Agenda

5:30 PM

Friday, July 7th: Introduction to Machine Learning

- Introduction to machine learning concepts, including supervised and unsupervised learning. - Overview of common machine learning algorithms, such as linear regression and decision trees. - Understanding the workflow of a machine learning project.

5:30 PM

Friday, July 14th: Data Preprocessing and Model Training

- Techniques for handling missing data, categorical variables, and data normalization. - Data preprocessing techniques. - Splitting data into training and testing sets. - Introduction to model training, loss functions, and optimization algorithms. - Practical exercises for data preprocessing and model training.

5:30 PM

Friday, July 28st: Model Evaluation and Performance Metrics

- Introduction to evaluation metrics. - Techniques for model evaluation. - Understanding the concept of overfitting and regularization techniques. - Practical exercises to evaluate and improve model performance.

3:30 PM

Friday, August 4th: Introduction to Neural Networks and TensorFlow.js

- Introduction to neural networks, including layers, activation functions, and backpropagation. - Overview of different types architechtures of neural networks. - Introduction to TensorFlow.js for building and deploying neural networks in web applications. - Practical examples and demonstrations using TensorFlow.js to showcase the capabilities of neural networks.