Applied Data Science & Machine Learning with Python

Course

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About

Welcome to the Applied Data Science & Machine Learning with Python course! In this program, you will dive into the exciting world of artificial intelligence, data science, and machine learning by leveraging the power of Python. This course is designed to provide a comprehensive understanding of essential concepts and hands-on experience with industry-standard tools.

Welcome to the Applied Data Science & Machine Learning with Python course!

In this program, you will dive into the exciting world of artificial intelligence, data science, and machine learning by leveraging the power of Python. This course is designed to provide a comprehensive understanding of essential concepts and hands-on experience with industry-standard tools.

What You Will Learn:

1	Debunk AI myths and learn the basics of machine learning and neural networks, understanding how computers learn.
2	Will refresh your Python skills, covering essential libraries like NumPy and Pandas for data manipulation and analysis.
3	Learn to use Keras with OpenAI's Gym to build and train machine learning models that simulate complex environments while optimizing performance and efficiency.
4	Explore reinforcement learning by creating game agents to solve real-world problems, including tasks like navigating environments and optimizing decisions.
5	Explore Natural Language Processing (NLP), learning how machines understand human language through projects on speech recognition and language-based AI models.

AI Orientation: We will start by dispelling common myths and misconceptions about Artificial Intelligence. You’ll get introduced to the fundamentals of machine learning and neural networks, demystifying how computers learn.

Python Basics Brush-Up: For those new to programming or in need of a refresher, this unit will bring you up to speed on Python programming and its crucial libraries like NumPy and Pandas—key tools for data manipulation and analysis.

Keras & Gym: You’ll learn how to use Keras, a powerful deep learning library, in conjunction with OpenAI’s Gym to build and train machine learning models. By the end of this unit, you’ll be able to implement models that simulate complex environments and balance performance, accuracy, and computational efficiency.

Game Agents & Reinforcement Learning: Dive into reinforcement learning by solving real-world problems. Through this unit, you’ll develop game agents using advanced machine learning techniques and apply them to solve tasks like navigating environments and optimizing decision-making.

Natural Language Processing: The final unit focuses on Natural Language Processing (NLP). You'll explore how machines understand and process human language, culminating in projects that involve speech recognition and language-based AI models.

Career Pathways

Data Scientist, Business Intelligence Analyst, Data Analyst, Data Engineer.

Modules11

Here is the course outline:

8 sections

Lesson Objectives

Key Terms

Is this real?

Discussion

Machine Learning

AI Experiments

Questions

17 sections

Lesson Objectives

Key Terms - 1

Key Terms - 2

Discussion

How Machines Learn

Build a Better Brain!

Neural Network Vocabulary Lesson - 1

Neural Network Vocabulary Lesson - 2

Neural Network Vocabulary Lesson - 3

Neural Network Vocabulary Lesson - 4

Neural Network Vocabulary Lesson - 5

Neural Network Vocabulary Lesson - 6

Neural Network Vocabulary Lesson - 7

Neural Network Vocabulary Lesson - 8

How it works

Quiz

Questions

7 sections

Lesson Objectives

Your Coding Experience

We will be using...

Modular Programming

Modules in Python

Star Wars

20 sections

Lesson Objectives

Data Scientists

Basic Operations with Numpy and Pandas

Importing What We Need

Working with Pandas

Importing Documents with Pandas - 1

Importing Documents with Pandas - 2

Importing Documents with Pandas - 3

Importing Documents with Pandas - 4

Selecting & Refining Data - 1

Selecting & Refining Data - 2

Selecting & Refining Data - 3

Saving Your Revised Data

Working with Numpy

The Shape Of Data: Using Numpy - 1

The Shape Of Data: Using Numpy - 2

Some Special Uses Of Numpy - 1

Some Special Uses Of Numpy - 2

Pandas Activity

What Did You Learn?

12 sections

Lesson Objectives

Key Terms

Questions

Google Gymnasium

Initializing a Game in OpenAI - 1

Initializing a Game in OpenAI - 2

Initializing a Game in OpenAI - 3

Initializing a Game in OpenAI - 4

Writing an Algorithm that Plays a Game at Random

Play the Game Yourself!

