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ARTIFICIAL INTELLIGENCE DEVELOPER

part time  Login or Register to get full details  Posted on 09 Feb 2020

  Start Date: 10 Feb 2020  End Date: 31 Mar 2020

Job Details
Salary Per Annum
NO STIPEND
Career Level
INTERN
Experience
Fresher
Job Location
HYDERABAD
Qualification
btech
Passout Year
2020 & 2021
Job Description

Training & Internship program to impart Artificial Intelligence Skills in a project based learning environment

ABOUT THE INTERNSHIP

Selected interns undergo intensive training on Artificial Intelligence via offline/online mode at our center or online with mentor support. After the completion of training, the interns shall join a team to build a project assigned by the mentors. During the last week of the internship, the interns shall take up a challenge given by industry partner & submit the solution for review and approval by industry personnel.

PROGRAM CONTENT

Module-1: Introduction to Artificial Intelligence

  1. Introduction to Artificial Intelligence
    • What is Artificial Intelligence
    • History of Artificial Intelligence
    • Use Cases of Artificial Intelligence
  2. Introduction to python programming
    • Python Data Structures
    • Python Programming Fundamentals
    • Conditions and Branching
    • Loops
    • Functions
    • Python Packages

Module-2 : Python for Data Science

  1. Working with NUMPY
  2. Working with Pandas
  3. Introduction to Data Visualization
  4. Introduction to Matplotlib and Seaborn
  5. Basic Plotting with Matplotlib and Seaborn

Module-3 : Data Pre-processing Techniques

  1. Introduction to Data preprocessing 
  2. Importing the Dataset
  3. Handling Missing data
  4. Working with categorical Data
  5. Splitting the data into Train and Test set
  6. Feature Scaling

Module-4 : Introduction to Neural Networks

  1. Introduction to Neural Networks
    • The Neuron
    • The Activation Function
    • How do Neural Networks work?
    • How do Neural Networks learn?
    • Gradient Descent
    • Stochastic Gradient Descent
    • Backpropagation
  2. Introduction to Keras Framework
    • Introduction to the Sequential Mode
    • Activation functions
    • Layers
    • Training
    • Loss function
    • Building ANN Using Tensor flow using sample dataset
    • Evaluating Improving and Tuning ANN

Module-5 : Introduction to Convolutional Neural Networks

  1. What are convolutional neural networks?
  2. Step 1 - Convolution Operation
  3. Step 1(b) - ReLU Layer
  4. Step 2 - Pooling
  5. Step 3 - Flattening
  6. Step 4 - Full Connection
  7. Classification of images using CNN
  8. Evaluating, Improving and Tuning the CNN

Module-6 : Introduction to Recurrent Neural Networks

  1. Introduction to Recurrent Neural Networks
    • The idea behind Recurrent Neural Networks
    • The Vanishing Gradient Problem
    • LSTMs
    • LSTM Variations
  2. Predicting Google stock prices using RNN
    • Evaluating, Improving and Tuning the RNN

Module-7 : Introduction to Natural Language Processing

  1. Introduction to NTLK
  2. Bag of Words model
  3. Natural Language Processing in Python
  4. Sentiment analysis using Natural Language Processing

Module-8 : Introduction to different modes of Deployments

  1. Working with the Flask framework
  2. Building an application with Flask Framework
  3. Integrating Deep learning model with Web Application
  4. Introduction to Python Flask APP
  5. Deploying Python Flask application

Module-9 : Introduction to Cloud Services

  1. Introduction to  Cloud
  2. Introduction to AI in  cloud
  3. Introduction to Watson Studio
  4. Building Machine learning model in Watson Studio
  5. Deploying Deep Learning Models as web services

Module-10 : Introduction to Auto AI

  1. Building a Machine Learning Model Using Auto AI
  2. Introduction to Node red
  3. Integrating Machine Learning model to Node red
  4. Building Web Application

 

Required Skills
PYTHON AI
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