conda create -n ml-algos python=3.9
conda activate ml-algos
conda install jupyter numpy pandas matplotlib scikit-learn tensorflow seaborn
conda install -c conda-forge notebook
jupyter notebook
conda install pip pip install "kagglehub[pandas-datasets]"
Write Algorithms from scratch in Python for all these Machine Learning Algorithms:
- Linear Regression - House Price Prediction
- Classification - Spam/ Ham Detection [*]
- Logistic Regression
- Ridge Regression
- Lasso Regression
- K-Means Clustering
- Gaussian Mixture Model
- Support Vector Machine - Heart Rate Failure
- Decision Trees
- Random Forest - House Price Prediction
- Gradient Boosting Regression
- Principal Component Analysis
- Neural Networks
- AdaBoost
- Naive Bayes
- Hidden Markov Model
- K-Nearest Neighbors
- DBSCAN
- Hierarchical Clustering Silhoutte Clustering (KN-Udemy) Anomaly Detection (KN-Udemy)
- Apriori
- FP-Growth
- PageRank
- AdaBoost
- XGBoost
- LightGBM
- CatBoost
- Word2Vec
- Doc2Vec
- GloVe
- BERT
- Transformer
- GPT-2
- ResNet
- VGG
- Inception
- MobileNet
- DenseNet
- CapsuleNet
- YOLO
- SSD
- Named Entity Recognation
- Faster R-CNN
- Mask R-CNN
- Neural Style Transfer
- Neural Doodle
- Neural Talk
- Neural Baby Talk
- Perceptron
- Multi Layer Perceptron
- XOR Problem
- Gradient descent
- Transfer learning
- N Gram
[ ] Universal approximation theorem
[ ] Optimizers - Momentum - Nesterov Accelerated Gradient - RMSprop - Adam
[ ] Curse of dimensionality
[ ] Convolution Neural Network - ALexNet - VGG - ResNet - Lenet - Activation Maximization - Saliency Maps
[ ] Recurrent neural network - LSTM - GRU
[ ] Generalization vs Overfitting Cross Validation Bias-Variance Tradeoff Optimization VS Learning