• This course is designed for Data Scientists, Data Engineers, Software Engineers, Software Architects, and Quality Assurance Engineers who have completed the “Kickstart AI and Machine Learning” course.

• Basic understanding of machine learning concepts (or completion of the “Kickstart AI and Machine Learning” course). No prior knowledge of neural networks is required.

Course Outline:

1. Handling Imbalanced Datasets

• Understanding challenges for classification problems due to imbalanced target variable
• Apply techniques to handle imbalanced datasets
• Lab: Visualization and applying different techniques to resolve this problem

2. Implementing Hyper-parameter tuning

• Learning importance of hyper parameters on model performance
• Use techniques for hyperparameter tuning such as Grid Search and K-Folds
• Lab: To apply Grid Search and K-Fold to various machine learning algorithms

3. Applying Dimensionality Reduction

• Understand dimensionality reduction on high dimensional data
• Implement common dimensionality reduction techniques
• Lab: Applying Principal Component Analysis (PCA)

4. Working with TensorFlow Tensors

• Gain practical experience with TensorFlow tensors, its creation, manipulating them
• Lab: Working with Tensors

5. Comprehend Neural Networks & Activation Functions

• Get an overview of neural network architecture and its components
• Understand how neural networks learns from data and their role in various machine learning tasks
• Understand various activation functions used in neural networks
• Lab: Activations functions and basic neural network

6. Build and Train Neural Networks

• Learn the process of designing and constructing neural network models
• Understand layer configurations, activation functions, optimizers and training procedures
• Lab: Implement a neural network using TensorFlow and Keras

Training material provided: Yes (Digital format)

Hands-on Lab: All labs will be conducted using Google Colaboratory. Participants must have a Google Email ID to access the lab environment.