As I ventured into the robotics industry as Software Test & Automation Engineering, I realized that becoming a more robust Robotics Software Engineer meant being able to implement Deep Reinforcement Learning (DRL) into self-developed projects involving hardware and algorithmns (path planning, computer vision, etc). For this dream to become a reality however, I understood this meant being able to grasp the precursors of Machine Learning and Deep Learning.
This repository serves to be a personal and communal benchmark detailing how to progress as a self-learner if curious about following this path as an RSE. I grew inspired by the embedded link on this repo, 'Machine Learning A-Z' by Udemy. I give props to them for the motivation and as the resource I used upon my initial journey debuting February 2024.
Please feel free to merge a PR or reccomendation through comments below. Any advice is always appreciated! :)
- Missing Data
- Categorical Data
- Template For Preprocessing Data (General Steps)
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
- Evaluating Regression Model
- Regularisation Methods
- Logistic Regression
- K-Nearest Neighbors (K-NN)
- Support Vector Machine (SVM)
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
- Evaluating Classification Model
- K-Means Clustering
- Hierarchical Clustering
- Apriori
- Eclat
- Upper Confidence Bound (UCB)
- Thompson Sampling
- Q-Learning
- Natural Language Processing
- Decision Tree
- Random Forest
- Max Entropy
- Artificial Neural Networks(ANN)
- Convolutional Neural Netwroks(CNN)
- Recurrent Neural Networks(RNN)
- Principal Component Analysis
- Linear Discriminant Analysis
- Kernel PCA
- Model Selection
- XGBoost
- Similar Movies
- Item Based Collabrative Filtering
- Keras-RNN -Keras-CNN
(Please View in SubBranches)
- Handwriting Recognition
- Predict Political Party
- Fire Detection Project