Summer Camp: Machine Learning Algorithms With Tensorflow (Python Level 5)

 Objectives:
Learn the basics of machine learning
What is learning?
History of machine learning
Regression vs classification
Introduce TensorFlow
Installing TensorFlow
What is a Tensor?
Low-level API and high-level API
How do Tensors perform computations?
Tensorflow variables
Project 1: Perform basic computations with TensorFlow
Matrix addition, matrix multiplication using tensors in TensorFlow 

 This project aims to teach linear regression fundamentals, including hypothesis setup with the equation 𝑦 = 𝑚 𝑥 + 𝑏 y=mx+b, where 𝑚 m represents weights and 𝑏 b denotes biases. Participants will learn to compute these parameters using gradient descent and understand the mean squared error cost function. Using TensorFlow and NumPy, they will implement a linear regression model, iteratively refining weights and biases over multiple epochs and visualizing model performance. 

 Objectives
Why do we need to tune models?
Convergence problems
Preparation
Strategies for tuning models
Grid Search
Random Search
Data transformation
Project 3: Tune the linear regression model to produce better results
Implement Grid Search
Implement Random Search
Implement a data transformation 

 Objectives:
How do we solve classification problems?
Problems with classification
Strategies and models to use
K-NN Algorithms
How do clustering algorithms work?
How does the K-NN algorithm perform classification
How do we measure distance?
What is the effect of the K hyperparameter?
Project 4: MNIST Classification with K-NN
Import the dataset and process it
Implement the K-NN algorithm
Visualize the results
Tune the model for better results