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

• 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.

Course Outline:

1. Handling Imbalanced Datasets
2. 1. • 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