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Computer Science
Artificial Intelligence
Python for AI
1. Python Fundamentals for AI
2. Essential Libraries for Data Science and AI
3. Machine Learning with Scikit-Learn
4. Deep Learning Frameworks
5. Specialized AI Applications
6. Model Deployment and Production
Machine Learning with Scikit-Learn
Machine Learning Fundamentals
Types of Machine Learning
Supervised Learning
Regression Problems
Classification Problems
Unsupervised Learning
Clustering
Dimensionality Reduction
Association Rules
Semi-supervised Learning
Reinforcement Learning Basics
Machine Learning Workflow
Problem Definition
Data Collection
Data Exploration
Data Preprocessing
Feature Engineering
Model Selection
Model Training
Model Evaluation
Model Deployment
Model Monitoring
Bias-Variance Tradeoff
Overfitting and Underfitting
Cross-Validation Concepts
Data Preprocessing
Data Cleaning
Handling Missing Values
Outlier Detection
Data Quality Assessment
Feature Scaling
StandardScaler
MinMaxScaler
RobustScaler
Normalizer
QuantileTransformer
Encoding Categorical Variables
One-Hot Encoding
Label Encoding
Ordinal Encoding
Target Encoding
Binary Encoding
Feature Engineering
Feature Creation
Feature Transformation
Polynomial Features
Interaction Features
Feature Selection
Univariate Selection
Recursive Feature Elimination
Feature Importance
L1-based Selection
Variance Threshold
Data Splitting
Train-Test Split
Train-Validation-Test Split
Stratified Splitting
Time Series Splitting
Supervised Learning Algorithms
Linear Models
Linear Regression
Ordinary Least Squares
Ridge Regression
Lasso Regression
Elastic Net
Logistic Regression
Binary Classification
Multiclass Classification
Regularization in Logistic Regression
Tree-based Models
Decision Trees
Classification Trees
Regression Trees
Tree Pruning
Feature Importance
Ensemble Methods
Random Forest
Extra Trees
Gradient Boosting
AdaBoost
Voting Classifiers
Bagging
Instance-based Learning
K-Nearest Neighbors
Distance Metrics
Choosing K
Weighted KNN
Support Vector Machines
Linear SVM
Non-linear SVM
Kernel Functions
SVM for Regression
Naive Bayes
Gaussian Naive Bayes
Multinomial Naive Bayes
Bernoulli Naive Bayes
Neural Networks
Multi-layer Perceptron
Activation Functions
Backpropagation
Unsupervised Learning Algorithms
Clustering Algorithms
K-Means Clustering
Choosing Number of Clusters
K-Means++
Mini-batch K-Means
Hierarchical Clustering
Agglomerative Clustering
Linkage Criteria
Dendrograms
Density-based Clustering
DBSCAN
OPTICS
Gaussian Mixture Models
Spectral Clustering
Dimensionality Reduction
Principal Component Analysis
PCA Theory
Explained Variance
PCA for Visualization
Linear Discriminant Analysis
Independent Component Analysis
t-SNE
UMAP
Factor Analysis
Association Rule Learning
Market Basket Analysis
Apriori Algorithm
FP-Growth
Model Evaluation and Validation
Evaluation Metrics
Regression Metrics
Mean Absolute Error
Mean Squared Error
Root Mean Squared Error
R-squared
Adjusted R-squared
Mean Absolute Percentage Error
Classification Metrics
Accuracy
Precision
Recall
F1-Score
Specificity
Sensitivity
Confusion Matrix
Classification Report
Probability-based Metrics
ROC Curve
AUC-ROC
Precision-Recall Curve
Log Loss
Multi-class Metrics
Macro Averaging
Micro Averaging
Weighted Averaging
Cross-Validation Techniques
K-Fold Cross-Validation
Stratified K-Fold
Leave-One-Out Cross-Validation
Leave-P-Out Cross-Validation
Time Series Cross-Validation
Nested Cross-Validation
Model Selection
Validation Curves
Learning Curves
Model Comparison
Statistical Significance Testing
Hyperparameter Tuning
Hyperparameter Optimization Methods
Manual Search
Grid Search
Random Search
Bayesian Optimization
Genetic Algorithms
Hyperparameter Tuning Tools
GridSearchCV
RandomizedSearchCV
Hyperopt
Optuna
Advanced Tuning Strategies
Early Stopping
Successive Halving
Multi-fidelity Optimization
Model Pipelines and Workflow
Scikit-Learn Pipelines
Creating Pipelines
Pipeline Components
Nested Pipelines
Feature Unions
Combining Features
Parallel Feature Processing
Custom Transformers
Creating Custom Transformers
Transformer Interface
Pipeline Persistence
Saving Pipelines
Loading Pipelines
Version Control for Models
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