Applied Artificial Intelligence(AI) and Machine Learning(ML) With Deep Learning, Computer Vision, NLP, Image Processing Training in Jaipur.

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Online Instructor Led Live Project based training in Artificial Intelligence(AI), Machine Learning(ML) & Deep Learning

Overview: Artificial Intelligence(AI) & Machine Learning Training in Jaipur

Course-1: Machine Learning using Python

Course-2: Applied AI and ML With Deep Learning, Computer Vision, NLP and Image Processing

Projects & Reviews

"Applied AI and ML With Deep Learning, Computer Vision, NLP, Image Processing" Summer, Winter Training in Jaipur | Register for Summer/Winter/Regular Training

Artificial Intelligence(AI) is such a happening concept these days. And people are showing extreme eagerness to take a course in Artificial Intelligence(AI), Machine(ML) and Deep Learning(DL). This intelligence shown by machines is something that has a wide application and has been able to carve a niche for itself. Although there are many institutes that offer Artificial Intelligence(AI), Machine Learning(ML) and Deep Learning training in Jaipur, you have to be extra cautious while choosing one for yourself. Choose a training institute that promises and deliver quality training.

Best Artificial Intelligence(AI) & Machine Learning(ML) Summer, Winter Training in Jaipur

Get enrolled for the most demanding skill in the world. Artificial Intelligence(AI), Machine(ML) and Deep Learning training will make your career a new height. We at Goeduhub technologies-Jaipur provide you an excellent platform to learn and explore the subject from industry experts. We help students to dream high and achieve it through our project based Summer/Regular training in Artificial Intelligence(AI) & Machine Learning(ML) & Deep Learning in Jaipur

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Artificial Intelligence(AI) & Machine Learning(ML) Summer training in Jaipur

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Python(FREE)

In our Artificial Intelligence(AI) & Machine Learning(ML) & Deep Learning(DL) Training, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

Finally, our Artificial Intelligence(AI) and Machine Learning(ML) training covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks.

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. This will going to be the totally project - based learning and research oriented. You will get the knowledge about how to write research papers and present in conferences at Goeduhub Technologies-Jaipur.

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Course Objectives for Artificial Intelligence(AI) and Machine Learning(ML) Training in Jaipur?

• Learn Python from scratch
• Use Python for Data Science and Machine Learning
• Implement Machine Learning Algorithms
• Learn to use NumPy for Numerical Data
• Learn to use Pandas for Data Analysis
• Data Visualization with MatPlotLib and Seaborn and Plotly
• Understand the intuition behind Artificial Neural Networks
• Understand the concept of Single Layer and Multi Layer Perceptron by implementing them in Tensorflow 2.0
• Learn about the working of CNN algorithm and classify the image using the trained model
• Grasp the concepts on important topics like Transfer Learning, RCNN, Fast RCNN, RoI Pooling, Faster RCNN, and Mask RCNN
• Understand the concept of Boltzmann machine and Auto Encoders
• Implement Generative Adversarial Network in TensorFlow 2.0
• Understand the concept behind Computer Vision and Image Processing
• Work on various projects like Emotion and Gender Detection project and strengthen your skill on OpenCV and CNN
• Understand the concept of RNN, GRU, and LSTM
• Perform Auto-Image Captioning using CNN and LSTM
• Work with text data using the Natural Language Tool Kit (nltk).
• Understand Tableau for Data Analytics

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Module-0 : Prerequisites -Python

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.

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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

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