4. Implementation Part 2
5. Implementation Part 3
6. Implementation Part 4
7. Implementation Part 5
8. Implementation Part 6
9. Implementation Part 7
10. Implementation Part 8
1. Getting Everything Together
2. Resources
1. Resources
2. Building a Window
3. Wireframing
4. Buttons and Text Fields
5. Color Pallets and Window Properties
6. Advanced Widgets Alignment
7. Checkboxes
8. Dynamic Widgets
9. Beautifying
10. Reusability Introduction
11. UI Core Class
12. Task Class
13. Handling Multiple Widgets View
14. Reusing Widgets
15. Demonstrating Multiple Widget Views
16. Passing Data Between Widgets
17. Deleting Widget in Runtime
18. Beautyfing
1. Overview
2. Resources
3. Recommender System
4. Wolrd Of Datasets
5. Building Netflix Series Recommender
6. Data pre-processing
7. Filtering by Series Genre
8. Is It Enough To Count Votes?
9. Weighing Average
10. Extract Top N series
11. Final Touches
12. Converting The Project To Object Oriented Programming
13. Converting To Generic Class
1. OCR Intuition
2. Resources
3. Downloading Libraries
4. Exctracting What Is important
5. Text Boxes
6. Creating an Invoice Automatic Database Logging
7. Reading Images
8. Coordinate Extraction
9. Processing
10. Modifying Database
11. Gluing
12. Project Main Function
1. Getting The Libraries
2. Resources
3. Listening Through Microphone
4. Crash-Free Code
5. A Simple Voice Command Engine
6. Mouse And Keyboard Automation
7. On-Screen Item Location
8. Opening Apps Automatically
9. Automating Whatsapp Messages
10. Automating Whatsapp Stickers
11. Whatsapp with Voice Commands
1. Intro
2. Resources
3. Installing Libs
4. Setting up Web-Driver
5. Xpath Simply Explained
6. Interacting Using XPATH
7. Keyboard Automation
8. Clicking Around
9. Handling Selection Boxes
10. Project Overview
11. Collecting XPATH
12. Testing Out Fields
13. DateFormatting
14. Class Skeleton
15. WaitingForEvent
16. First Filling Round
17. Submit
18. IteratingData
19. Hiding The Browser
1. What Is Web Scraping?
2. Resources
3. The Larger Scraping Picture
4. Requesting a Page
5. Extracting Nested HTML Elements
6. Extracting Similar HTML Classes
7. Selenium XPATH Interaction
8. Tracing Back XPATH Issues
9. Selenium to BS4 HTML Passing
10. Dynamic Page Elements Handling and Final Touches
1. Overview
2. Data Intuition
3. Resources
4. Linear Data Generation
5. Adding Noise To Linear Data
6. Outliers
7. Polynomial Data Generation
8. Blobs
9. Circular Data Generation
1. Features, Dependent and Independent Variables
2. Supervised Vs Unsupervised
3. Data Splitting And Its Importance
4. Cross Validation
1. Intuition
2. Resources
3. One Shot Encoding
4. Mean Values
5. Dealing with missing Data
1. Datasets
2. Resources
3. Data Relationships Visualization
4. Data Similarity Visualization
5. Pair Plots
6. Histogram And KDE
1. Overview
2. Simply Explained
3. Resources
4. Classifying Marketing Campaign Purchases
5. Analyzing Dataset
6. Creating and Training Model
7. Training Model
8. Evaluating Model Perforamnce
9. Cross Validation
10. Confusion Matrix
11. Predicting New Data
1. Simply Explained
2. Resources
3. Let's Add Few More Details
4. Creating PCA Model For Data Visualization
1. Easily Explained: Linear Regression
2. Resources
3. Linear Regression Implementation Part 1
4. Linear Regression Implementation Part 2
5. Extracting Hyper Parameters
6. Easily Explained: Polynomial Regression
7. Watching Linear Regression Fail
8. Training Polynomial Model
1. Simply Explained
2. Resources
3. K-means Clustering Implementation
4. Combining PCA and K-means Clustering
5. Predicting New Data
1. Simply Explained
2. Let's add Few More Details
3. Wrapping Up
4. Resources
5. Minst in Action
6. Implementing SVM model
7. Visual Evaluation
8. Metric Evaluation
1. Simply Explained
2. Resources
3. Implementing LDA Model
4. Visualizing Data
5. LDA Vs PCA
1. Simply Explained
2. Resources
3. Implementing TSNE model For Data Visualization
1. Simply Explained
2. Resources
3. Analyzing Dataset
4. Dendograms
5. Training HC Model
6. Visual Evaluation
7. Notes Regarding Scaling
1. Decision Trees Simply Explained
2. Random Forests Simply Explained
3. Resources
4. Data Classification
5. Building Model
6. Evaluating Model
7. Importance Matrix
8. Random Search
9. Evaluating Results
1. Overview
2. Resources
3. Problem Statement
4. Downloading Libraries
5. Rendering
6. Brute Forcing A Solution Part 1
7. Brute Forcing A Solution Part 2
8. Q-learning Simply Explained
9. Q-Learning Implementation Part 1
10. Q-Learning Implementation Part 2
11. Q-Learning Implementation Part 3
1. Overview
2. NPL Intuition
3. Resources
4. Text Tokenizing
5. Removing Unnecessary Words
6. Dealing With Punctuations
7. Tagging
8. Lemmatizing a Text
9. Combining Techniques
10. Text Frequency Analysis
1. Project Intuition
2. Resources
3. Project Implementation Part 1
4. Project Implementation Part 2
5. Project Implementation Part 3
6. Project Implementation Part 4
7. Project Implementation Part 5
8. Project Implementation Part 6
1. Overview
2. Simply Explained
3. ANN and Images
4. Resources
5. Neuron
6. Activation Functions
7. Back-propagation
8. Deep Nets
9. Building Model
10. Installing Libs
11. Training Model
12. Plotting Performance
13. Predicted Vs Ground Truth
14. Shallow Or Deep?
15. Increasing The Function Order
16. Metrics Evaluation
1. Problem Description And Dataset
2. Resources
3. Project Implementation Part 1
4. Project Implementation Part 2
5. Project Implementation Part 3
6. Project Implementation Part 4
1. Classification
2. Resources
3. Data Pre-Processing
4. Image Table Format P1
5. Image Table Format P2
6. Building Model
7. Measuring Performance
8. Data Augementation
9. Dropout And More Data
10. Confusion Matrix
11. Predicting New Data
12. Saving Model
1. Web Apps Intuition
2. Resources
3. Getting Libraries
4. Running The Server
5. Creating Widgests
6. Sidebars
7. Getting Widget Value
8. More Widgets Handling
9. Callbacks
10. Adding Plots
11. Page Layout
12. Overview
1. Project Overview
2. Resources
3. Project Implementation Part 1
4. Project Implementation Part 2
5. Project Implementation Part 3
6. Project Implementation Part 4
7. Project Implementation Part 5
8. Project Implementation Part 6
9. Project Implementation Part 7
10. Project Implementation Part 8
1. Resources
2. Understanding DB in Social Media
3. Primary and Foreign Keys
4. Tables Relationships
5. Modelling And Sketching Databases
6. Installing SQLAlchemy
7. Users Table
8. Creating SQLite Database
9. SQLite DB Browser
10. DB Commit
11. Creating a New Social Media Post
12. Populating DB
13. Queries : Getting Started
14. More Queries
15. Likes Table
16. Social Media Like Workflow
17. Counting Post's Likes
18. Join Query And Listing Who Liked a Post

