Artificial Intelligence Training (2)

Artificial Intelligence Training in Bangalore

5.00 out of 5 based on 1 customer rating
(1 customer review)


10 Learners Enrolled

Course details:
Classroom Training: 75 hours
Case Studies: 6
Project:  2 Real Time Projects

  • S : Oct 14th Onwards
Select Batch and time
Weekend ,1st Sep ,2019,12:30 Pm - 04:00 Pm
Weekday ,05th Sep, 2019,08:00 Am-10:30 Am

Course description

Artificial Intelligence Training in Bangalore

Learnbay Provides artificial intelligence training  in bangalore which includes Deep Learning ,Natural Language Processing And Computer Visison.Our course helps you to learn various deep learning techniques using  Python programming. Course content is designed by experts to match with the real world requirements for both beginner and advance level.Many real world problems and case studies are implemented throughout the course and discussed in the class with tons of assignments for practice.
This course will benefit you to master Deep learning Skills ,NLP and computer vision Which will prepare you to become a Artificial intelligence expert.

Who Should Attend:
Those who want to become master in Artificial intelligence And  Natural Language Processing
Data Scientist/Analyst Who wants to improve their skills and want to learn Deep Learning/NLP
Freshers/Experienced Professional,Managers,IT professional

Prerequisite For Data Science Course:

You should be familiar with Python Programming and Machine learning  to be eligible for this  course.

Schedule Career Counselling Session

Duration Of Course – 10  Weeks Course  (Weekend Only  3.5  Hours Saturday And Sunday) – 75 Hours Course
4 Months Duration

Course Fee:    Rs. 55,000/- + Taxes

  • Course Features:
  • Online And Classroom data science training in Bangalore by industry experts
  • Classes with 40% theory and 60% hands on
  • Practical Approach With Mini Projects And Case studies.
  • Job Assistance And Placement Support After end of Course.
Click here To Watch Demo/Sample Recordings

Course Content 

Download Course Brochure And Syllabus
  • What is Artificial Intelligence? – Introduction.
  • Importance of  Artificial Intelligence.
  • Types Of Intelligence
  • Application of Artificial Intelligence
  • Difference Between Human And Machine Intelligence
  • Tools and Technologies  used in AI
  • Demand for AI/ML  Professional..

PART A  – Natural Language Procession(4 Weekends-20 Hours)

1. Introduction to NLP & Text Analytics
Introduction to Text Analytics
Introduction to NLP
What is Natural Language Processing?
What Can Developers Use NLP Algorithms For?
NLP Libraries
Need of Textual Analytics
Applications of Natural Language Procession
Word Frequency Algorithms for NLP
Sentiment Analysis
2. Text Pre Processing Techniques
Need of Pre-Processing
Various methods to Process the Text data
Tokenization ,Challenges in Tokenization
Stopping ,Stop Word Removal
Stemming – Errors in Stemming
Types of Stemming Algorithms – Table lookup Approach ,N-Gram Stemmers
3. Distance Algorithms used in Text Analytics
string Similarity
Cosine Similarity Mechanishm – Similarity between Two text documents
Levenshtein distance – measuring the difference between two sequences
Applications of Levenshtein distance
LCS(Longest Common Sequence ) Problems and solutions ,LCS Algorithms
 4. Information Retrieval Systems
Information Retrieval – Precision,Recall,F-score
KNN for document retrieval
K-Means for document retrieval
Clustering for document retrieval
5. Topic Modelling & Dirchlett Distributions
Introduction to Topic Modelling
Latent Dirchlett Allocation
Adavanced Text Analytics & NLP
Introduction to Natural Language Toolkit
POS Tagging
6.5. Projects And Case Studies

a. Sentiment analysis for twitter, web articles
b. Movie Review Predictionc. Summarization of Restaurant Reviews 


 PART B-Deep Learning Using TensorFlow And Keras(6 Weekends – 35 hours )

