Course Content (For Online Artificial Intelligence(AI) & Machine Learning(ML) Training in Jaipur):-
In this course, you will get knowledge about Artificial Intelligence(AI), Machine Learning(ML), Deep Learning(DL) and understand how Deep Learning solves real world problems.
Module-0 : Prerequisites -Python (Covered in Training)
In this module, you’ll get a complete knowledge of python and it’s libraries which are going to be used in better understanding in problem solving of Deep Learning and Machine Learning(ML).
Topics:
- Getting Started with python
- Data Types and Variables
- Operators
- Structural Data Types-Lists, Tuples, Strings & Dictionaries
- Conditional Code
- Loops and Iterations
- Functions
- Files I/O
- Accessing Web Data
Module-1 : Python Libraries for Data Science
Topics:
- Python Libraries for Data Scientists-
- Numpy
- Scipy
- Pandas
- Scikit-learn
- Matplotlib
- Seaborn
- Plotly
- Reading Data; Selecting and Filtering the Data; Data manipulation, sorting, grouping, rearranging
- Plotting the data
- Descriptive statistics
- Inferential statistics
Module-2 : Machine Learning(ML)
The Math behind Machine Learning(ML): Linear Algebra
- Scalars
- Vectors
- Matrices
- Tensors
- Hyperplanes
The Math Behind Machine Learning(ML): Statistics
- Probability
- Conditional Probabilities
- Posterior Probability
- Distributions
- Samples vs Population
- Resampling Methods
- Selection Bias
- Likelihood
Algorithms of Machine Learning(ML)
- Regression
- Classification
- Clustering
- Reinforcement Learning
- Underfitting and Overfitting
- Optimization
Module-3 : Introduction to Deep Learning(DL)
- Deep Learning: A revolution in Artificial Intelligence(AI)
- Limitations of Machine Learning(ML)
- What is Deep Learning(DL)?
- Advantage of Deep Learning(DL) over Machine learning(ML)
- 3 Reasons to go for Deep Learning(DL)
- Real-Life use cases of Deep Learning(DL)
Module-4 Understanding Fundamentals of Neural Networks using Tensorflow 2.x
In this module, you will get an introduction to Neural Networks and understand its working i.e. how it is trained, what are the various parameters considered for its training and the activation functions that are applied.
Topics:
- How Deep Learning(DL) Works?
- Activation Functions
- Illustrate Perceptron
- Training a Perceptron
- Important Parameters of Perceptron
- Introduction to TensorFlow 2.x
- Installing TensorFlow 2.x
- Defining Sequence model layers
- Model Training
- Digit Classification using Simple Neural Network in TensorFlow 2.x
Module-5 Convolutional Neural Networks (CNN)
In this module, you will understand convolutional neural networks and its applications. You will understand the working of CNN, and create a CNN model to solve a problem.
Topics:
- Introduction to CNNs
- CNNs Application
- Architecture of a CNN
- Convolution and Pooling layers in a CNN
- Understanding and Visualizing a CNN
- Transfer Learning and Fine-tuning Convolutional Neural Networks
Module-6 R-CNN | Region Based CNNs
In this module, you will be able to understand the concept and working of RCNN and why it was developed in the first place. The module will cover various important topics like Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask RCNN.
Topics:
- Regional-CNN
- Pre-trained Model
- Model Accuracy
- Model Inference Time
- Model Size Comparison
- Transfer Learning
- Object Detection – Evaluation
- RCNN – Speed Bottleneck
- Fast R-CNN
- RoI Pooling
- Fast R-CNN – Speed Bottleneck
- Faster R-CNN
- Mask R-CNN
Module-7 Generative Adversarial Network(GAN)
In this module, you will understand what generative adversarial model is and how it works by implementing step by step Generative Adversarial Network.
Topics:
- Understanding GAN
- What is Generative Adversarial Network?
- How does GAN work?
- Step by step Generative Adversarial Network implementation
- Types of GAN
- Recent Advances: GAN
Module-8 Recurrent Neural Networks (RNN)
In this module, you will understand Recurrent Neural Networks and its applications. You will understand the working of RNN, how LSTM are used in RNN, what is Recursive Neural Tensor Network Theory, and finally you will learn to create a RNN model to solve a problem.
Topics:
- Intro to RNN Model
- Application use cases of RNN
- Modelling sequences
- Training RNNs with Backpropagation
- Long Short-Term memory (LSTM)
- Recursive Neural Tensor Network Theory
- Recurrent Neural Network Model
Module-9 Restricted Boltzmann Machine(RBM) and Autoencoders
In this module, you will understand RBM Autoencoders along with their applications. You will understand the working of RBM Autoencoders, illustrate Collaborative Filtering using RBM and understand what are Deep Belief Networks.
Topics:
- Restricted Boltzmann Machine
- Applications of RBM
- Collaborative Filtering with RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
Module-10 Computer Vision
In this module, you will understand about classical image analysis techniques such as Edge detection,watershed,distance transformations using the OpenCV library .Here you will explore the evolution of image analysis ,from classical deep learning techniques.
At the end of this module, you should be able to:
- Introduction to computer vision and Image Processing
- Image processing using OpenCV
- Video processing and Image extraction using OpenCV
- Convolutional Features for visual recognition
- Object ,Face and Gestures Detection using Haar Cascade Classifier
- Object Tracking and Action Recognition
Module-11 Natural Language Processing (NLP)
In this module, you will understand to design NLP applications that perform sentiment analysis and question-answering, create tools to translate languages and summarize text, generate text.
Topics:
- Understanding NLP
- Working with Text Corpus
- Real world example of Text Classification
- NLP Libraries
- NLP with Machine Learning and Deep Learning
- Machine Translation using Attention model.
Module-12 Hands-On Project
In this module, you should learn how to approach and implement a Machine project end to end, the instructor from the industry will share his experience and insights from the industry to help you kickstart your career in this domain. At last we will be having a QA and doubt clearing session for the students.
At the end of this module, you should be able to:
- How to approach a project
- Hands-On project implementation
- What Industry expects
- Industry insights for the Machine Learning domain
- QA and Doubt Clearing Session
This is all about summer, winter and regular training in Artificial Intelligence(AI), Machine Learning(ML) Deep Learning at Goeduhub Technologies-Jaipur. Apart from this student will also complete some real time projects during training.
Outcomes of Artificial Intelligence(AI), Machine(ML) and Deep Learning(DL) Training in Jaipur-
On completion of the course students should be able to:
- Master some of the most sought-after AI, Machine Learning and Deep Learning, Neural Networks and NLP skills
- Learn Deep AI techniques which are transforming industry for high transformation in a post pandemic world
- Practice multiple hours of lab exercises in a lab environment integrating real-world datasets in digital business context
- Learn actual application use cases of AI in real-time Web Applications from top and most recognized Industry leaders Work on an end-to-end AI project and get feedback from a panel of experts
Understand impact of the latest emerging AI trends such as chatbots, image and speech recognition and intelligent automation
Make your own Major and Minor Projects With Online Summer Training
Our Online/Offline Summer Training Students will make Projects in Artificial Intelligence(AI), Machine Learning(ML) & Deep Learning(DL).
Few Projects in Artificial Intelligence(AI) Completed By Our Students During "Online Summer Training in Artificial Intelligence(AI) & Machine Learning(ML)-2020"
Summer Training-Internship program-2021
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