OUTLINE:

I. MATHEMATICS AND STATISTICS:
1. Descriptive Statistics
2. Inferential Statistics
3. Probability Theory
4. Multivariate Statistics
5. Bayesian Statistics

II. STATISTICAL HYPOTHESIS TESTING:
1. Hypothesis Testing Strategies
2. A/B Testing

III. DATA QUALITY AND MANAGEMENT

IV. DATA EXPLORATION

V. DATA CLEANING

VI. DATA TRANSFORMATION & PREPROCESSING:
1. Categorical Data
2. Data Distribution
3. Feature Scaling
4. Data Augmentation
5. Handling Missing Data
6. Handling Outliers
7. Handling Imbalanced Data
8. Preprocessing for Neural Networks

VII. FEATURE SELECTION & ENGINEERING:
1. Feature Selection
2. Feature Engineering

VIII. MODEL DIAGNOSTICS

IX. DATA VISUALIZATION

X. MODEL EVALUATION METRICS:
1. General Model Evaluation Metrics:
2. Regression Model Evaluation:
3. Classification Model Evaluation:
4. Specialized Model Evaluation
5. Time Series Model Evaluation

XI. MODEL SELECTION

XII. MODEL OPTIMIZATION:
1. Model Complexity
2. Model Assessment
3. Hyperparameter Tuning
4. Regularization Techniques
5. Neural Networks Tuning

XIII. MACHINE LEARNING:
1. Supervised Learning:
i. Regression
ii. Classification
2. Unsupervised Learning:
i. Anomaly Detection
ii. Clustering
iii. Dimensionality Reduction
iv. Autoencoder
3. Semi-supervised Learning
4. Reinforcement Learning
5. Ensemble Learning
6. Transfer Learning

XIV. DEEP LEARNING & NEURAL…