Keep These in Mind:

Presentation of your Gaming Experience

19 sections

Lesson Objectives

Key Terms

Balancing Act by Humans

Balancing Act by a Computer

Setting up Keras

Importing the Right Packages

Setting Up a Gym Environment - 1

Setting Up a Gym Environment - 2

Building Our First Model in Keras - 1

Building Our First Model in Keras - 2

Training, Playing

Train it Up! - 1

Train it Up! - 2

Train it Up! - 3

Generating Data & Training the Model-1

Generating Data & Training the Model-2

Play Time!

Play around with:

Comparing Your Models

32 sections

Lesson Objectives

Key Terms

Apollo 11 Lunar Landing

Lunar Landing Project

Ground Control to Major Tom

Importing the Right Stuff

Setting up the problem - 1

Setting up the problem - 2

An Easier, Wordier Way To Find The State_size - 1

An Easier, Wordier Way To Find The State_size - 2

Building HAL

Defining the Model's Inputs - 1

Defining the Model's Inputs - 2

Building Hidden Layers - 1

Building Hidden Layers - 2

Building Hidden Layers - 3

Defining the Model's Outputs & Compiling - 1

Defining the Model's Outputs & Compiling - 2

Training, Training

From Chaos To Order: Generating Training Data - 1

From Chaos To Order: Generating Training Data - 2

Training The Model Using The Data Generated

Saving your Model - 1

Saving your Model - 2

Seeing your Progress - 1

Seeing your Progress - 2

Your Task

Required Link

Draw out your Algorithm

Questions

Apply API to a Game

8 sections

Lesson Objectives

Your Learning Style?

Activity

Discussion

Applying this to ML

Post-video Discussion 1

Using this for Games

Post-video Discussion 2

19 sections

Lesson Objectives

Discussion

Q Learning - 1

Q Learning - 2

Taxi

Setting Up the Problem - 1

Setting Up the Problem - 2

Setting Up the Problem - 3

Setting Up the Problem - 4

Setting Up the Problem - 5

Training the Q-Table - 1

Training the Q-Table - 2

Training the Q-Table - 3

Training the Q-Table - 4

Training the Q-Table - 5

Playing the game

Play with Variables

Questions

Problem Solving

35 sections

Lesson Objectives

Key Terms

AI Gaming - 1

AI Gaming - 2

Build a Better Atari Gamer

Atari: SpaceInvaders

In this image, when does the game change states?

Building the Model - 1

Building the Model - 2

Building the Model - 3

Building the Model - 4

Building the Model - 5

Building the Model - 6

Building the Model - 7

Stacking Frames Example

Building the Model - 8

Building the Model - 9

Building the Model - 10

Building the Model - 11

Building the Model - 12

Building the Model - 13

Building the Model - 14

Building the Model - 15

Building the Model - 16

Building the Model - 17

Building the Model - 18

Building the Model - 19

Building the Model - 20

Building the Model - 21

Training the Model - 1

Training the Model - 2

Training the Model - 3

Letting the Model Train

Questions

Speeding up the Process

16 sections

Lesson Objectives

Key Terms

Question

NLP

NLP: Sentiment Analysis

Embeddings: Encoding Words for a Computer Brain

Sample Reviews - 1

Sample Reviews - 2

Sample Reviews - 3

A Better Model

Word Embeddings - 1

Word Embeddings - 2

A new network: RNN & LSTM

Video on LSTM

Magical Text Understanding Robot

Questions

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Applied Data Science & Machine Learning with Python

Career Pathways

1.1 AI Orientation - An Introduction to Machine Learning: What It Isn't

1.2 AI Orientation: Neural Networks (unmasked)

2.1 Brushing up on basics: Python

2.2 Brushing Up On Basics: Numpy & Pandas

3.1 Keras & Gym: Navigating OpenAI Gym

3.2 Keras & Gym: "Balanced. As Everything Should Be."

3.3 Keras & Gym: Using Keras' Functional API To "Go To The Moon"

4.1 Machine Vision: Learning How to Learn: Supervised Learning vs. Unsupervised Learning

4.2 Machine Vision: Solving the Taxi problem with Q-Learning

4.3 Machine Vision: Build a Better Atari Gamer

5.1 NLP: How do Computer Brains deal with Words?