Machine Learning: Hands On
- Created By raju2006
- Last Updated December 29th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Artificial Intelligence and machine learning are the cornerstones of the next revolution in computing. These technologies hinge on the ability to recognize patterns then, based on data observed in the past, predict future outcomes.
Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two very popular concepts Machine Learning and Deep Learning. But lately, Deep Learning is gaining much popularity due to it’s supremacy in terms of accuracy when trained with huge amount of data. Practically, Deep Learning is a subset of Machine Learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.
Demand for skilled Deep Learning Engineers is booming across a wide range of industries, making this Deep Learning course with Keras and Tensorflow certification training well-suited for professionals at the intermediate to advanced level.
Course Code/Duration:
BDT189 / 3 Days
Learning Objectives:
After this course, you will be able to:
- Understanding the intuition behind Artificial Neural Networks.
- Understanding the basic to advance concept of Convolutional Neural Networks.
- Where and why to use Recurrent Neural Networks.
- Apply CNN in practice.
- Hands on RNN with real life use case.
- Hands on learning on TensorFlow and Keras
- Building CNN Model using TensorFlow.
- Understanding LSTM and their use case.
- Real time Object Detection using YOLO
- Basic programming knowledge and basic understanding of machine learning concept.
- Anyone like any stream programmer, Analyst, data engineer, want to started their career or know more about Machine Learning and Deep Learning.
- Anyone like any stream programmer, Analyst, data engineer, want to started their career or know more about Machine Learning and Deep Learning.
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)
