This course has been designed by Professional Data Scientists. Complex theory, algorithms and coding libraries are explained in a simple way.
- Part 1 - Data Preprocessing
- Part 2 - Regression:
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- SVR, Decision Tree Regression
- Random Forest Regression
- Part 3 - Classification:
- Logistic Regression
- K-NN, SVM
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
- Part 4 - Clustering:
- K-Means
- Hierarchical Clustering
- Part 5 - Association Rule Learning:
- Apriori
- Part 6 - Reinforcement Learning:
- Upper Confidence Bound
- Thompson Sampling
- Part 7 - Natural Language Processing:
- Bag-of-words model algorithms for NLP
- Part 8 - Deep Learning:
- Artificial Neural Networks
- Convolutional Neural Networks
- Part 9 - Dimensionality Reduction:
- PCA
- LDA
- Kernel PCA
- Part 10 - Model Selection & Boosting:
- k-fold Cross Validation
- Parameter Tuning
- Grid Search
- XGBoost