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https://www.eventshigh.com/detail/chennai/636566186beeca42987dd5ce56c61f01-deep-learning-90-hours-certification

Deep Learning 90+ hours certification course

12.9893286,80.2416418
Sun, 15 Mar 9:30AM - Wed, 25 Mar 6:00PM
3G Institute Of Research Policy Studies , south chennai
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Rs 55000
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Do chatbots, robots, and digital assistants intrigue you?

What about automated vehicles and virtual assistants?

They're all part of the world of artificial intelligence. AI is a field of computer science that focuses on the creation of a machine that can replicate human behavior.

The science fiction of yesterday quickly becomes reality. AI statistics surrounding the business and tech industries are changing. AI has already altered the way we think and interact with each other every day. Whether its in healthcare, education, or manufacturing, AI yields a great deal of success in nearly every industry.

Whether its AIs effect on startups and investments, robotics, big data, virtual digital assistants, the full market overview, or voice search and recognition,Developing skills in A.I is the need of the hour

DEEP LEARNING CERTIFICATION COURSE CURRICULUM

1.0 Foundations (5+Hrs)

 

1.1 Python

1.1.1. Overview of python

1.1.2. Basics of python

1.1.3. Python datastrucutre

1.1.4. Python libraries for AI & ML

1.1.4.1. Numpy Statistical Analysis

1.1.4.2. Pandas

1.1.4.3. Matplotlib Data visualisation

1.1.5. Debugging methodology

1.1.6. Hands on sessions

1.1.7. Milestone project

1.2 Statistics

1.2.1 Probability

1.2.2 Basic statistics

1.2.3 Terminology in statistics

1.2.4 Distributions

1.2.5 Statistical theorems

1.2.5.1. Central Limit theorem

1.2.5.2. Bayes theorem

1.2.6 Descriptive analysis and descriptive statistics

1.2.7 Inferential analysis and Inferential statistics

1.2.8 Statistical Significance

1.2.9 Hypothesis testing

1.2.10 Bias Variance trade-off

 

 

 

2.0 Pre-set up (3+Hrs)

2.1. System requirements

2.2. Installations

2.3. Environment setup

 

3.0 Machine Learning (15+Hrs)

 

3.1. Introduction to Machine Learning

3.2. Analytics and its types

3.3. Machine Learning and its types

3.4. Algorithms and techniques

4.0 Feature Engineering (4+Hrs)

 

4.1. Model selection and tuning

4.2. Feature extraction

4.3. Error metrics and evaluation

4.4. Milestone project

5.0 Decision making methodology (6+Hrs)

 

5.1. Genetic Algorithm

5.2. Linear programming

5.3. Monte Carlo simulations

5.4. Neural networks concepts

6.0 Deep Learning (18+Hrs)

 

6.1 Introduction to deep learning

6.2 Insights of Neural networks

6.3 Fameworks of Neural networks

6.3.1 Tensorflow

6.3.2 Keras

 

7.0 Neural Networks (13+Hrs)

 

7.1 Basics

7.1.1 What is neuron?

7.1.2 What is neural network?

7.1.3 Perceptrons

7.1.4 Hidden Layers

7.1.5 Neuron predictions

7.1.6 Neuron training

7.1.7 Back propogation

7.2 Intermediate

7.2.1 No linear activation function

7.2.2 Back propagation

7.2.3 Vanishing and exploding gradient descent

7.2.4 How to mitigate over fitting?

7.2.5 Pre-trained models

   7.2.6 Transfer learning Resnet

 

8.0 Artificial Neural Network ANN (15+Hrs)

 

8.1 What is ANN?

8.2 Building an ANN model

8.3 Fine-tuning the model

8.3.1 Evaluating

8.3.2 Defect diagnosing

8.3.3 Improving the model

8.3.4 Tuning the ANN model

8.4 Hands-on project

 

 

 

 

9.0 Convolution Neural Network CNN (4+Hrs)

 

9.1 What is CNN?

9.2 Building a CNN model

9.2.1 CNN operation

9.2.2 ReLU layer

9.2.3 Pooling

9.2.4 Flattening

9.3 Fine-tuning the model

9.3.1 Evaluating

9.3.2 Defect diagnosing

9.3.3 Improving the model

9.3.4 Tuning the CNN model

9.4 Hands-on project

 

10.0 Recurrent Neural Network (5+Hrs)

 

10.1 What is RNN?

10.2 Building a RNN model

10.3 Fine-tuning the model

10.3.1 Evaluating

10.3.2 Defect diagnosing

10.3.3 Improving the model

10.3.4 Tuning the RNN model

10.4 LSTMs

10.5 Hands-on project

 

11.0 Reinforcement learning (5+Hrs)

 

11.1 What is reinforcement learning?

11.2 Markov Decision Process - MDP

11.3 Dynamic programming to solve MDP

11.4 Prediction and reinforcement testing(use case)

11.5 Milestone project

 

12.0 Computer vision (5+Hrs)

 

12.1 What is computer vision?

12.2 Importing data(image & video) through different methods

12.3 Image processing

12.3.1 Converting into matrix

12.3.2 Color spaces

12.3.3 Contouring

12.3.4 Filtering

12.3.5 Feature extraction

12.3.6 Sizing and more..

12.4 Image recognition and its applications

12.5 Hands on Project

 

13.0 Advanced AI (5+Hrs)

 

13.1 Advanced Computer vision

13.2 Image recognition in comparison with Basis NN and CNN

13.3 Bi-directional recurrent models

13.4 Object detection and image segmentation

13.5 OpenCV and tensorflow projects

13.6 YOLO, Faster RCNN and SSD models

13.7 Examples with pre-trained models

13.8 Generative Adversial Network GANs

13.9 Applications of GANs

13.10 Hands-on project on facial recognition and identification

13.11 Hands-on project on Object identification.

 

14.0 Career Assistance (3+Hrs)

 

14.1 Resume Building

14.2 Internship

14.3 Interview preparation

14.4 Complete career guidance

 

 

 

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3G Institute of Research Policy Studies A Block 6th floor IIT Madras Research Park, 32, Kanagam Rd, Kanagam, Tharamani, Chennai, Tamil Nadu 600113, India
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