• Candidates with Computer Science or Electrical Engineering degree or equivalent experience and pursuing a career in Robotics.

• Basic Python Programming experience

Course Outline:

• Course Introduction
• Overview of Machine Learning

Milestone 1: Intro to Google Colab

• Introduction to neural networks
• Introducing Perceptrons
• Step Function
• Updating the weights
• Hidden Layers
• Activation functions
• Loss functions
• Gradient descent
• Back propagation
• Vanishing gradient problem and ReLU
• Understanding the intuition behind neural networks
• Introduction to TensorFlow and Keras

Milestone 2: Using TensorFlow to implement a neural network

• Sequential API
• Keras layers
• Loss functions
• Optimizers
• Compile model
• Model summary
• Fit model
• From Deep Neural Networks to Deep Learning
• Understanding unstructured data
• The architecture of deep learning
• Shared weights
• Introduction to Convolutional Neural Networks (CNN)
• Convolutional layers
• Pooling layers
• Fully connected layers

Milestone 3: Building a CNN in TensorFlow

• Image recognition
• Hyperparameter tuning
• Image augmentation
• Visualize Models using TensorBoard
• Keras functional API
• Inputs
• Layers
• Model
• Image Segmentation
• Introduction to Recurrent Neural Networks (RNN)
• Recurrence
• Tanh activation function
• LSTM

Milestone 4: Building an RNN in TensorFlow

• Preprocessing text
• Word Embeddings
• Natural Language Processing
• Sentiment analysis

Milestone 5: Deploying a TensorFlow Model

• TensorFlow serving
• Flask and TensorFlow
• Conclusion: Next Steps

Structured Activity/Exercises/Case Studies:

• Milestone 1: Google Colab
• Milestone 2: Using TensorFlow to implement a neural network
• Milestone 3: Building a CNN in TensorFlow
• Milestone 4: Building an RNN in TensorFlow
• Milestone 5: Deploying a TensorFlow Model

Training material provided: Yes (Digital format)