SmartBridge in collaboration with IBM Organizes

Summer Internship Program - 2019
Hyderabad, Chennai, Vizag, Kurnool

Note: "Early bird registrations are available"

For the first 200 confirmed applicants will get an offer price of Rs.6000 + GST

Slot - 1 : May, 2019

Slot - 2 : June, 2019

Slot - 3 : July, 2019

Register Now

Nagarjuna: +91 9000195116

Jai Prakash: +91 9676938853

About Program

SmartBridge in collaboration with IBM offering a summer training cum internship programs for the students on latest emerging technologies like IoT, AI, Embedded Systems Development and Machine Learning. This program delivers a structured training on technology for 80 hrs. and the students shall develop their project ideas & submit a report in next 40 hrs. It’s a complete hands-on training.

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

Embedded Systems Development For IOT Applications

  1. Introduction to Embedded Systems, Micro-controllers
  2. ARM Micro-controllers
  3. Architecture of ARM Micro-controllers
  4. Introduction to LPC1768, a 32-bit ARM Cortex-M3
  1. Introduction to mBED platform
  2. Setting up environment
  3. Programming
    • Digital Input and Output
    • Analog Output
    • Analog Input
    • Pulse Width Modulation
  4. Working with Analog & Digital Sensors
  5. Introduction to Displays & Actuators
  6. Programming
    • Servo Motor
    • DC Motor
    • Stepper Motor
  1. Introduction to serial communication
  2. Programming
    • UART
    • SPI
    • I2C
  3. Introduction to Network Connection Ethernet Interface
  4. Program TCP/IP Communication
  5. TCP Socket programming
  1. Advanced Programming Interrupts,
  2. Timers;
  3. Task Management API's Security
  4. API's
  5. Memory and Data Management;
  1. Introduction to ESP32 Platform
  2. Hardware Architecture of ESP32
  3. Programming ESP32
  4. Espressif IOT development framework Programming Environments Compiling & Flashing the Program
  1. Working with GPIO's
  2. Programming UART, SPI and I2C Program ADC & DAC
  3. Sleep Modes
  4. Integrating sensors & programming
  1. Introduction to Bluetooth Low Energy
  2. Bluetooth GAP
  3. Bluetooth GATT
  4. GATT Client programming GATT Server programming Service Discovery Protocol
  5. Bluetooth RFCOMM
  6. Bluetooth Beacon & Physical Web
  1. Initializing the Wi-Fi Environment
  2. Configuring the operational modes
    • Station
    • Access Point

What do you get

What do you get after internship Program

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Training Certificate

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

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Internship Certificate

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

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Build your Own Project Idea

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

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Complete Hands-on Training

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

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Earn IBM skill Badges

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

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Long-Term Mentorship

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

Our Interns words

Testimonials from our Interns from SmartBridge

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