Artificial Intelligence Certification Course

Edureka

Develop an unbeatable skill in text analytics by doing Edureka's Artificial Intelligence Certification Course.

Online

5 Weeks

₹ 16639 20799

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based	Frequency of Classes Weekends

Course overview

Edureka's Artificial Intelligence Certification Course guides a candidate step by step to nlp and text analytics using python's programming language. Anyone working with text and data and little knowledge of python programming language can take up this course. However, they provide a complimentary course to brush up skills on python programming.

Nlp is a form of artificial intelligence which has a great significance on the way humans and computers interact. This course has a lot of practical situations where candidates can apply what they have learnt. Topics such as text processing, semantics analysis, machine learning and other such topics have been discussed.

Their syllabus is designed in such a manner that they take a candidate through all the important concepts which are required while applying the learning of nlp. It discusses real life problems, and after this course one can build their own machine learning model for text classification.

The highlights

Instructor led sessions
Assignments
Real life case studies
Lifetime access
24x7 expert support
Certification from Edureka
Learning forum

Program offerings

Online classes
Assignments

Course and certificate fees

Fees information

₹ 16,639 ₹20,799

The fees for the course Artificial Intelligence Certification Course is -

Head	Amount in INR
Original price	Rs. 19,995
Discounted price	Rs. 17,995

*No Cost EMI starts at Rs. 5,999 / month

certificate availability

Yes

certificate providing authority

Edureka

Who it is for

Business analyst Analytics manager

Edureka's Artificial Intelligence Certification Course is recommended for :

Developers aspiring to be a ‘data scientist'
A college student having exposure to programming to a technical architect/lead in an organisation
Analytics managers who are leading a team of analysts
'Python' professionals who want to design automatic predictive models on text data
Business analysts who want to understand text mining techniques
It is open for everyone

Eligibility criteria

Education

Candidates taking up this course should know a little about python programming and should have a good understanding of the concept of machine learning. However, they have a complementary course on python offered by edureka.

Certification qualifying details

Candidates will be given a certificate of completion only after clearing all the projects and submitting the tests. On completion of the programme, the candidates will be awarded with certification of completion.

What you will learn

Knowledge of python

After completing this course successfully one will be able to :

Build your own text classifier
Know processes such as text processing, semantic analysis, machine learning.
Work on nltk corpora
analyse sentence structure
Extract, clean and pre process text
Handle real time data efficiently
Know deeply about role played by a 'nlp' engineer
Master the art of step by step text processing using i python notebooks
Gain expertise to handle business in future , living in the present
Use n-gram models to model and analyze the bag of words from corpus
Learn about latent semantic analysis and its usage in the processing of context aware semantic content
Learn about bag of words modelling and tokenization of text
Learn techniques to access or modify some of the most common file types
Learn basics of nlp in the most popular python library : nltk
Learn about converting text to vectors using word frequency count, tf-idf etc.

The syllabus

Module 1 - Introduction to Text Mining and NLP

Topics

Overview of Text Mining
Need of Text Mining
Natural Language Processing (NLP) in Text Mining
Applications of Text Mining
OS Module
Reading, Writing to text and word files
Setting the NLTK Environment
Accessing the NLTK Corpora

Hands-on/Demo

Install NLTK Packages using NLTK Downloader
Accessing your operating system using the OS Module in Python
How to read json format, understand key-value pairs, and for that matter, understand uses of pkl files

Skills

Reading & Writing .txt Files from/to your Local
Reading & Writing .docx Files from/to your Local
Working with the NLTK Corpora

Module 2 - Extracting, Cleaning and Preprocessing Text

Topics

Tokenization
Frequency Distribution
Different Types of Tokenizers
Bigrams, Trigrams & Ngrams
Stemming
Lemmatization
Stopwords
POS Tagging
Named Entity Recognition

Hands-on/Demo

Regex, Word, Blankline, Sentence Tokenizers
Bigrams, Trigrams & Ngrams
Stopword Removal
UTF encoding, dealing with URLs, hashtags
POS Tagging
Named Entity Recognition (NER)

