Techniques

• Classification, Regression, Time Series Analysis

• Supervised & Unsupervised

• Hyperparamter Tuning & Cross Validation

• Data Cleaning & Preprocessing

• Feature Selection & Engineering

• Ensemble Learning & Stacking

Tools

• Sklearn, TPOT- ML Models

• Numpy, Pandas - Data Wrangling

• Jupyter Notebook - Data Exploration

• Maptlotlib, Seaborn, Plotly - Data visualizations

Classification

• Logistic Regression

• K-Nearest Neighbors (K-NN)

• Support Vector Machine (SVM)

• Kernel SVM

• Naive Bayes

• Decision Tree Classification

• Random Forest Classification

Clustering

• K-Means Clustering

• Hierarchical Clustering

Regression

• Simple Linear Regression

• Multiple Linear Regression

• Polynomial Regression

• Support Vector Regression (SVR)

• Decision Tree Regression

• Random Forest Regression

• XGBoost

Dimensionality Reduction

• Principal Component Analysis (PCA)

• TSNE

• Variational autoencoders (Deep Learning)

Techniques:

• Image classification, Regression & Transfer Learning. Using Keras sequential/function API

• Ensemble Learning & Stacking, multiple input & outputs model

• Advance Hyperparameter Tuning, Bayesian Search

• Efficient Data Pipelines using tf.data & training using Multiple GPUs & TPUs.

• Showing Final Results, making Reports.

• Object detection & Image segmentation including architectures like UNET

• GAN, CycleGAN etc.

Tools

• Tensorflow, Keras, FastAI v2, Pytorch, Detectron2 - For Deep Learning & Computer Vision

• Hugging face, tokenizer, ktrain - Natural Language Processing

• OpenCV - Image Preprocessing

• Weights & biases, Tensorboard - For Experimentation

• FastAPI, Flask, Streamlit, Docker - For production

• Git, Github, Github Actions - Version Control & CI/CD

• AWS, Heroku, GCP - Deploying Services

Web Development 📱

• HTML5, CSS3, Javascript (ES6, ES7, ES8, ES9 )

• Bootstrap, flexbox

• Npm, Node JS. and Express JS.

• React JS

Game Development 🎳

• Unity

• Machine Learning A-Z : Hands-On Python & R In Data Science —Super Data Science

• Deep Learning A-Z: Hands-On Artificial Neural Networks —Super Data Science

• Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs —Super Data Science

• Full Stack Deep Learning Online Course

• Learning to learn: Efficient Learning — Zero to mastery

• Complete Python Developer in 2020 — Zero to mastery

• Complete Web Developer in 2020 — Zero to mastery

• Complete Machine Learning and Data Science — Zero to mastery

• Machine Learning — Andrew Ng

• Deep Learning Specialization — Andrew Ng

• DeepLearning.AI TensorFlow Developer Professional Certificate — Laurence Moroney

• Deep Reinforcement Learning — Udacity

Some wonderful series 📺

• The essence of linear algebra — 3blue1brown

• Essence of calculus — 3blue1brown

• Neural networks — 3blue1brown

• The Age of AI — Youtube Originals

Books I am reading/read 📚

• Life 3.0: Being Human in the Age of Artificial Intelligence — Max Tegmark

• Approaching (Almost) Any Machine Learning Problem — Abhishek Thakur

• Reinforcement Learning: An Introduction By Richard S. Sutton, Andrew G. Barto · 2018

• Structures: Or Why Things Don't Fall Down — J. E. Gordon

Other interesting courses 🎩

( not fully done )

• Practical Deep Learning for Coders — FastAI

• Deep Reinforcement Learning 2.0 — Super Data Science

• Modern NLP in python — Super Data Science

• Master Coding Interviews: Data Structures & Algorithms — Zero To Mastery

Hobbies 🚀

• Robotics

• Science

• Rocketry/Model Rocketry & Astronomy

• AI & Programming

• Football