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 /-

Pay Now

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

Register Now

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

Module – 2: Python Fundamentals

  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

Module - 3: Python for Data Science

  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

Module - 4: Preprocessing Techniques

  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

Module - 5: Machine Learning Foundations

  1. Fundamentals of Machine Learning
  2. Supervised & Unsupervised learning
  3. Regression & Classification

Module - 6: Supervised Learning - Regression

  1. Introduction to Scikit-Learn Package
  2. Regression Analysis
  3. Linear Regression
  4. Logistic Regression
  5. Polynomial Regression
  6. Selection of Right Regression Model

Module - 7: Supervised Learning – Classification

  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

Module - 8: Explore IBM Watson Studio

  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

Module - 9: Project Development

  1. Project Work - 1

Artificial Intelligence

Module - 1: Introduction to Artificial intelligence and Python

  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

Module - 2 : Python Fundamentals

  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

Module - 3: Python for Data Science

  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

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. 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

Module - 5: Working with Keras Framework

  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

Module - 6: Convolutional Neural Networks

  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

Module - 7: 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
  3. Evaluating, Improving and Tuning the RNN

Module - 8: Natural Language Processing

  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

Module - 9: Explore IBM Watson Studio

  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

Module - 10: Project Developement

  1. Project Work - 1

AI for Mechanical Engineers

Module - 1: Introduction to Artificial Intelligence

  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

Module - 2 : Python Programming

  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

Module - 3: Python for Data Science

  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

Module - 4: Artificial 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. 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

Module - 5: Convolutional Neural Networks (CNN’s)

  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

Module - 6: Usecase Development (CNN for Equipment Inspection & Quality Control)

  1. CNN’s for Mechanical Equipment Inspection
  2. CNN’s for Quality Control & Quality Inspection
  3. CNN’s for Industrial Safety

Module - 7: Recurrent Neural Networks (RNN’s) / LSTMs

  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

Module - 8: Usecase Development (RNN’s for Predictive Maintenance)

  1. Mechanical Equipment Failure Prediction
    • High power pumps
    • Blowers
    • Turbo Engines
  2. Vibrational Analysis of Equipment with RNN’s

Module - 9: IBM Watson Studio & Model Deployment

  1. Introduction to IBM Watson Studio
  2. Explore IBM Machine Learning Model Builder
  3. Deploy a model
  4. Build an AI Application

Module - 10: Project Work

  1. Develop a project

AI for Electrical Engineers

Module - 1: Introduction to Artificial Intelligence

  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

Module - 2 : Python Programming

  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

Module - 3: Python for Data Science

  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

Module - 4: Artificial 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. 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

Module - 5: Convolutional Neural Networks (CNN’s)

  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

Module - 6: Usecase Development (Image & Video Analysis)

  1. CNN’s for Electrical Equipment Inspection
  2. CNN’s for Transmission & Distribution Line Inspection

Module - 7: Recurrent Neural Networks (RNN’s) / LSTMs

  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

Module - 8: Usecase Development (Time Series Analysis & Prediction)

  1. Electrical Equipment Failure Prediction
    • High Power Motors
    • Transformers
  2. Power Demand Forecasting with LSTM’s

Module - 9: IBM Watson Studio & Model Deployment

  1. Introduction to IBM Watson Studio
  2. Explore IBM Machine Learning Model Builder
  3. Deploy a model
  4. Build an AI Application

Module - 10: Project Work

  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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