An engaging introduction to Machine Learning with Python

• 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).

• 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

• 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

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?

• 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.

• 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.

• 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.

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