TECHNOLOGY
Advanced Data Science and analytics Algorithmica
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Advanced Data Science And Analytics

Learn Deep Learning Algorithms In Data Analyt

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Course at a Glance
  • 45 hours of Classroom Teaching
  • Study Content (Online)
  • 15 Hours Lab Work
  • English Language
  • Online Doubt Support
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About Course

The course aims at developing in-depth understanding of the key technologies in data science and business analytics: data mining, machine learning, visualization techniques, predictive modeling, and statistics.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.

Objectives

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
About Institute

Algorithmica, founded in 2008, is a world class corporate training company that focuses on improving and expanding the engineering skills of developers and on enhancing the quality of the software they develop. Since 2008, Algorithmica has been helping IT professionals get better at what they do by providing an extensive range of training services on emerging technologies. Always pushing the envelope, Algorithmica constantly explores new fields of knowledge as well as new training methodologies to better serve clients. The company is led by a team of experts from IIT Alumni , with accumulated experience of tens of years of software development, architectural design and project management. The team has provided most authentic, comprehensive and high quality training services to good number of companies in recent years, ranging from small start-ups to large enterprises. We pride ourselves by standing by our commitment to help IT professionals get to the next level, by being in tune with our customers actual needs, and by always delivering on what we promise, all while having fun doing it.

Instructors
 ThimmaReddy Maramreddy ThimmaReddy Maramreddy
ThimmaReddy MaramReddy is the founder and the strategist for Algorithm...
Course Structure

1. Review of data analytics life cycle

  • Data analytics life cycle
  • Summary of per-phase techniques

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

3. Python for data analytics

  • Setting up python environment
  • Data types
  • Control statements
  • Python libraries for data analytics

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

5. Pre-deep learning approaches for text based analytics

  • Text preprocessing
  • Handicrafted Feature extraction for text
    • Feature extraction
      • Unigrams
      • Bigrams
      • Trigrams
      • Skipgrams
    • Bag of word feature representation
  • Solving Kaggle problem: Sentiment analysis on movie reviews

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

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

8. Practical deep learning networks

  • Fully connected deep networks(DNN)
  • Convolutional deep networks(CNN)
  • Recurrent deep networks(RNN/LSTM)

9. Deep learning frameworks

  • Theano
  • Caffe
  • Torch
  • TensorFlow

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

11. Applying deep learning approaches for image based analytics

  • Deep networks for image analysis
  • Kaggle Problem:Detecting cats & dogs
  • Kaggle Problem:Face recognition
For Whom

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.

Prerequisites

Nothing but passion & interest towards data engineering

Center Address
Algorithmica Center
Center Details
  • Ac Classroom Yes
  • Power Backup Yes
  • Lift Yes
  • Purified Water Yes
  • Four Wheeler Parking Yes
  • Two Wheeler Parking Yes
  • Hostel Support No
  • Girls Wash Room Yes
  • Female Staff Yes
  • Fire Alarm System Yes
  • Fire Extinguishers Yes
  • