Artificial Intelligence and Deep Learning Certification Training

Become an Artificial Intelligence and Deep Learning Engineer by mastering concepts from basics to advanced with this course, designed by experts, filled with real-world projects and hands-on exercises.

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Watch
INTRO VIDEO

Duration
6 Weeks

Start Date
27th Mar, 2022

Format
Online

EMI Options
No interest EMI

Program Overviews

Key Highlights

36+ Hours of interactive learning
30+ Hours of exercise and project work
5+ Projects and case studies
Lifetime access to LMS
o Attend as many batches for lifetime
24/7 Technical Support
Resume Building
Placement Assistance

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Artificial Intelligence and Deep Learning Certification Training

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Choose your Dates specialisations, Receive industry mentorship, Dedicated career support,
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Batches Date
TIME TABLE

Date	Days	Time
27th Mar	Sat & Sun (Weekend)	8:00 PM IST to 11:00 PM IST
2nd Apr	Sat & Sun (Weekend)	8:00 PM IST to 11:00 PM IST
9th Apr	Sat & Sun (Weekend)	8:00 PM IST to 11:00 PM IST
17th Apr	Sat & Sun (Weekend)	8:00 PM IST to 11:00 PM IST

Course Fees
Details & Offers

Was $ 300.00

Now $ 199.00

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Syllabus

Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects, assignments and live sessions

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Module 1: Python, libraries and its application

• Basics of Python
• OOPs Concept in Python
• Introduction to NumPy
• Introduction to Pandas
• Data Pre-processing
• Data Manipulation
• Data Visualization

Module 2:Understanding predictive analytics

• Fundamentals of Statistics
• Generalized Linear Models
• Regression and Clustering

Module 3: Getting started with Machine Learning

• What is Machine Learning?
• Supervised Learning – Regression
• Supervised Learning – Classification
• Model Selection and Boosting
• Unsupervised Learning
• Dimensionality Reduction
• Association Rules Mining and Recommendation

Module 4: Understanding Time Series Analysis

• What is a Time Series?
• Time Series Analysis techniques and applications
• Components of Time Series
• Moving average
• Smoothing techniques
• Exponential smoothing
• Univariate time series models
• Multivariate time series analysis
• Arima model
• Time Series in Python

Module 5: Graphical Models

• Understanding graphical model
• Bayesian Network
• Inference
• Model learning

Module 6: Introduction to Reinforcement Learning

• Module 6: Introduction to Reinforcement Learning
• Getting started with Reinforcement Learning
• Bandit Algorithms and Markov Decision Process
• Dynamic Programming and Temporal Difference Learning methods
• What is Deep Q Learning?

Module 7: Learning Natural Language Processing

• Text Preprocessing and Natural Language Processing
• Analyzing Sentence Structure
• Text Classification
• Sentiment Analysis

Module 8: Introduction to Deep Learning

• What is Deep Learning?
• Why Deep Learning?
• Advantage of Deep Learning over Machine learning
• 3 Reasons to go for Deep Learning
• Real-Life use cases of Deep Learning

Module 9: Understanding Neural Networks With Tensorflow

• How Deep Learning Works?
• Activation Functions
• Illustrate Perceptron
• Train a Perceptron
• Parameters of Perceptron
• TensorFlow
• Graph Visualization
• Constants, placeholders, and variables
• Create a Model

Module 10: Advanced Neural Networks With Tensorflow

• Understand limitations of a Single Perceptron
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• What is a backpropagation?
• Getting started with TensorBoard

Module 11: Deep Networks

• What is Deep Network?
• Why Deep Networks?
• Understand How Deep Network Works?
• How Backpropagation Works?
• Illustrate Forward pass, Backward pass
• Different variants of Gradient Descent
• Types of Deep Networks

Module 12: Convolutional Neural Networks (CNN)

• What is CNN?
• Application of CNN
• Architecture of a CNN
• Convolution and Pooling layers in a CNN

Module 13: Recurrent Neural Networks (RNN)

• 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 14: Restricted Boltzmann Machine (RBM) and Autoencoders

• Introduction to Restricted Boltzmann Machine
• Applications of RBM
• Collaborative Filtering with RBM
• Getting started with Autoencoders
• Autoencoders applications

Module 15: Understanding Keras Api

• Here you will learn how to implement Keras API and how to use Keras with TensorBoard.
• Getting started with Keras
• Compose Models in Keras
• What is sequential composition?
• What is functional composition?
• Predefined Neural Network Layers
• What is Batch Normalization?
• Save and Load a model with Keras
• Customize the model training process

Module 16: Tflearn Api

• What is TFLearn?
• Compose Models in TFLearn
• Sequential Composition
• Functional Composition
• Predefined Neural Network Layers
• Batch Normalization
• Save and Load a model with TFLearn
• Customize the Training Process

Industry Projects

Learn through real-life industry projects sponsored by top companies across industries

Engage in collaborative projects with student-mentor interaction

Benefit by learning in-person with expert mentors

Personalised subjective feedback on your submissions to facilitate improvement

Project 1: Build a Machine Learning model which should be able to detect multiple faces when shown. Use OpenCV to perform the operations.

Provide your machine with the multiple dataset of multiple persons in OpenCV, and after running it, it should be able to detect those faces and display their names whenever detected on the screen or camera. A Dataset which should consist of everyone’s face images into subfolders by names. Each person’s dataset should consist of at least 3 images which will be used to verify the operation of your model

Project 2: Make a Hybrid Deep Learning Model by Analyzing the Credit Card Applications Data Set

Build a model to identify the frauds from the Data Set. Then, move to building an advanced deep learning mapping model to identify and predict the probability that each customer cheated. First, build the unsupervised deep learning branch from your hybrid deep learning model. Second, develop the supervised learning branch and then compose this hybrid deep learning model comprising both supervised and unsupervised deep learning.