Python in 3 Hours! [+ Machine Learning Deep Learning]

Python in 3 Hours! [+ Machine Learning & Deep Learning], available at $59.99, has an average rating of 4.53, with 18 lectures, based on 211 reviews, and has 2888 subscribers.

You will learn about 1. Python 3+ Programming Using Google Colab Free CPU, GPU, & TPU Nodes by A Johns Hopkins Instructor. 2. Machine Learning Fundamentals Including Supervised Learning by A Johns Hopkins Instructor. 3. Deep Learning Classification & Regression Programming Using TensorFlow by A Johns Hopkins Instructor. 4. Limited Development of Convolutional Neural Networks Using TensorFlow by A Johns Hopkins Instructor. This course is ideal for individuals who are Those programmers who want to learn Python 3+ quickly. or Those programmers who want to learn machine learning fundamentals & deep learning quickly. or Those programmers who want to develop deep learning models using TensorFlow. or Those programmers who want to run machine learning models on free online GPUs & TPUs. or Those programmers who want to learn Python & machine learning minimums. It is particularly useful for Those programmers who want to learn Python 3+ quickly. or Those programmers who want to learn machine learning fundamentals & deep learning quickly. or Those programmers who want to develop deep learning models using TensorFlow. or Those programmers who want to run machine learning models on free online GPUs & TPUs. or Those programmers who want to learn Python & machine learning minimums.

Enroll now: Python in 3 Hours! [+ Machine Learning & Deep Learning]

Summary

Title: Python in 3 Hours! [+ Machine Learning & Deep Learning]

Price: $59.99

Average Rating: 4.53

Number of Lectures: 18

Number of Published Lectures: 18

Number of Curriculum Items: 18

Number of Published Curriculum Objects: 18

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

1. Python 3+ Programming Using Google Colab Free CPU, GPU, & TPU Nodes by A Johns Hopkins Instructor.

2. Machine Learning Fundamentals Including Supervised Learning by A Johns Hopkins Instructor.

3. Deep Learning Classification & Regression Programming Using TensorFlow by A Johns Hopkins Instructor.

4. Limited Development of Convolutional Neural Networks Using TensorFlow by A Johns Hopkins Instructor.

Who Should Attend

Those programmers who want to learn Python 3+ quickly.

Those programmers who want to learn machine learning fundamentals & deep learning quickly.

Those programmers who want to develop deep learning models using TensorFlow.

Those programmers who want to run machine learning models on free online GPUs & TPUs.

Those programmers who want to learn Python & machine learning minimums.

Target Audiences

Those programmers who want to learn Python 3+ quickly.

Those programmers who want to learn machine learning fundamentals & deep learning quickly.

Those programmers who want to develop deep learning models using TensorFlow.

Those programmers who want to run machine learning models on free online GPUs & TPUs.

Those programmers who want to learn Python & machine learning minimums.

1.1. Course instructor

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My name is Mohammad H. Rafiei, Ph.D. I am a researcher and instructor at Johns Hopkins University, College of Engineering, and Georgia State University, Department of Computer Science. I am also the founder of MHR Group LLC in Georgia, a data-analytic company, where we work with various domestic and global researchers at different institutions to address persistent challenges in Computer Science, Engineering, and Medicine, using state of the art machine learning and optimization techniques.

It is my great pleasure to serve as a Udemy instructor, helping thousands of students and researchers across the globe to learn Python and machine learning.

1.2. Does this course suit you?

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You want to (1) learn Python, (2) learn and apply machine learning artificial intelligence in Python, (3) run Python on free CPU, GPU, and TPU cloud computers, (4) do not want to install any bulky software on your computer, (5) want to do all this in less than 3 hours, (6) and want this course to be 100% moneyback guaranteed. If that is the case, then you are in the right place!

In less than 3 hours, this course will teach you:

1. Python 3+ from scratch (no installation is required; all on free cloud computers at Google)

2. General machine learning concepts and neural networks

3. How to develop machine learning models using TensorFlow in Python 3+

4. How to investigate your problems in Python

This course helps you if:

1. You are a Python beginner who is interested in learning Python and using Python to develop machine learning models in less than 3 hours.

2. You are interested in using free and powerful cloud CPU and GPU computers to develop and run your Python codes.

3. Almost wherever you are in the world, Google will give you free remote access to its computers.

4. Free CPU, GPU, and TPU processors to develop and run your Python codes for Free!

5. You only need to have Gmail (free) and Google Chrome (also free) installed on your operating system!

6. It does not matter what your operating system is.

7. No bulky software is required, just Google Chrome web browser!

8. Almost all the cheapest computers in the market can handle Google Chrome, so no significant computer system is required.