Skills

Tokenization
Stopword Removal
UTF encoding
POS Tagging
Named Entity Recognition (NER)

Module 3 - Analyzing Sentence Structure

Topics

Syntax Trees
Chunking
Chinking
Context Free Grammars (CFG)
Automating Text Paraphrasing

Hands-on/Demo

Parsing Syntax Trees
Chunking
Chinking
Automate Text Paraphrasing using CFG’s

Skills

Chunking
Chinking
Automate Text Paraphrasing

Module 4 - Text Classification-I

Topics

Machine Learning: Brush Up
Bag of Words
Count Vectorizer
Term Frequency (TF)
Inverse Document Frequency (IDF)

Hands-on/Demo

Demonstrate Bag of Words Approach
Working with CountVectorizer()
Using TF & IDF

Skills

Bag of Words
CountVectorizer()
TF-IDF

Module 5 - Introduction to Deep Learning

Topics

What is Deep Learning?
Curse of Dimensionality
Machine Learning vs. Deep Learning
Use cases of Deep Learning
Human Brain vs. Neural Network
What is Perceptron?
Learning Rate
Epoch
Batch Size
Activation Function
Single Layer Perceptron

Hands-on/Demo

Single Layer Perceptron

Skills

Curse of Dimensionality
Single Layer Perceptron

Module 6 - Getting Started with TensorFlow 2.0

Topics

Introduction to TensorFlow 2.x
Installing TensorFlow 2.x
Defining Sequence model layers
Activation Function
Layer Types
Model Compilation
Model Optimizer
Model Loss Function
Model Training
Digit Classification using Simple Neural Network in TensorFlow 2.x
Improving the model
Adding Hidden Layer
Adding Dropout
Using Adam Optimizer

Hands-on/Demo

Classifying handwritten digits using TensorFlow 2.0

Skills

Installing and Working with TensorFlow 2.0

Module 7 - Convolution Neural Network

Topics

Image Classification Example
What is Convolution
Convolutional Layer Network
Convolutional Layer
Filtering
ReLU Layer
Pooling
Data Flattening
Fully Connected Layer
Predicting a cat or a dog
Saving and Loading a Model
Face Detection using OpenCV

Hands-on/Demo

Saving and Loading a Model
Face Detection using OpenCV

Skills

Image Classification using CNN
Face Detection using OpenCV

Module 8 - Regional CNN

Topics

Regional-CNN
Selective Search Algorithm
Bounding Box Regression
SVM in RCNN
Pre-trained Model
Model Accuracy
Model Inference Time
Model Size Comparison
Transfer Learning
Object Detection – Evaluation
mAP
IoU
RCNN – Speed Bottleneck
Fast R-CNN
RoI Pooling
Fast R-CNN – Speed Bottleneck
Faster R-CNN
Feature Pyramid Network (FPN)
Regional Proposal Network (RPN)
Mask R-CNN

Hands-on/Demo

Transfer Learning
Object Detection

Skils

Transfer Learning
Object Detection
Mask R-CNN

Module 9 - Boltzmann Machine & Autoencoder

Topics

What is Boltzmann Machine (BM)?
Identify the issues with BM
Why did RBM come into the picture?
Step-by-step implementation of RBM
Distribution of Boltzmann Machine
Understanding Autoencoders
Architecture of Autoencoders
Brief on types of Autoencoders
Applications of Autoencoders

Hands-on/Demo

Implement RBM
Simple encoder

Skills

RBM
Autoencoders

Module 10 - Generative Adversarial Network(GAN)

Topics

Which Face is Fake?
Understanding GAN
What is Generative Adversarial Network?
How does GAN work?
Step by step Generative Adversarial Network implementation
Types of GAN
Recent Advances: GAN

Hands-on/Demo

Implement Generative Adversarial Network

Skills

Generative Adversarial Network

Module 11 - Emotion and Gender Detection (Self-paced)

Topics

Where do we use Emotion and Gender Detection?
How does it work?
Emotion Detection architecture
Face/Emotion detection using Haar Cascade
Implementation on Colab

