Machine Learning using Python
COURSE DESCRIPTION
- Applications of Machine Learning
- Machine Learning - The Future
- Help with Installing Application and Environment Setup
- Recommended Version of Applications
All you need to join the course:
- Graduate or Under Graduate in any stream
- Dedication to learn
- Basic coding skills
Machine Learning using Python - Syllabus
- Data Preprocessing
- Acquiring the Dataset
- Important Libraries
- Using the Dataset
- Python – a starter guide
- Missing Data
- Categorical Data
- Splitting the Dataset (in Training set and Test set)
- Feature Scaling
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Decision Tree Regression
- Random Forest Regression
- Evaluating Regression Models Performance
- Logistic Regression
- K-Nearest Neighbors (K-NN)
- Support Vector Machine (SVM)
- Kernel SVM
- Bayes
- Decision Tree Classification
- Random Forest Classification
- Evaluating Classification Models Performance
- K-Means Clustering
- Hierarchical Clustering
- Upper Confidence Bound (UCB)
- Thompson Sampling
- Artificial Neural Networks
- Convolutional Neural Networks
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Kernel PCA
