Summer training at Goeduhub Technologies-Jaipur

Online Instructor-Led Live Training Courses/Workshops details

Artificial Intelligence(AI) Training in Jaipur.

Course-1: Machine Learning using Python  Course-2: Artificial Intelligence & ML & Deep Learning Projects & Reviews

Artificial Intelligence(AI) & Machine Learning(ML) Summer, Winter Training in Jaipur | Register for 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.

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
  • 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

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
  • What is Tensorflow?
  • Tensorflow code-basics
  • Graph Visualization
  • Constants, Placeholders, Variables
  • Creating a Model
  • Step by Step - Use-Case Implementation

Module-5 Deep Dive into Neural Networks using Tensorflow

In this module, you will understand backpropagation algorithm which is used for training Deep Networks. You will know how Deep Learning uses neural network and backpropagation to solve the problems which Machine Learning(ML) cannot.

Topics:

  • Understand limitations of A Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlow
  • TensorBoard
  • Summary

Module-6 Master Deep Networks

In this module, you will get started with the TensorFlow framework. You will understand how it works, its various data types & functionalities. You will learn to create an image classification model.

Topics:

  • Why Deep Learning(DL)?
  • SONAR Dataset Classification
  • What is Deep Learning(DL)?
  • Feature Extraction
  • Working of a Deep Network
  • Training using Backpropagation
  • Variants of Gradient Descent
  • Types of Deep Networks(DL)

Module-7 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-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 Keras

In this module, you will understand how to use Keras API for implementing Neural Networks, the goal is to understand various functions and features that Keras provide to make the task of neural network implementation easy.

Topics:

  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras

Module-11 TFlearn

In this module, you will understand how to use TFlearn API for implementing Neural Networks, the goal is to understand various functions and features that TFlearn provide to make the task of neural network implementation easy.

Topics:

  • Define TFlearn
  • Composing Models in TFlearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFlearn
  • Customizing the Training Process
  • Using TensorBoard with TFlearn
  • Use-Case Implementation with TFlearn

Module-12 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-13 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.

Courses & Fee

  1. Augmented Reality & Virtual Reality - INR 15000/-
  2. AI, ML & Deep Learning - INR 12000/-
  3. Robotic Process Automation (RPA) - INR 12000/-
  4. Big Data HADOOP - INR 12,000/-
  5. Internet of Things (IoT) - INR 10,000/-
  6. Web development using python (Django) - INR 8000/-
  7. Industrial Automation (PLC-SCADA) - INR 8000/- 

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