• Anyone like any stream programmer, Analyst, data engineer, want to started their career or know more about Machine Learning and Deep Learning.

• Basic programming knowledge and basic understanding of machine learning concept.

Course Outline:

1. Introduction and Basic Knowledge

• Overview of Machine Learning
• Different type of ML
• IDE (Anaconda) Installation and Intro.
• Simple Program of basic ML and Hands On.

2. Introduction of 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

3. Introduction of TensorFlow and Keras

• Why TensorFlow and Keras
• Difference in Tensorflow and keras
• Sample Code and Hands on Tensorflow and Keras

4. Introduction to Convolutional Neural Networks

• Convolutional layers
• Pooling layers
• Kernel
• Stride
• Padding
• Pooling
• Flatten
• Fully connected layers

5. Building CNN using TensorFlow

• Image recognition
• Hyperparameter tuning
• Image augmentation
• Visualize Modes TensorBoard

6. Recurrent Neural Network

• Why RNN and where to use
• Basic concept and architecture of RNN
• Sample Code and Hands on

7. LSTM (Long Short Term Memory)

• Why LSTM and where to use
• Basic difference among ML, DL, RNN, LSTM
• Basic concept and architecture of RNN

8. RNN: Building Code

• Pre-processing text
• Word Embeddings
• Natural Language Processing

9. Real Time Object Detection

• YOLO (You Only Look Once) basic and Installation
• Hands on YOLO
• Object Detection

Training material provided:

Yes (Digital format)