Summer Internship Program - 2020

Summer Internship Program 2020


Hyderabad | Chennai | Bangalore

Month of Joining will be May, June , July

Rs.7000 Rs.6500 + (18% GST) special Discount for early bird offer

Register Now Pay Now

About Program

SmartBridge in collaboration with IBM, is elated to announce our flagship event Summer Internship Program 2020 for students on the latest emerging technologies:

  1. Artificial Intelligence - the pace of progress
  2. Internet of Things - the automated world
  3. Machine Learning - intelligent computer

This platform could be the turning points of your career. We do the best for the improvement of an individual's skills in the domain of their choice.

Registration Process

Three steps to confirm your slot for our Internship program.

Step - 1

Fill up the Google Form

Click the button below, which will redirect to the registration page and submit your details in the Google Form

Register Now

Step - 2

Block your seat with an basic amount

Pay Rs.1000 as the confirmation amount of your slot and then the balance can be paid after joining, click on the button below for the initial payment.

Pay Now

Step - 3

Grab an offer letter

After the registration and payment - You will receive an offer letter to the registered e-mail address within 24 hours.

Program Schedule

Kick off your summer, with an eventful and productive session by undertaking our 4 week program

WEEK 1 & 2

Intense Hands on training program!

Intense Hands on training program! We would teach you every part of the technology chosen by you in the field of you desire.


Project development

Under guidance of highly skilled trainers. "Don't stress" - Our team is always available to assist you. Here you also gain the opportunity to interact with the mentors from the industry level.


Industrail Project Development in VPS

Have to develop a project under the guidence of Industrail Mentor with complete team environment using Virtual Practice School

You will learn

Students can choose any track based on their interest & skill

Internet of Things (IoT) with IBM Cloud

Machine Learning with Python & IBM Watson Studio

  1. Introduction to python programming and Environment Setup
  2. Python Basics
  3. Data types
  4. Expressions and Variables
  5. String Operations
  6. Python Data Structures
  7. Python Programming Fundamentals
  8. Conditions and Branching
  9. Loops
  10. Functions
  11. Packages
  1. Introduction to NumPy
  2. 2D NumPy Array
  3. NumPy: Basic Statistics
  4. Introduction to Matplotlib
  5. Basic Plots with Matplotlib
  6. Histograms
  7. Customization
  8. Introduction to Pandas
  9. Dictionaries & Data frames
  10. Data Manipulations
  1. Import data from txt files
  2. Import data from flat files with NumPy
  3. Import data from other file types
  4. Import data from Databases
  5. Import data from web through API’s
  6. Cleaning Data for Analysis
  1. Fundamentals of Machine Learning
  2. Supervised & Unsupervised learning
  3. Regression & Classification
  4. Machine Learning Terminology
  1. Introduction to Scikit-Learn Package
  2. Regression Analysis
  3. Linear Regression
  4. Logistic Regression
  5. Polynomial Regression
  6. Selection of Right Regression Model
  1. Introduction to Classification Problems
  2. Logistic Regression
  3. Decision Tree
  4. Support Vector Machine
  5. K-Nearest Neighboring
  6. Naive-Bayes
  7. Random Forest
  1. Getting started with IBM Watson Studio
  2. Understand the features
  3. Organize resources in a project
  4. Set up a project
  5. Watson Data Platform projects
  6. Project Collaborators
  7. Add associated services
  8. Prepare data
  9. Add data to a project
  10. Refine data
  11. Ingest streaming data
  12. Working with Jupyter Notebooks
  13. Create notebooks
  14. Code and run notebooks
  15. Share and publish notebooks
  16. Watson Machine Learning
  17. Setting up your machine learning environment
  18. Building models
  19. Deploying the model & integration to Apps
  1. Project Work - 1
  2. Project Work - 2

Artificial Intelligence

  1. Introduction to Artificial Intelligence
  2. Introduction to python programming and Environment Setup
  3. Python Basics
    • Hello World Example
    • Data types
    • Expressions and Variables
    • String Operations
  4. Python Data Structures
    • Lists and Tuples
    • Sets
    • Dictionaries
  5. Python Programming Fundamentals
    • Conditions and Branching
    • Loops
    • Functions
  1. Python - Files I/O
    • File Handling
    • Create a New File
    • Write to an Existing File
    • Delete a File
  2. Python - Exceptions Handling
    • What is Exception?
    • Handling an exception
    • Argument of an Exception
    • Raising an Exceptions
    • User-Defined Exceptions
  3. Python - Object Oriented
    • Overview of OOP Terminology
    • Creating Classes
    • Creating Instance Objects
    • Accessing Attributes
    • Built-In Class Attributes
  1. Working with Data in Python
    • Reading files with open
    • Writing files with open
    • Loading data with Pandas
    • Working with and Saving data with Pandas
  2. Introduction to Visualization Tools
    • Introduction to Data Visualization
    • Introduction to Matplotlib
    • Basic Plotting with Matplotlib
    • Dataset on Immigration to Canada
    • Line Plots
  3. Data Preprocessing
    • Importing the Dataset
    • Handle Missing Data
    • Categorical Data
    • Splitting the Dataset into the Training set and Test set
    • Feature Scaling
  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. Understanding Neural Networks with TensorFlow
    • Activation Functions
    • Illustrate Perceptron
    • Training a Perceptron
    • What is TensorFlow?
    • TensorFlow code-basics
    • Constants, Placeholders, Variables
    • Creating a Model
  3. Building ANN Using Tensorflow using sample dataset
  4. Evaluating, Improving and Tuning the ANN
  1. Introduction to Keras Framework
    • Introduction to the Sequential Mode
    • Activation functions
    • Layers
    • Training
    • Loss functions
  2. Building ANN Using Keras (Tensorflow backend) using sample dataset
  3. Evaluating, Improving and Tuning the ANN
  1. Introduction to Convolutional Neural Networks
    • What are convolutional neural networks?
    • Step 1 - Convolution Operation
    • Step 1(b) - ReLU Layer
    • Step 2 - Pooling
    • Step 3 - Flattening
    • Step 4 - Full Connection
  2. Classification of images using CNN
  3. Evaluating, Improving and Tuning the CNN
  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
  3. Evaluating, Improving and Tuning the RNN
  1. Introduction to Natural Language Processing
  2. Introduction to NTLK
  3. Bag of Words model
  4. Natural Language Processing in Python
  5. Sentiment analysis using Natural Language Processing
    • Cleaning the texts
    • Creating the Bag of Words model
    • Classification of texts
  1. Introduction to IBM Cloud
  2. Introduction to AI in IBM Cloud
  3. Explore IBM Conversation Service
    • Build Chatbot' s using IBM Conversation service
    • Integrate Chatbot to Applications
  4. Explore Visual Recognition service
  5. Explore Watson Studio
    • Build Deep learning models in Watson Studio
    • Deploy models as web service

Desirable Rewards

Do mind the deliverables you will be missing out, if you think this is some run of the mill kinda event. i.e.


Training Certificate

On successful completion of training program, training certificate will be provided in collaboration with IBM


Internship Certificate

Internship certificate will be provided to the students on building an idea into a prototype.


Build your Own Project Idea

Mentors will support you to convert your idea into a working prototype.


Complete Hands-on Training

The curriculum is planned in such a way that every concept is explored through lab session.


Earn IBM skill Badges

IBM provides skill badges upon completion of their video course & basic assessment.


Long-Term Mentorship

You can also get mentorship from our technical team, post internship to build your prototype into a product.

Happy Interns!

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