Hands-on/Demo

Implement Emotion and Gender Detection

Skills

Emotion and Gender Detection

Module 12 - Introduction to RNN and GRU (Self-paced)

Topics

Issues with Feed Forward Network
Recurrent Neural Network (RNN)
Architecture of RNN
Calculation in RNN
Backpropagation and Loss calculation
Applications of RNN
Vanishing Gradient
Exploding Gradient
What is GRU?
Components of GRU
Update gate
Reset gate
Current memory content
Final memory at current time step

Hands-on/Demo

Implement COVID RNN GRU

Skills

Module 13 - LSTM (Self-paced)

Topics

What is LSTM?
Structure of LSTM
Forget Gate
Input Gate
Output Gate
LSTM architecture
Types of Sequence-Based Model
Sequence Prediction
Sequence Classification
Sequence Generation
Types of LSTM
Vanilla LSTM
Stacked LSTM
CNN LSTM
Bidirectional LSTM
How to increase the efficiency of the model?
Backpropagation through time
Workflow of BPTT

Hands-on/Demo

Intent Detection using LSTM

Skills

LSTM
Sequence Prediction
Sequence Generation

Module 14 - Auto Image Captioning Using CNN LSTM (Self-paced)

Topics

Auto Image Captioning
COCO dataset
Pre-trained model
Inception V3 model
The architecture of Inception V3
Modify the last layer of a pre-trained model
Freeze model
CNN for image processing
LSTM or text processing

Hands-on/Demo

Auto Image Captioning

Skills

Auto Image Captioning
CNN for image processing
LSTM or text processing

Module 15 - Developing a Criminal Identification and Detection Application Using OpenCV (Self-paced)

Topics

Why is OpenCV used?
What is OpenCV
Applications
Demo: Build a Criminal Identification and Detection App

Hands-on/Demo

Build a Criminal Identification and Recognition app on Streamlit.

Skills

OpenCV
Project Implementation with OpenCV

Module 16 - TensorFlow for Deployment (Self-paced)

Topics

Use Case: Amazon’s Virtual Try-Out Room.
Why Deploy models?
Model Deployment: Intuit AI models
Model Deployment: Instagram’s Image Classification Models
What is Model Deployment
Types of Model Deployment Techniques
TensorFlow Serving
Browser-based Models
What is TensorFlow Serving?
What are Servables?
Demo: Deploy the Model in Practice using TensorFlow Serving
Introduction to Browser based Models
Demo: Deploy a Deep Learning Model in your Browser.

Hands-on/Demo

Learn and build a program that Detects Faces using your webcam using OpenCV.
Learn Hyper parameter tuning techniques in Keras on a Fashion Dataset.
Build and deploy a model using TensorFlow Serving.
Build a neural network model for Handwritten digits use activation function, batch size, Optimizer and learning rate for betterment of you model.
Build a Object detection model and detection is done by providing a video the model accurately identifies the objects that are depicted in the video.

Skills

Deploying model with Tensorflow

Module 17 - Text Classification-II (Self-paced)

Topics

Converting text to features and labels
Multinomial Naive Bayes Classifier
Leveraging Confusion Matrix

Hands-on/Demo

Converting text to features and labels
Demonstrate text classification using Multinomial NB Classifier
Leveraging Confusion Matri

Skills

Converting text to features and labels
Text classification
Confusion Matrix

Module 18 - In Class Project (Self-paced)

Topics

Sentiment Classification on Movie Rating Dataset

Hands-on/Dem

Implement all the text processing techniques starting with tokenization
Express your end to end work on Text Mining
Implement Machine Learning along with Text Processing

Skills

Sentiment Analysis

Admission details

The Edureka's Artificial Intelligence Certification course. Admission procedure is very simple.

Candidates may follow the given steps to register themselves-

Step 1: Open the website by clicking on the URL : https://www.edureka.co/advanced-artificial-intelligence-course-python

Step 2: Click on the 'sign up' option given on the rightmost corner and create a new account.

