_____# Virtual Machine Learning Master Course

Initial Plan:

1: Foundations and Core Concepts

1.1: Foundations

• Weeks 1-4: Introduction to Machine Learning, Python basics.
• Weeks 5-8: Probability, Statistics, and Data Analysis.
• Weeks 9-12: Algorithms and Data Structures for ML.
• Weeks 13-16: Introductory Projects and Hands-on Python Exercises.

2.2: Core ML Concepts

• Weeks 1-4: Supervised and Unsupervised Learning Techniques.
• Weeks 5-8: Introduction to Neural Networks and Deep Learning.
• Weeks 9-12: Feature Engineering and Data Preprocessing.
• Weeks 13-16: Semester Project on a Supervised Learning Problem.

2: Advanced Topics and Specialization

• Weeks 1-4: Natural Language Processing and Computer Vision.
• Weeks 5-8: Reinforcement Learning and Predictive Analytics.
• Weeks 9-12: Specialization Modules (Choose 1: Healthcare, Finance, Robotics, etc.).
• Weeks 13-16: Advanced Projects in chosen specialization.

2.1: Real-World Applications and Capstone Project

• Weeks 1-4: Ethics in AI, Bias and Fairness.
• Weeks 5-8: Large Scale Machine Learning and Cloud Computing.
• Weeks 9-16: Capstone Project: From Proposal to Deployment.

3: Assessments and Projects

• Bi-weekly quizzes and coding assignments.
• End of each semester: Comprehensive Take-Home Exams.
• Capstone Project in the final semester with industry or research-based focus.

4: Workshops and Guest Lectures

• Regular workshops on the latest ML tools and technologies.
• Guest lectures from industry experts and renowned researchers.

5: For a top-tier program like ours, let's set it up like this:

• 3 Lectures per Week: Each lasting about 2 hours, focusing on core concepts, theory, and applications.
• 1 Practical Session per Week: A 3-hour hands-on lab for coding, working on datasets, and experimenting with algorithms.
• 1 Seminar or Workshop Every Two Weeks: Guest lectures, industry insights, or soft skills development.

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1: Getting Started with Machine Learning and Python

Introduction to Machine Learning

Topic: Overview of ML, history, key concepts.
Material:

• Video: "What is Machine Learning?" for a basic overview.
• Video: "The 7 Steps of Machine Learning" to understand the ML process.
• TensorFlow Playground: https://playground.tensorflow.org/
• Activity: Discussion on ML's impact in various industries.

Python Programming Basics

Topic: Python syntax, basic data types, control structures.
Material:

• Video: Python Programming Tutorial for Beginners. https://www.youtube.com/watch?v=_uQrJ0TkZlc
  • notebooks:
    • using Pandas and Numpy, MatplotLib: https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example/blob/master/Exercises_1.ipynb
• Interactive Python tutorials on websites like Codecademy or Kaggle.
  • Python basic, SciKit.learn, classification exercises: https://app.datawars.io/dashboard
• Activity: Basic Python coding exercises.

Python for Data Science

Topic: Introduction to NumPy and Pandas, basic data manipulation.
Material:

• Jupyter notebooks with examples (can be found on GitHub repositories).
• Video: Data Analysis with Python - Full Course for Beginners. https://www.youtube.com/watch?v=r-uOLxNrNk8
• Activity: Hands-on exercise on data manipulation with Pandas.
  • exercises HERE

Assignments:

• Reading: Chapters from "Python for Data Analysis" by Wes McKinney (e-book or online resource).
• Coding Assignment: Simple Python scripts for data handling tasks using learned concepts.

Additional Resources:

• Access to interactive Python courses on platforms like Coursera, Codecademy, or Kaggle.
  • DataWars: https://app.datawars.io/login?redirect_to=https://app.datawars.io/dashboard
• Forums for Q&A and discussion.

2: Diving Deeper into Machine Learning Concepts

Class 4: Introduction to Probability and Statistics

Topic: Basic probability theory, descriptive statistics, and inferential statistics.
Material:

• Video: Statistics and Probability Full Course || Statistics For Data Science
• Online Course: Basics of Statistics & Probability on platforms like edX or Khan Academy.
• Activity: Applying statistical methods to a sample dataset.

Class 5: Calculus and Linear Algebra in Machine Learning

Topic: Algebra Material:

• video: The Essence of Calculus
• Video: Linear Algebra - Math for Machine Learning
• video: Understanding Euler Constant
  • video: Euler Imaginary unit i in Compound Interest Context
  • video: Euler Imaginary unit i in Geometry Context
  • video: Euler constant is everywhere!

Class 6: Neural Net Structure

Topic: Structure of Neural-Nets Material:

• video: MIT - Structure of Neural Nets for Deep Learning
• video: MIT - Backpropagation: Find Partial Derivatives
• video list: Coding Train - Neural Net from Scratch

Topic: Activation Functions Material:

• sigmoid, sigmoid gradient
• hyperbolic-tangent
• ReLU
• softmax

Topic: Transformers Material:

• video: What are Transformers?
• Use case: Word Embedding
  • self attention Paper
    • encoder. decoder, position encoders

Class 7: Back Propagation & Gradient Descent

Topic: Backpropagation introduction Material:

• video: Loss, cost, negative gradient and Backpropagation
• video: Backpropagation Calculus
• video: Neural Nets and the Learning Function

Topic: Understanding Gradient Descent Material:

• video: Gradient Descend in 3 minutes
• video: Gradient Descend in Deep Neuro Network
• video: MIT CLASS - Matrices
• video: Stanford CS229 - Linear Regression and Gradient Descend
• video: MIT CLASS - Stochastic Gradient Descent
• video: Stochastic Gradient Descent Classifier - Using MNIST dataset
  • video: Understanding Euler Constant
• Interactive Module: Khan Academy or Coursera lessons on Linear Algebra.
• Activity: Solving linear algebra problems using Python.
• Gradient Descent BEST EXPLANATION WITH CODE

Topic: Measure Performance of Training Material:

• video: Performance Measures

3: HANDS ON: build a Neuron Net from scratch

Class 8: Practical - Build a Neural Net from Scratch

Material:

• build a Perceptron: Coding Train - Perceptron Part 1
• Project HERE
• TODO LINK PROJECT!!

4: HANDS ON: build a Neuron Net from scratch + Learning Algorithms

Class 8 continuation: Practical - Build a Neural Net from Scratch

Material:

• training the Perceptron and test with pygame: Coding Train
• build Neural-net: Coding Train - Neural Net from Scratch
• Project HERE =))))))

Class 9: Genetic Algorithms

Material:

• research..... NEAT - Neuro Evolution of Augmented Topology
• research..... PPO - Proximal Policy Optimization

5: HANDS ON: build DeepNeuralNet with Pytorch and test with a game

Class 10: Pytorch 25hrs Full Course

Material:

• Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial
• [-] PyTorch for Deep Learning & Machine Learning – Full Course
• [-] PROJECT HERE

Class 11: Convolutional NN Architectures

Material:

• What is Convolution?
• Convolutional NN Explained, CNN Explained Deeper
• Correct CNN Animation
• Why 3x3 Kernel is better? History
• Intuitive view of Kernels working: The moment we stopped understanding AI

Class 12: Applied RAG

• LangChain Course - freeCodeCamp.org
• [-] PROJECT HERE

Class 13: LEET-CODE REINFORCEMENT 900 HOURS PLAN

Material

• MIT - Lecture 5: Binary Search Trees, BST Sort
• MIT - Lecture 4: Heaps and Heap Sort