nidhirai95 / machinelearningwithpython

Get started with Machine Learning with Python - An introduction with Python programming examples

Machine Learning With Python

Get started with Machine Learning with Python

An engaging introduction to Machine Learning with Python

TL;DR

There are two options.

1. Download all the notebooks from this repository and run them in Jupyter Notebook. Chapter one in eBook will get you started with that.
2. Follow along using Google colab

Note: On each of those options, you'll find:

• A starter folder, which contains all the notebooks, that are empty in order to follow along.
• A final folder, which contains all the notebooks with all the source code.

Option 1

• Download all Jupyter Notebooks from repo (zip-file-download).
• Unzip download (main.zip) appropriate place.
• Launch Ananconda and start JuPyter Notebook (Install it from here if needed)
• Open the first Notebook from download.
• Start watching the first video lesson (YouTube).

Option 2

• No installations needed.

• Go to Colab Notebooks Folder

• Start watching the first video lesson (YouTube).

• Note: On each notebook, click on "Open in Colab", in order to open it on Google Colab

Machine Learning (ML)

Goal of Course

• Learn the advantages of ML
• Master a broad variety of ML techniques
• Solve problems with ML
• 15 projects with ML covering:
  • k-Nearest-Neighbors Classifier
  • Linear Classifier
  • Support Vector Classification
  • Linear Regression
  • Reinforcement Learning
  • Unsupervised Learning
  • Neural Networks
  • Deep Neural Networks (DNN)
  • Convolutional Neural Networks (CNN)
  • PyTorch classifier
  • Recurrent Neural Networks (RNN)
  • Natural Language Processing
  • Text Categorization
  • Information Retrieval
  • Information Extraction

Course Structure

• The course puts you on an exciting journey with Machine Learning (ML) using Python.
  • It will start you off with simple ML concepts to understand and build on top of that
  • Taking you from simple classifier problems towards Deep Neural Networks and complex information extractions
• The course is structured in 15 sessions, where each session is composed of the following elements
  • Lesson introducing new concepts and building on concepts from previous Lessons
  • Project to try out the new concepts
  • YouTube video explaining and demonstrating the concepts
    • A walkthrough of concepts in Lesson with demonstrating coding examples
    • An introduction of the Project
    • A solution of the project

Are You Good Enough?

Worried about whether you have what it takes to complete this course?

• Do you have the necessary programming skills?
• Mathematics and statistics?
• Are you smart enough?

What level of Python is needed?

• Some basic knowledge of Python and/or programming is highly recommended.
  • If you feel lost check out this free Python for beginners course
    • It is structured in 17 lessons
    • 8 hours of video tutorials
    • 17 projects taking you from scratch
    • It includes a free eBook with all concepts
    • It has a GitHub repository with all examples and projects prepared for you
• There will be links to help materials when using difficult programming concepts.

What about mathematics and statistics?

• Fortunately, when it comes to the complex math and statistics behind the Machine Learning models, you do not need to understand that part.
• All you need is to know how they work and can be used.
  • It's like driving a car. You do not have to be a car mechanic to drive it - yes, it helps you understand the basic knowledge of an engine and what the engine does.
  • Using Machine Learning models is like driving a car - you can get from A to B without being a car mechanic.

Still worried?

• A lot of people consider me a smart guy - well, the truth is, I'm not
  • I just spend the hours learning it - I have no special talent
• In the end, it all depends on whether you are willing to spend the hours
• Yes, you can focus your efforts and succeed faster
  • How?
  • Well, structure it with focus and work on it consistently.
  • Structure your learning - many people try to do it all at once and fail - stay focused on one thing and learn well.
  • Yes, structure is the key to your success.

Any questions?

• I try to answer most questions. Feel free to contact me.