In collaboration with

Winter Internship Program - 2019

Internet of Things Machine Learning Artificial Intelligence
AI for Mechanical Engineers AI for Electrical Engineers AI Powered Robotics with ROS
Ethical Hacking & Cyber Threat Intelligence

Hyderabad, Bangalore



Training Fee

Rs. 4900 /-

Under Early Bird Offer

Rs.4000 /-

Slots (2-Weeks)

Dec 9th 2019 - Dec 21st 2019

Dec 16th 2019 - Dec 28th 2019

Jan 6th 2020 - Jan 19th 2020

Jan 20th 2020 - Jan 31st 2020

Contact Us

Nikhil: +91 8499004200

Nagarjuna: +91 8919961367

About Program

SmartBridge in collaboration with IBM offers Winter Internship Program on cutting edge Technologies. This program delivers a structured training for 40Hrs. This is a complete hands on training where students get maximum exposure to the technology. We offer a great mentor support through out the training period.

We provide them with hands-on training in the evolving technologies of Artificial Intelligence, Machine Learning and Internet of Things. At the end of the training program they will be given a career constructive certificate which will be in collaboration with IBM. A Smart choice for a Smart life.

Registration Process

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

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

Grab an offer letter

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

Internship Tracks

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
  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. Introduction to IBM Cloud
  2. Introduction to AI in IBM Cloud
  3. Explore Watson Studio
    • Build Machine learning models in Watson Studio
    • Deploy models as web service
  4. Creating a Web application using Flask Framework

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. Preprocessing Techniques
    • 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 Watson Studio
    • Build Deep learning models in Watson Studio
    • Deploy models as web service
  4. Creating a Web application using Flask Framework

AI for Mechanical Engineers

  1. Introduction to Artificial Intelligence
  2. Applications of Artificial Intelligence in
    • Manufacturing
    • Automobiles
    • Industrial Engineering
    • Equipment Condition Monitoring
    • Quality Control & Assurance
    • Industrial Safety
  3. Understanding the core of Artificial Intelligence
  4. AI Algorithms, Tools
  5. Machine Learning
    • Supervised Learning
  6. Regression
  7. Classification
    • Unsupervised Learning
  8. Deep Learning
  9. Introduction to Neural Networks
  10. Rapid Prototyping tools for AI development
  1. Introduction to python programming and Environment Setup
  2. Python Basics
    • Hello World Example
    • Data types
    • Expressions and Variables
    • String Operations
  3. Python Data Structures
    • Lists and Tuples
    • Sets
    • Dictionaries
  4. Python Programming Fundamentals
    • Conditions and Branching
    • Loops
    • Functions
  5. Python - Files I/O
    • File Handling
    • Create a New File
    • Write to an Existing File
    • Delete a File
  6. Python - Exceptions Handling
    • What is Exception?
    • Handling an exception
    • Argument of an Exception
    • Raising an Exceptions
    • User-Defined Exceptions
  7. 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. Preprocessing Techniques
    • 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
  5. Introduction to Keras Framework
    • Introduction to the Sequential Mode
    • Activation functions
    • Layers
    • Training
    • Loss functions
  6. Building ANN Using Keras (Tensorflow backend) using sample dataset
  7. 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. CNN’s for Mechanical Equipment Inspection
  2. CNN’s for Quality Control & Quality Inspection
  3. CNN’s for Industrial Safety
  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. Mechanical Equipment Failure Prediction
    • High power pumps
    • Blowers
    • Turbo Engines
  2. Vibrational Analysis of Equipment with RNN’s
  1. Introduction to IBM Watson Studio
  2. Explore IBM Machine Learning Model Builder
  3. Deploy a model
  4. Build an AI Application
  1. Develop a project

AI for Electrical Engineers

  1. Introduction to Artificial Intelligence
  2. Introduction to Artificial Intelligence
  3. Applications of Artificial Intelligence in
    • Power Transmission
    • Power Distribution
    • Power Generation
    • Equipment Condition Monitoring
    • Renewable Energy Sources
    • Energy Storage
  4. Understanding the core of Artificial Intelligence
  5. AI Algorithms, Tools
  6. Machine Learning
    • Supervised Learning
  7. Regression
  8. Classification
    • Unsupervised Learning
  9. Deep Learning
  10. Introduction to Neural Networks
  11. Rapid Prototyping tools for AI development
  1. Introduction to python programming and Environment Setup
  2. Python Basics
    • Hello World Example
    • Data types
    • Expressions and Variables
    • String Operations
  3. Python Data Structures
    • Lists and Tuples
    • Sets
    • Dictionaries
  4. Python Programming Fundamentals
    • Conditions and Branching
    • Loops
    • Functions
  5. Python - Files I/O
    • File Handling
    • Create a New File
    • Write to an Existing File
    • Delete a File
  6. Python - Exceptions Handling
    • What is Exception?
    • Handling an exception
    • Argument of an Exception
    • Raising an Exceptions
    • User-Defined Exceptions
  7. 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. Preprocessing Techniques
    • 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
  5. Introduction to Keras Framework
    • Introduction to the Sequential Mode
    • Activation functions
    • Layers
    • Training
    • Loss functions
  6. Building ANN Using Keras (Tensorflow backend) using sample dataset
  7. 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. CNN’s for Electrical Equipment Inspection
  2. CNN’s for Transmission & Distribution Line Inspection
  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. Electrical Equipment Failure Prediction
    • High Power Motors
    • Transformers
  2. Power Demand Forecasting with LSTM’s
  1. Introduction to IBM Watson Studio
  2. Explore IBM Machine Learning Model Builder
  3. Deploy a model
  4. Build an AI Application
  1. Develop an Application of AI in Electrical Engineering

Ethical Hacking & Cyber Threat Intelligence

AI Powered Robotics with ROS

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.

Arnaud

Hands-on Training

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

Arnaud

Build your Own Project Idea

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

Arnaud

Long-Term Mentorship

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

Arnaud

Training Certificate

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

Arnaud

Internship Certificate

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

Arnaud

Earn IBM skill Badges

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

Happy Interns Testimonials

Industry Partners

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