Week 1
Lesson 1
 Intro To TensorFlow
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
 Week 2
Lesson 2
Linear Regression
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.
 Week 3
Lesson 3
Model Tuning
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
 Week 4
Lesson 4
K-NN Clustering
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
 Week 5
Lesson 5
Decision Forests
Objectives:
What are Ensembles and Decision Forests?
How do decision trees work and their shortcomings?
What is an ensemble and how do they make predictions?
What is a decision forest?
Tuning the forest
What is Gradient boost?
What hyperparameters does a Decision Forest have? 
Project 5: Fraud Detection using Decision Forests
Preprocess data
Create and train model
Visualize results
Test other optimization methods to produce the best model
 Week 6
Lesson 6
 Intro To Neural Networks
Objectives
Cover the basics of Neural Networks
Neural Network playground
Nodes, weights, bias, Linear combination
Input Layer
Hidden Layer
Output Layer
Explain data manipulation and ways to view data
Length of dataset
Shape of dataset
Preprocess images
Verify the images are in the correct format
Project 6: Fashion MNIST dataset preparation
Import the dataset
Prepare dataset for machine learning
 Week 7
Lesson 7
Neural Networks Continued
Objectives
What are Feed-forward neural networks?
Activation functions (Relu)
Loss functions (Cross-entropy)
Optimizer functions (ADAM)
Feed forward
Backpropagation
Project 7: Fashion MNIST
Flatten dataset for model
Create and train the neural network
Visualize the results
 Week 8
Lesson 8
 Convolutional Neural Network
Objectives:
Explain Convolutional Neural networks 
The differences with feed-forward neural networks 
What is a convolutional layer?
When to use CNN?
Project 8: CIFAR10 database classification
Prepare the dataset
Create the CNN and train it
Visualize the results - Training Curves
 Week 9
Lesson 9
CNN Tuning & Final Project Workshop
Regularization
Why do we need regularization?
What is L2 regularization?
What is dropout regularization?
Mini Project 9: Tune CIFAR10 model
Implement dropout and L2 regularization
View results
Final Project Workshop
Discuss ideas for a project
Help students find a dataset
Begin work on projects
 Week 10
Lesson 10
Final Project
Final Projects
Students will add finishing touches to their projects
Students will take turns presenting their work

Final Project Ideas
Face Identification
Character recognition
Create ensembles of models to tackle a difficult challenge
Analyze the effects of different hyperparameters for models