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.

Watch
INTRO VIDEO

Duration
6 Weeks

Start Date
10th Oct, 2021

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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IND : +91-8003428234

US : +1- 833-777-233 Toll Free 

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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Course Fees

Choose your Dates specialisations,   Receive industry mentorship,   Dedicated career support,  
learn tools & languages & much more

Batches  Date 
TIME TABLE 

  Date  Days  Time 
10th Oct Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST
16th Oct Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST
23rd Oct Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST
31st Oct Sat & Sun (Weekend) 8:00 PM IST to 11:00 PM IST

Course Fees
Details & Offers 

Was $ 258.00

Now $ 175.00

This Offer   Expires in

Syllabus 

Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects, assignments and live sessions
• Basics of Python
• OOPs Concept in Python
• Introduction to NumPy
• Introduction to Pandas
• Data Pre-processing
• Data Manipulation
• Data Visualization
• Fundamentals of Statistics
• Generalized Linear Models
• Regression and Clustering
• What is Machine Learning?
• Supervised Learning – Regression
• Supervised Learning – Classification
• Model Selection and Boosting
• Unsupervised Learning
• Dimensionality Reduction
• Association Rules Mining and Recommendation
• 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
• Understanding graphical model
• Bayesian Network
• Inference
• Model 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?
• Text Preprocessing and Natural Language Processing
• Analyzing Sentence Structure
• Text Classification
• Sentiment Analysis
• 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
• How Deep Learning Works?
• Activation Functions
• Illustrate Perceptron
• Train a Perceptron
• Parameters of Perceptron
• TensorFlow
• Graph Visualization
• Constants, placeholders, and variables
• Create a Model
• Understand limitations of a Single Perceptron
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• What is a backpropagation?
• Getting started with TensorBoard
• 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
• What is CNN?
• Application of CNN
• Architecture of a CNN
• Convolution and Pooling layers in a CNN
• 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
• Introduction to Restricted Boltzmann Machine
• Applications of RBM
• Collaborative Filtering with RBM
• Getting started with Autoencoders
• Autoencoders applications
• 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
• 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

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    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.

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    What Our Learners Are Saying?

    Where our Alumni Works 

    Learn from leading faculty and industry leaders
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    Archit Jha

    Avatar Pre Sales Consultant
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    Suvarchala Kopella

    Avatar System Engineer
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    Avatar Cloud Consultant

    Certification

    Master's Degree from Eduranz

    Complete all the courses successfully to obtain this prestigious recognition from LJMU.
  • Connect with Global network of Faculty
  • Get access to complete digital library
  • Earn a Master's degree same as on-campus degree at 1/10th the cost
  • Corporate Training

  • Customized Learning
  • Enterprise Solution
  • 24x7 Support
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