My journey into Machine Learning started with the essentials of Python. I gradually moved towards to concepts of advanced algorithms and, finally moved into the cores of Machine Learning. With my key focus being the live projects, I dive deeper into the fundamentals of Regression Techniques and Neural Networks enabling the essential skills required in optimizing solutions to the real-world problems. It was just a matter of some time before I actually begin building intelligent systems, working on AI algorithms and data crunching.
Part 1. Introduction to Machine Learning
- Python Recap
- Intermediate Python
- Machine Learning Introduction
- Data Generation & Visualisation
- Linear Algebra in Python
Part 2. Supervised Learning Algorithms
- Linear Regression
- Locally Weighted Regression
- Multivariate Regression
- Logistic Regression
- K-Nearest Neighbours
- Naive Bayes
- Support Vector Machines
- Decision Trees & Random Forests
Part 3. Unsupervised Learning
- K-Means
- Principal Component Analysis
- Autoencoders(Deep Learning)
- Generative Adversial Networks(Deep Learning)
Part 4. Deep Learning
- Deep Learning Fundamentals
- Keras Framework, Tensorflow Basics
- Neural Networks Basics
- Building Text & Image Pipelines
- Multilayer Perceptrons
- Optimizers, Loss Functions
Part 5. Deep Learning in Computer Vision
- Convolution Neural Networks
- Image Classification Pipeline
- Alexnet, VGG, Resnet, Inception
- Transfer Learning & Fine Tuning
Part 6. Deep Learning Natural Language Processing
- Sequence Models
- Recurrent Neural Networks
- LSTM Based Models
- Transfer Learning
- Natural Lang Processing
- Word Embeddings
- Langauge Models
Part 7. Reinforcement Learning
- Basics of Reinforcement Learning
- Q Learning
- Building AI for Games
- Most of the codes are build from scratch using- the following libraries.
- Pandas (Data Handling)
- Matplotlib (Data Visualisation)
- Numpy (Maths)
- Keras (Deep learning)
- Tensorflow(Introduction)
- Sci-kit Learn(ML Algorithms)
- OpenAI Gym (Reinforcement Learning)
- Hardwork Pays Off (Regression Prediction)
- Air Quality Prediction (Multivariate Regression)
- Separating Chemicals (Logistic Regression)
- Face Recognition (OpenCV, K-Nearest Neighbours)
- Handwritten Digits Classifier
- Naive Bayes Mushroom Classification
- Movie Review Prediction (Naive Bayes, LSTM etc)
- Image Dominant Color Extraction (K-Means)
- Image Classification using SVM
- Titanic Survivor Prediction using Decision Trees
- Diabetic Patients Classification
- Non-Linear Data Separation using MLP
- Pokemon Classification using CNN, Transfer Learning
- Sentiment Analysis using MLP, LSTM
- Text/Lyrics Generation using Markov Chains
- Emoji Prediction using Transfer Learning & LSTM
- Odd One Out (Word2Vec)
- Bollywood Word Analgoies (Word Embeddings)
- Generating Cartoon Avatars using GAN's (Generative Adversial Networks)
- Reinforcement Learning based Cartpole Game Player
Image Captioning Generating Captions for images using CNN & LSTM on Flickr8K dataset.