Learning AI

Completed

Mar 2020 - Oct 2020 (8 months)

Learning AI is a long-form study and implementation project that tracks practical AI upskilling from foundational concepts to hands-on model work.

The repository captures course progression, assignment execution, and iterative experiments across machine learning, deep learning, and supporting tooling in a single chronological workspace.

Built with: Jupyter Notebook, Python, TensorFlow, Keras, Octave/MATLAB, Flutter, and Dart.

Links

Project Activity

Recent updates for Learning AI. Completed

October 2020

  • Deep Learning - Course 2 (Improving Deep Neural Networks) - Week 3 Slides
  • Deep Learning - Course 2 (Improving Deep Neural Networks) - Week 3 (Tensor Flow) - Assignment Start
  • Deep Learning - Course 2 (Improving Deep Neural Networks) - Week 2 (Tensor Flow 1.0) - Assignment Completed
  • Deep Learning - Course 2 (Improving Deep Neural Networks) - Week 1 - Assignment Completed
  • Deep Learning - Course 2 (Improving Deep Neural Networks) - Week 2 Slides
  • Deep Learning - Course 2 (Improving Deep Neural Networks) - Week 2 (Optimisation Methods) - Assignment Start
  • Deep Learning - Course 2 (Improving Deep Neural Networks) - Week 2 (Optimisation Methods) - Assignment Completed
  • Deep Learning - Course 2 (Improving Deep Neural Networks) - Week 1 - Assignment Start
  • Digit classifier project (using tensorflow) -- different models
  • Deep learning with python (book)
  • Digit classifier project (using tensorflow)
  • Experiment Project -- working on digits classifier ( general algorithm )
  • Number_of_layers - variable -- Experiment Project -- working on digits classifier ( general algorithm )
  • Experiment Project -- working on digits classifier ( basic algorithm )
  • Week 4 - Deep L-layer Neural Networks
  • Deep Learning - Course 2 (Improving Deep Neural Networks) - Week 1
  • Deep Learning - Course 1 - Week 1,2,3

August 2020

  • Machine Learning - Week 9 - Anomaly Detection & Recommender Systems
  • Machine Learning - Week 9 ( programming assignment started )
  • Machine Learning - Week 9 ( programming assignment completed )
  • Machine Learning - Week 10 - Large Scale Machine Learning
  • Machine Learning - Week 7 - Unsupervised Learning & Dimensionality Reduction
  • Machine Learning - Week 8 - Unsupervised Learning ( programming assignment started )
  • Machine Learning - Week 8 - Unsupervised Learning ( programming assignment completed )
  • Machine Learning - Week 7 - SVM - Programming Assignment: Gaussian Kernel
  • Machine Learning - Week 7 - SVM - Programming Assignment: Parameters (C, sigma) for Dataset 3
  • Machine Learning - Week 7 - SVM - Programming Assignment: Email Preprocessing & Email Feature Extraction
  • Machine Learning - Week 10 - Application Example : Photo OCR

July 2020

  • Machine Learning - Week 6 - Support Vector Machines
  • Machine Learning - Week 7 - SVM ( programming assignment started )
  • Machine Learning - Week 6 (Part 1) - Programming exercise - Polynomial Feature Mapping
  • Machine Learning - Week 6 (Part 1) - Programming exercise - Cross Validation Curve
  • Machine Learning - Week 6 (Part 1) - Machine Learning System Design
  • Machine Learning - Week 6 (Part 1) - Evaluating a Learning Algorithm
  • Machine Learning - Week 6 (Part 1) - Evaluating a Learning Algorithm (Programming exercise - started)
  • Machine Learning - Week 6 (Part 1) - Programming exercise - Regularized Linear Regression Cost Function
  • Machine Learning - Week 6 (Part 1) - Programming exercise - Regularized Linear Regression Gradient
  • Machine Learning - Week 6 (Part 1) - Programming exercise - Learning Curve
  • Machine Learning - Week 5 - Neural Network Gradient (Backpropagation)
  • Machine Learning - Week 5 - Neural Network Regularized Gradient
  • Machine Learning - Week 5 - Neural Network (Vectorized Implementation of Back Propagation)
  • Machine Learning - Week 5 - Neural Network Learning
  • Week 4 - Neural Network Learning (Programming exercise - started)
  • Machine Learning - Week 5 - Neural Network Learning - Feedforward and Cost Function
  • Machine Learning - Week 5 - Neural Network Learning - Regularized Cost Function
  • Machine Learning - Week 5 - Neural Network Learning - Sigmoid Gradient
  • Machine Learning - Week 4 (Neural Network Representation)
  • Machine Learning - Week 4 - Programming Exercise (Logistic Regression multi-class classification)
  • Machine Learning - Week 4 - Programming Exercise ( Neural Network Prediction )
  • Machine Learning - Week 3 - Logistic Regression
  • Machine Learning - Week 3 - Programming Exercise (part 1 completed)
  • Machine Learning - Week 3 - Programming Exercise (completed)
  • PSX - OGDCL data
  • Octave Examples & Practice
  • Machine Learning - Week 2 - Programming Exercise (Linear Regression with one variable)
  • Machine Learning - Week 2 - Linear Regression with Multiple Variables
  • Machine Learning - Week 1 - Introduction
  • Machine Learning - Week 1 - Model and Cost Function
  • Machine Learning - Week 1 - Gradient Descent
  • Machine Learning - Week 1 - Linear Algebra Review