 1. Introduction to Deep Learning
What is Deep Learning?
Machine Learning VS Deep Learning
Limitations Of Machine Learning
Problems that Deep Learning Can Solve.
Advantages of Deep Learning over Machine  Learning
2. Introduction to Tensor Flow
Installing TensorFlow
Simple Computation ,Contants And Variables
Types of file formats in TensorFlow
Creatting A Graph – Graph Visualization
Creating a Model  – Logistic Regression Model Building
TensorFlow Classification Examples
5. Recurrent Neural Networks (RNN)
Introducing Recurrent Neural Networks
skflow – RNNs in skflow
Application use cases of RNN
Manual Creation of RNN
Long Short-Term memory (LSTM) And GRU theory
Restricted Boltzmann Machine(RBM) And Autoencoders
Collaborative Filtering with RBM
Dimensionality Reduction with Linear Autoencoder
4. Convolutional Neural Network(CNN)
Convolutional Layer Motivation
Convolutional Layer Application
Architecture of a CNN
Pooling Layer Application
Deep CNN
Understanding and Visualizing a CNN
5. Recurrent Neural Networks (RNN)
Introducing Recurrent Neural Networks
skflow – RNNs in skflow
Application use cases of RNN
Manual Creation of RNN
Long Short-Term memory (LSTM) And GRU theory
Restricted Boltzmann Machine(RBM) And Autoencoders
Collaborative Filtering with RBM
Dimensionality Reduction with Linear Autoencoder
6. Understanding Of TFLearn APIs 
Getting Started With TFLearn
High-Level API usage -Layers,
Built-in Operations,Training and Evaluatiion-Customizing the Training Process,Visualization APIs
Sequential And Functional Composition
Fine tuning,
Using TensorBoard with TFLearn
7.  Understanding Of Keras APIs 
Understanding Keras API for implementing Neural Networks.
Getting Strated With Keras APIs
Keras Model ,Sequential And Functional Model,shared layers,Composig a Model with Keras API
BAtch Normalization
Tensor Board With Keras
Projects And Case Studiesa.Building a CNN for Image Classification 
b. SPAM Prediction Using RNN


PART C  – Computer Vision (   3 Weekends – 20 hours )

1. Introduction to Computer Vision
Introduction to computer Vision
Computer Vision overview
Historical Perspective
2. Image Classification
Data Driven approach
K-nearest Neig
3. OpenCv Library
Opencv Installation And Python API
Drawing shapes ,Image Processing
Image Rotation and Thresholding
Image Filtering – Gaussian Blur,Median Blur
Feature Detection – Canny Edge Detector5. Convolutional Neural Network
Activation Functions, Initialization
Dropout, Update Rules
4. Use of Neural Network in CV
Multi-Layer Perceptron
Backpropagation6. Object Detection(SSD)
Single Shot MultiBox Detector,
Object Localization
How would you find an object in an image?
The Problem of Scale and Shape
SSD in TensorflowFace Detection 7. Project Using Computer vision And
Deep Learning


Certificates On successful Completion of Project  And Assessment.


Course FAQ

1. What are the profiles of Trainers?
Our Trainers have relevant industry experience and are working in MNC as data scientist.

2.How many Case studies and Projects are covered in the course?
Course has multiple case studies and mini Project.Our course is designed by industry experts.
Course features many real time problems.Please refer course content for more details.

3. Do i Need to carry my own laptop?What the the softwares required?
Yes,You need to carry your own laptop.To start with ,You need to install R And R studio installed in your system.
Both Of these are open source and in first class,trainer will help you to setup the environment in your system.

4. Can i Attend a Demo Session before enrolling for the course?
Of course,You can attend a Free live Demo Session before enrolling for the Course.

5. Are the session Online or Classroom?
We provide both live Online and classroom session.You can opt for online or classroom based on your

6. Will i Get Class Recording if i Enroll for Classroom Session?
Our live Classroom Sessions are recorded and after the session ,Class recording will be shared to you.

Sample Class Recording

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