Regression Workshop

1. Linear Regression:
   • Demonstrate how to perform simple linear regression using a small dataset.
   • Show how to visualize the relationship between the input feature and the target variable using scatter plots.
   • Interpret the coefficients and evaluate the model’s performance using metrics such as mean squared error (MSE) or R-squared.
2. Polynomial Regression:
   • Introduce the concept of polynomial regression and how it can capture nonlinear relationships.
   • Show how to create polynomial features from the original input features.
   • Train and evaluate a polynomial regression model and compare it to a linear regression model.
3. Multiple Linear Regression:
   • Extend the linear regression example to include multiple input features.
   • Demonstrate how to handle categorical features using one-hot encoding or feature encoding techniques.
   • Train a multiple linear regression model using the extended dataset and evaluate its performance.
4. Regularization Techniques:
   • Discuss the concept of overfitting and the need for regularization.
   • Illustrate how to apply regularization techniques like L1 (Lasso) and L2 (Ridge) regularization.
   • Compare the effects of different regularization strengths on the model’s coefficients and performance.
5. Evaluation and Cross-Validation:
   • Introduce the importance of model evaluation and the risk of overfitting.
   • Show how to split the data into training and testing sets for evaluation.
   • Discuss the concept of cross-validation and demonstrate how to perform k-fold cross-validation.
6. Visualization of Regression Models:
   • Use plots such as scatter plots, line plots, or residual plots to visualize the model’s predictions and residuals.
   • Demonstrate how to create visualizations that help interpret the model’s performance and understand the relationship between the features and the target variable.

Classification and Clustering

Classification Examples:

1. Binary Classification with Logistic Regression:
   • Introduce binary classification and logistic regression.
   • Show how to preprocess the data, split it into training and testing sets, and perform feature scaling if necessary.
   • Train a logistic regression model using a binary classification dataset and evaluate its performance using metrics like accuracy, precision, recall, and F1-score.
   • Visualize the decision boundary and the predicted probabilities.
2. Multi-class Classification with Support Vector Machines (SVM):
   • Explain the concept of multi-class classification and SVM.
   • Illustrate how to apply one-vs-rest or one-vs-one strategies for multi-class classification using SVM.
   • Train an SVM classifier on a multi-class dataset and evaluate its performance.
   • Visualize the decision boundaries and class separation.
3. Decision Trees for Classification:
   • Introduce decision trees and their ability to perform both binary and multi-class classification.
   • Demonstrate how to train a decision tree classifier and visualize the resulting tree structure.
   • Show how to interpret the decision boundaries and feature importances.

     Clustering Examples:

4. K-means Clustering:
   • Explain the concept of clustering and the K-means algorithm.
   • Generate a synthetic dataset with distinct clusters.
   • Demonstrate how to apply K-means clustering to identify and visualize the clusters.
   • Evaluate the quality of clustering using metrics like silhouette score or inertia.
5. Hierarchical Clustering:
   • Introduce the concept of hierarchical clustering and different linkage methods (e.g., complete, average, ward).
   • Demonstrate how to perform hierarchical clustering on a dataset and visualize the resulting dendrogram.
   • Discuss how to determine the optimal number of clusters using dendrogram analysis or techniques like the elbow method.
6. Density-Based Clustering with DBSCAN:
   • Explain the principles of density-based clustering and the DBSCAN algorithm.
   • Generate a dataset with varying densities and noise.
   • Show how to apply DBSCAN to discover clusters and identify outliers.
   • Visualize the resulting clusters and evaluate the clustering performance.

Neural Networks

1. What are Neural Networks?
   • Brief introduction to the concept of neural networks and their inspiration from the human brain.
   • Explanation of the basic building blocks of neural networks - artificial neurons or perceptrons.
   • Understanding their activation functions and how they process inputs to generate outputs.
   • Overview of the architecture of neural networks, including input layer, hidden layers, and output layer.
   • Explanation of feedforward and backpropagation processes.
   • Different types of activation functions (sigmoid, ReLU, etc.) and their role in neural network computations.
2. Training Neural Networks:
   • Introduction to the process of training neural networks using labeled data.
   • Overview of loss functions and optimization techniques like gradient descent.
   • Explanation of overfitting in neural networks and methods to address it, such as regularization.
3. Convolutional Neural Networks (CNNs):
   • Introduction to CNNs, which are particularly suited for image-related tasks.
   • Explanation of convolutional layers, pooling, and feature extraction.
4. Recurrent Neural Networks (RNNs):
   • Overview of RNNs, designed for sequence data and time series analysis.
   • Explanation of recurrent connections and the vanishing/exploding gradient problem.
   • Discussing the limitations and challenges of neural networks, including data requirements, interpretability, and computational resources.

Time Series:

1. Introduction to Time Series Data

• Define time series data and its characteristics.
• Explain common examples of time series data in different domains.
• Discuss the importance of time series analysis in real-world applications.

1. Preprocessing Time Series Data

• Handle missing values in time series datasets.
• Apply resampling techniques to align time series data.
• Smooth time series data using moving averages or other methods.

1. Time Series Visualization and Feature Engineering

• Visualize time series data using line plots and seasonal decomposition.
• Create lag features and rolling statistics for time series analysis.
• Extract seasonality and trends from time series data.

1. Time Series Forecasting with Traditional Methods

• Introduce forecasting methods like moving averages and exponential smoothing.
• Explain the concept of ARIMA (AutoRegressive Integrated Moving Average) models.
• Demonstrate how to build and evaluate forecasts using traditional methods.

1. Introduction to Recurrent Neural Networks (RNNs) for Time Series

• Define RNNs and their application in sequence data.
• Explain the concept of Long Short-Term Memory (LSTM) networks.
• Implement an LSTM model for time series forecasting using Python and TensorFlow/Keras.

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