Step 3: enter your email and phone number and proceed further.

Step 4: save this email for future use.

Step 1: click on the 'enroll now' option.

Step 2: enter your email id and mobile number.

Step 3: click on 'begin now'.

Step 4: start your learning.

How it helps

NLP is one form of artificial intelligence which has a great scope in future. This course help machines and humans interact with each other. If one wants to go in this field of artificial intelligence, then edureka's NLP Certification Training with Python course is very essential as it provides exactly what an employer wants and what one should know before stepping into this field.

Candidates who have successfully completed this course are placed in companies like dell, vmware, honeywell, cisco and many others. The certificate will shower ample employment opportunities on candidates. It will yield a higher package. Mean salary of a person with this course is $128,857.

NLP is bridging the gap between humans and machines and this is going to be the future. It opens job opportunities in international spheres. It is a highly developing concept and the desires for the professionals with this certificate are constantly increasing. It is a time friendly course which will be conducted in batches as per your convenience.

This course is a boon for the CV of a professional and will highlight it. The real life projects provided by it help you to analyze the real life problems which will be beneficial in future also.

FAQs

What questions shall be in the assignment given by them?

The assignment given by them shall have real life problems where you can apply your knowledge gained by this certificate course.

What if I miss a session?

There is no problem if a candidate misses a session. One can view recorded sessions available in lms and also one can attend the sessions in another live session.

What can I do if I have a query after completing this course?

They have a 24x7 support system where one can clear doubts anytime.

What if my queries are not solved in this support?

If your problems are not solved in the support system you can contact them at +91 90660 20867/1844 230 6362 (us toll free number) or email at sales@edureka.co.

Which companies offer placement after this course?

Candidates with certificates of this course are placed in companies like Dell, Honeywell, cisco, Vmware.

After how many hours of signing up will I get the access to study material?

After signing up the access to study material is given instantly. Access to pre recorded videos and other material made available by them is also given.

Will the access to this course end after I get my certificate?

The access of this course is available lifetime once you enroll in this course.

What if I am not aware of python? Can I take up this course?

This course can be taken up by anyone. They provide a complementary course to polish your knowledge on python programming.

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Artificial Intelligence Certification Course

Online

5 Weeks

₹ 16639 20799

Quick Facts

Course overview

The highlights

Program offerings

Course and certificate fees

Fees information

certificate availability

certificate providing authority

Who it is for

Eligibility criteria

What you will learn

The syllabus

Module 1 - Introduction to Text Mining and NLP

Topics

Hands-on/Demo

Skills

Module 2 - Extracting, Cleaning and Preprocessing Text

Topics

Hands-on/Demo

Skills

Module 3 - Analyzing Sentence Structure

Topics

Hands-on/Demo

Skills

Module 4 - Text Classification-I

Topics

Hands-on/Demo

Skills

Module 5 - Introduction to Deep Learning

Topics

Hands-on/Demo

Skills

Module 6 - Getting Started with TensorFlow 2.0

Topics

Hands-on/Demo

Skills

Module 7 - Convolution Neural Network

Topics

Hands-on/Demo

Skills

Module 8 - Regional CNN

Topics

Hands-on/Demo

Skils

Module 9 - Boltzmann Machine & Autoencoder

Topics

Hands-on/Demo

Skills

Module 10 - Generative Adversarial Network(GAN)

Topics

Hands-on/Demo

Skills

Module 11 - Emotion and Gender Detection (Self-paced)

Topics

Hands-on/Demo

Skills

Module 12 - Introduction to RNN and GRU (Self-paced)

Topics

Hands-on/Demo

Skills

Module 13 - LSTM (Self-paced)

Topics

Hands-on/Demo

Skills

Module 14 - Auto Image Captioning Using CNN LSTM (Self-paced)

Topics

Hands-on/Demo

Skills

Module 15 - Developing a Criminal Identification and Detection Application Using OpenCV (Self-paced)

Topics

Hands-on/Demo

Skills

Module 16 - TensorFlow for Deployment (Self-paced)

Topics

Hands-on/Demo