Software Training Institute in Ahmedabad, Best Software Training Center Gujarat

Artificial Intelligence

Artificial Intelligence

AI is important for its potential to change how we live, work and play. It has been effectively used in business to automate tasks done by humans, including customer service work, lead generation, fraud detection and quality control. In a number of areas, AI can perform tasks much better than humans. Particularly when it comes to repetitive, detail-oriented tasks, such as analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors. Because of the massive data sets it can process, AI can also give enterprises insights into their operations they might not have been aware of. The rapidly expanding population of generative AI tools will be important in fields ranging from education and marketing to product design.

Indeed, advances in AI techniques have not only helped fuel an explosion in efficiency, but opened the door to entirely new business opportunities for some larger enterprises. Prior to the current wave of AI, it would have been hard to imagine using computer software to connect riders to taxis, but Uber has become a Fortune 500 company by doing just that.

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This training will help you learn various aspects of AI like Machine Learning, Deep Learning with TensorFlow, Artificial Neural Networks, Statistics, Data Science ,Python programming through hands-on projects

Course Description:

Lean key AI concepts.

In this course you learn complex theory, algorithms, and coding libraries in a simple way.

Part 1 – Multilayered Neural Network.

Part 2 – Artificial Neural Network

2.1       Introduction

2.2       Topology of Neural Network

Part 3 – TFLearn API for Tensorflow

3.1       Intro to Tensorflow

3.2       Intro to Tensorflow2.0

3.3       Hands-On

Part 4 – Deep Neural Network

4.1       Limitation of Single layer perceptron

4.2       Feed Forward Network

4.3       Back Propagation

4.4.      Optimization Algorithm

4.5       Neural Network with Multi-Variable

4.6       Hands-On

Part 5 – Modeling with Keras

5.1       Introduction

5.2       Model

5.3       Regularization

5.4       Sequential Model

5.5       Functional Model

5.6       Hands-ON

Part 6 – Convolutional Neural Network

6.1       Issues with Feed Forward

6.2       Introduction to convolutional  Network

6.3       Convolutional Layer

6.4       ReLu Layer

6.5       Pooling Layer

6.6       Fully Connected Layer

6.7       Hands-On

Part 7 – Recurrent Neural Network

7.1       Issues with Feed Forward

7.2       Introduction to RNN

7.3       Back Propagation

7.4       Types of RNN

7.5       Issues with RNN

7.6       LSTM(Long Short Term Network)

7.7       Hands-On

Part 8- Autoencoders

8.1       Introduction to Auto-Encoders

8.2       Auto-Encoders Vs Principal Component Analysis

8.3       Architecture of Auto-Encoders

8.4       Types

8.5       Hands-On

Part 9- Restricted Boltzman Machine

9.1       Collaborative Filtering Using RBM

9.2       Hands-On

Part 10 – Deep Learning with TFleaarn

10.1     Intro to TFlearn

10.2     Layers and Built in Operation in TFlearn

10.3     Saving and restoring TFLearn

10.4     Fine Tuning

10.5     Hands-On

So not only will you learn the theory, but you will also get some hands-on practice building your own models.

Course Objective/What will Learn

  • Build an AI
  • Understand the theory behind Artificial Intelligence
  • How Data Science and AI overlap
  • Design of ML Systems
  • Solve Real World Problems with AI
  • Deep Learning Problems


General Programming Skills in any Language (Preferrably Python)

Target Audience:

  • Developers who want to make their career in Machine learning , R and Python
  • Professionals working in the domains of Data Science, Analytics, and BI
  • Professionals employed in fields of search engines and e-commerce