9. You have no or little knowledge of Python, are interested in learning Python and want to practice machine learning problems in Python, all in a matter of fewer than 3 hours.

10. You might have no or little knowledge of Python; you will be taught!

11. You might have no or little knowledge of machine learning or neural networks; you will be taught, and you will practice them in Python!

12. You are so busy and don’t have the time to go over a 25-hour course that teaches you many rudimentary programming basics.

13. You need optimum materials in a minimum amount of time to help you drive Python by yourself!

14. You prefer not even install any additional complicated software, editors, or programs on your computer to run Python!

15. You might have an old rusty computer, but it is able to run the latest version of Google Chrome (i.e., the Google free web browser).

16. Your computer has limited memory to run programming scripts or has a limited hard drive to install bulky and complicated software.

17. You will benefit the most if you are familiar with at least one computation-based programming language, such as MATLAB, R, C, C++, C#, etc., and want to switch to or learn Python.

18. We are not going to explain, say, what a “for-loop” is, but we will see how to create, say, “for-loops” in Python.

19. We are not explaining what an array or matrix is.

1.4. Course Overview

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180 Minutes in 12 Lectures:

Lecture 01: An Introduction to the Course ( < 18 minutes)

Lecture 02: Gmail, Chrome, and Google Colab (~11 minutes)

Lecture 03: Operations, Built-in Functions, and Data Types (~20 minutes)

Lecture 04: Loops, Conditional Scripts, and Functions (~16 minutes)

Lecture 05: Numpy and Pandas for Data Processing (~28 minutes)

Lecture 06: Matplotlib and Seaborn for Data Visualizations (~10 minutes)

Lecture 07: Data Repositories and Data Split in Machine Learning (~15 minutes)

Lecture 08: Data Processing and Calibrations in Machine Learning (~13 minutes)

Lecture 09: Brief Introduction to Neural Networks (~11 minutes)

Lecture 10: TensorFlow Keras for Regression Neural Networks (~16 minutes)

Lecture 11: TensorFlow Keras for Classification Neural Networks ( ~13 minutes)

Lecture 12: Hit the Road on Your Own! ( ~9 minutes)

1.5. Your Contribution

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Please write a review about this course; then, we can modify it and make it better. If you find this course interesting, please refer it to your friends and colleagues.

1.6. Acknowledgment

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I want to thank my wife, Fatemeh, for all her support in developing this course. I want to thank my friend and brother, Ahmad Mohammadshirazi, a computer science Ph.D. student at Ohio State University, for helping me in the video editing of this course.

Course Curriculum

Chapter 1: An Introduction to the Course

Lecture 1: An Introduction to the Course

Chapter 2: Gmail, Chrome, and Google Colab

Lecture 1: Gmail, Chrome, and Google Colab

Chapter 3: Operations, Built-in Functions, and Data Types

Lecture 1: Operations, Built-in Functions, and Data Types, Part 01

Lecture 2: Operations, Built-in Functions, and Data Types, Part 02

Chapter 4: Loops, Conditional Scripts, and Functions

Lecture 1: Loops, Conditional Scripts, and Functions, Part 01

Lecture 2: Loops, Conditional Scripts, and Functions, Part 02

Chapter 5: Numpy and Pandas for Data Processing

Lecture 1: Numpy and Pandas for Data Processing, Part 01

Lecture 2: Numpy and Pandas for Data Processing, Part 02

Lecture 3: Numpy and Pandas for Data Processing, Part 03

Chapter 6: Matplotlib and Seaborn for Data Visualizations

Lecture 1: Lecture 10

Chapter 7: Data Repositories and Data Split in Machine Learning

Lecture 1: Lecture 11

Lecture 2: Lecture 12

Chapter 8: Data Processing and Calibration in Machine Learning

Lecture 1: Lecture 13

Chapter 9: Brief Introduction to Neural Networks

Lecture 1: Brief Introduction to Neural Networks

Chapter 10: TensorFlow Keras for Regression Neural Networks

Lecture 1: TensorFlow Keras for Regression Neural Networks, Part 01

Lecture 2: TensorFlow Keras for Regression Neural Networks, Part 02

Chapter 11: TensorFlow Keras for Classification Neural Networks

Lecture 1: TensorFlow Keras for Classification Neural Networks

Chapter 12: Increase Our Python Expertise and Knowlege on Our Own!

Lecture 1: Increase Our Python Expertise and Knowledge on Our Own!

Instructors

Mohammad H. Rafiei
Instructor and Academic Advisor

Rating Distribution

1 stars: 1 votes

2 stars: 3 votes

3 stars: 17 votes

4 stars: 76 votes

5 stars: 114 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!