Computer Vision Nanodegree

Module 1: Introduction to Computer Vision
  • Image Representation & Classification
  • Convolutional Filters and Edge Detection
  • Types of Features & Image Segmentation
  • Feature Vectors
  • CNN Layers and Feature Visualization
Module 2: Advanced Computer Vision and Deep Learning
  • Advanced CNN Architectures
  • YOLO
  • RNN's and Long Short-Term Memory Networks (LSTMs)
  • Attention Mechanisms and Image Captioning
Module 3: Object Tracking and Localization
  • Robot Localization
  • Introduction to Kalman Filters and Representing State and Motion
  • Matrices and Transformation of State and Simultaneous Localization and Mapping
  • Vehicle Motion and Calculus

Deep Learning Nanodegree

Module 1: Neural Networks
  • Introduction to Neural Networks and Implementing Gradient Descent
  • Training Neural Networks and Sentiment Analysis
Module 2: Convolutional Neural Networks
  • Transfer Learning and Autoencoders
  • Style Transfer
Module 3: Recurrent Neural Networks
  • Long Short-Term Memory Networks (LSTMs)
  • Embeddings & Word2Vec
  • Attention
Module 4: Generative Adversarial Networks
  • Deep Convolutional GANs
  • Pix2Pix & CycleGAN
Module 5: Deploying a Model
  • Building and Deploying a Model using AWS SageMaker
  • Automatic Hyperparameter Tuning

IBM Professional Badges

  • Deep Learning
    • Deep Learning Fundamentals
      Modules: Introduction to Deep Learning | Deep Learning Models | Additional Deep Learning Models | Deep Learning Platforms and Software Libraries
    • Deep Learning with TensorFlow
      Modules: Introduction to TensorFlow | Convolutional Neural Networks (CNN) | Recurrent Neural Networks (RNN) | Unsupervised Learning | Autoencoders
    • Accelerating Deep Learning with GPU
      Modules: Quick review on Deep Learning | Hardware Accelerated Deep Learning | Deep Learning in the Cloud | Distributed Deep Learning
  • Machine Learning with Python
    Modules: Introduction to Machine Learning | Regression | Classification | Unsupervised Learning | Recommender Systems
  • Python for Data Science
    Modules: Python Basics | Python Data Structures | Python Programming Fundamentals | Working with Data in Python

IBM Profeessional Badges

  • Applied Data Science with Python
    • Data Analysis with Python
      Modules: Importing Datasets | Cleaning and Preparing the Data | Summarizing the Data Frame | Model Development | Model Evaluation
    • Data Visualization with Python
      Modules:Introduction to Visualization Tools | Basic Visualization Tools | Specialized Visualization Tools | Advanced Visualization Tools | Creating Maps and Visualizing Geospatial Data
  • Data Science Foundations
    • Introduction to Data Science
      Modules: Defining Data Science | What do data science people do? | Data Science in Business | Use Cases for Data Science | Data Science People
    • Data Science Tools
      Modules: Cognitive Class Labs | Jupyter Notebooks | Zeppelin Notebooks | RStudio IDE | Seahorse | OpenRefine
    • Data Science Methodology
      Modules: Problem to Approach | Requirements to Collection | Understanding to Preparation | Modeling to Evaluation | Deployment to Feedback


  • IBM Cloud Application Developer 2018 - Mastery Award
  • Google Cloud Platform Fundamentals
  • Google Applied CS with Android
  • Google Code-In 2015
  • Google Power Searching
  • Scientific Computing using Python by FOSSEE, IIT Bombay
  • LaTeX Training Certificate by Spoken Tutorial, IIT Bombay


  • SAS Business Analytics and Data Mining Championship 2018
  • Microsoft India AppFest
  • Intel Tech Challenge
  • TCS IT Wiz
  • Google Science Fair 2014