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Edua

Advanced Data Science/Analytics + Project

Next Batch Starts on 03rd Feb 2018

This course is an extension of basic data science course, and focus upon deep learning algorithms in data analysis. It allows you to understand state-of-the art research in analyzing image, text, video and voice data at very practical level.

Category: Data Analytics

  • Details
  • Objectives
  • Target Audience
  • Syllabus

Technical Details

Duration 60 Hours
Prerequisites Passion & Interest towards Data Engineering
Class Room Course Available
Video Course Unvailable

Upon successful completion of Data Science/Analytics course, participants will be able to:

  • Understand the power of neural networks
  • Understand how to automate feature extraction
  • Apply deep learning algorithms to practical problems
  • Analyze text, video and voice data
  • People under any of these following categories

  • Anyone who is passionate about understanding the world and intends to impact world with technology
  • Developers at all levels
  • BI professionals
  • DataWarehousing Professionals
  • Team Leads
  • Analytics Managers
  • Business Managers
  • 1. Review of data analytics life cycle
  • Data analytics life cycle
  • Summary of per-phase techniques
  • 3. Creational Patterns
  • Factory Method
  • Abstract Factory
  • Builder
  • Prototype
  • Singleton
  • Practical Applications
  • Assignment
  • 5. Behavioral Patterns
  • Command
  • Mediator
  • Chain of Responsibility
  • Iterator
  • Memento
  • Observer
  • State
  • Strategy
  • Template Method
  • Interpreter
  • Visitor
  • Practical Applications
  • Assignment
  • 7. Deep neural networks for machine learning
  • Neural network vs Deep neural network
  • Deep learning algorithms
  • Sparse coding
  • Deep belief networks
  • Deep sparse auto encoders
  • Speeding up deep learning: GPU based learning
  • 9. Deep learning frameworks
  • Theano
  • Caffe
  • Torch
  • TensorFlow
  • 11. Applying deep learning approaches for image based analytics
  • Deep networks for image analysis
  • Kaggle Problem:Detecting cats & dogs
  • Kaggle Problem:Face recognition
  • 2. Introduction to deep learning & unstructued data analysis
  • Types of unstructured data
  • Use cases for unstructured data analytics
  • How do you handle unstructured data for classification and regression analytics?
  • Nature of features in image, text, voice, audio & video data
  • What is deep learning?
  • Why do we care about deep learning now?
  • Benefits of deep learning
  • Limitations of deep learning
  • Applications of deep learning
  • 4. Pre-deep learning approaches for image based analytics
  • Image preprocessing
  • Handicrafted Feature extraction for image
  • Feature extraction
  • Color
  • SIFT
  • HOG
  • Edges
  • Keypoints
  • Bag of word feature representation
  • Bag of word with spatial pyramids
  • Part based feature representation
  • Solving Kaggle problem: Detecting cats & dogs
  • 6. Neural networks for machine learning
  • Modelling human brain learning to machines
  • Idea of neuron
  • Activation functions
  • Concept of neural network
  • Perceptron
  • Perceptron model
  • Perceptron learning
  • Multi-layer neural network
  • Multi-layer NN model
  • NN learning
  • Why are neural networks special?
  • Neural networks for supervised learning
  • Neural networks for unsupervised learning
  • 8. Practical deep learning networks
  • Fully connected deep networks(DNN)
  • Convolutional deep networks(CNN)
  • Recurrent deep networks(RNN/LSTM)
  • 10. Applying deep learning approaches for text based analytics
  • Deep networks for text analysis
  • Kaggle Problem:Named entity recognition
  • Kaggle Problem:Sentiment analysis on movie reviews