About this course

$79.00
502 lessons
34 hours of video content

What is the most valuable thing about this course?

Python might not be that super hard Programming language, but its applications are not easy. Machine Learning and Data analysis concepts are not easy to visualize or grasp due to the high dimensionality math. We are breaking those concepts and visualizing them, like explaining to a 5-years old with tons of hands-on step by step Applications.

Basics

Object Oriented Programming (OOP)

Build Games

User Interfaces and GUI

Machine Learning & AI

Deep Learning

Database

Image/Video Processing and OCR

Course Introduction

Section 1: Installing And Preparing The Environment

Section 2: Strings, Random Number Generators and CLI Game Fun

Section 3: Conditional Statements and Branching

Section 4: Lists and Bundle Data Storage

Section 5: Finite State Machines In Python

Section 6: Loops and Data Iteration

Section 7: Organizing Code With Pyhton Funcitons

Section 8: Graphical Game: Build Your First Ping Pong

Section 9: Data Storage With Python Dictionaries

Section 10: Exceptions and Preventing Program Crash

Section 11: Lambda and Built-in Functions

Section 12: Object Oriented Programming

Section 13: Decorators And Wrapper Funcitons

Section 14: Pygame : RPG Game Development Using Object Oriented Programming

Section 15: Read/Write/Zip/Unzip Files

Section 16: Introduction To Matplotlib

Section 17: Directory Managment

Section 18: Building Command Line Applications

Section 19: Numpy and Advanced Data Structures

Section 20: Image Processing with OpenCV

Section 21: Video Processing

Section 22: Object Tracking In Video

Section 23: The Jupyter Notebook

Section 24: Pandas and Tabular Data Processing

Section 25: GUI Apps

Section 26: GUI App Portfolio Project

Section 27: Converting Python To Stand-Alone Software

Section 28: Flutter For Python - UI

Section 29: Portfolio Project: Netflix Recommender Systems Introduction

Section 30: OCR and Image To Text Conversion

Section 31: GUI Automation And Speech Recognition

Section 32: Selenium: Webforms Automation

Section 33: Web Scraping With Selenium & Beautiful Soup

Section 34: Starting Data Science: Artificial Data

Section 35: ML Terminology Easily Explained

Section 36: Data Preprocessing And Features Engineering

Section 37: Seaborn

Section 38: Gaussian Naive Bayes

Section 39: Principal Component Analysis (PCA)

Section 40: Linear And Polynomial Regression

Section 41: K-means and Classification Models

Section 42: Support Vector Machine (SVM)

Section 43: Linear Discriminate Analysis (LDA)

Section 44: t-SNE

Section 45: Hierarchical Clustering

Section 46: Random Forests And Decision Trees

Section 47: Reinforcement Machine Learning and Teaching Models

Section 48: Natural Language Processing (NLP) and Text Comprehension

Section 49: NLP Portfolio Project

Section 50: Deep Learning With Artificial Neural Networks

Section 51: Deep Learning Portfolio Project

Section 52: Deep Learning Image Classification Portfolio Project

Section 53: Artificial Intelligence Web Apps

Section 54: AI Web App Portfolio Project

Section 55: SQL And Relational Databases (SQL Alchemy and SQLite)

About this course