1. Machine Learning 1.1. Supervised Learning Numpy Pandas Automated EDA Handling Outliers Feature Scaling Dimensionality Reduction Feature Selection - Dimensionality Reduction LDA - Feature Extraction - Dimensionality Reduction PCA - Feature Extraction - Dimensionality Reduction Encoding Categorical Data EvalML - Automated ML AutoViML - Automated ML Cross-Validation Techniques Hyperparameter Tuning 1.2. Unsupervised Learning K-Means Clustering Topic Modeling - LDA Self Organizing Map (SOM) 2. Computer Vision CNN (Keras Sequential model) + Keras Tuner CNN (Keras Functional API) GRNN Fastai - Transfer Learning Tensorflow - Transfer Learning GAN Stacking Ensemble Average Ensemble Depth Map from stereo images SIFT - Image Matching SIFT+VLAD - Image Matching Motion Feature Extraction Object Tracking HOF and 3DCNN - Action Recognition 2DCNN - Action Recognition Pose Estimation 3. Natural Language Processing NLTK Text Preprocessing Tensorflow - Text Classification LSTM - Keras - Text Classification CNN-LSTM - Keras - Text Classification H20 - Text Classification RNN - Natural Language Generation Seq2Seq - Machine Translation Similarity-based Chatbot 4. Web Scraping with Python Infinite Scrolling Anti-Blocking Techniques AutoScraper (Automatic Web Scraping) Hide Credentials Login using Requests Login using cookies 5. Audio Data Audio Data Augmentation Audio Classification HMM-based Speech Recognition System 6. Signal Processing Peak Detection and Baseline Correction Dynamic Time Warping (DTW) Wavelet Transformation - Feature Extraction Autoencoder - Anomaly Detection 7. Optimization OR-Tools - Constraint Satisfaction Problem Fuzzy Logic Genetic Algorithm 8. Web Development Streamlit Flask (Without Styling) Flask (With Styling) FastAPI PyPI (pip) 9. Resources Daniel Bourke Ashish Patel PapersWithCode Aman Kharwal DL Drizzle