May 2020

  • Clean flutter app - part 2-4
  • Removed clean flutter app (moved to separate github repo)
  • Clean flutter app - part 1
  • Bmi calculator - code refactoring
  • Bmi calculator in progress
  • Bmi calculator - layout
  • Bmi calculator - gender selection
  • BMI calculator (initial project)
  • BMI calculator - basic skeleton
  • Quiz app - implemented correct / wrong without classes
  • Quiz app (not completed - got bored)
  • Started a new learning milestone and added the initial implementation/materials for flutter/dart practice projects.
  • Completed a learning milestone and finalized related artifacts for flutter/dart practice projects.
  • Completed a learning milestone and finalized related artifacts for flutter/dart practice projects.
  • Dicee-flutter starter project
  • Completed a learning milestone and finalized related artifacts for flutter/dart practice projects.
  • Dice variable in stateless widget
  • Stateful widget (changing dice on button click)
  • Completed a learning milestone and finalized related artifacts for flutter/dart practice projects.
  • Decision making app - step 1 (setup project)
  • Decision making app - step 2
  • Decision making app - step 3
  • Decision making app - step 4
  • Decision making app - step 5 and 6
  • Xylophone app - play sound
  • Completed a learning milestone and finalized related artifacts for flutter/dart practice projects.
  • Reorganized Flutter practice project structure and numbering to keep the learning progression clearer across app exercises.
  • Cleaned the repository structure by removing extra folders so active learning tracks and project folders remained easier to maintain.
  • I_am_milllioniare exercise
  • Flutter Layouts Challenge (completed)
  • How to Add App Icons to the iOS and Android Projects
  • How to Add App Icons to the iOS and Android Projects
  • Creating a flutter project from scratch - Scaffolding a material app
  • Working with Images Assets in Flutter & the Pubspec file
  • Completed the AI For Everyone course track and archived the final learning materials in the repository.

April 2020

  • Defined estimated study time and deadlines to plan execution across AI coursework and practice tracks.
  • Week 2 and 3 pdfs

March 2020

  • ML implementation track & AI courses list
  • Deleted images & converted to pdf
  • Compressed and optimized AI course documents to keep study resources lightweight and easier to navigate.
  • Course: AI For Everyone (Week 1)

Objective Link to heading

  • Build a structured, repository-backed AI learning journey spanning conceptual AI foundations, classical machine learning exercises, deep learning coursework, and hands-on model experimentation.
  • Consolidate study materials, assignment solutions, and practical experiments in one versioned workspace to track progression from fundamentals to implementation.

Delivery scope Link to heading

  • Completed the AI For Everyone track and maintained weekly learning artifacts.
  • Progressed through Machine Learning weekly modules with programming exercises across regression, neural networks, SVMs, clustering, anomaly detection, and recommender systems.
  • Advanced through Deep Learning courses and assignments, including initialization/regularization/optimization exercises and TensorFlow-based tutorials.
  • Built and iterated digit-classifier experiments (basic NN and CNN variants), including model export artifacts for practical experimentation.

Technical foundation Link to heading

  • Notebook-first learning workflow using Jupyter for iterative experimentation and documentation.
  • Mixed practical stack combining Python/TensorFlow/Keras experimentation, Octave/MATLAB assignment workflows, and introductory Flutter/Dart practice projects.
  • Version-controlled course notes and resource packaging to keep progress auditable across multi-month learning cycles.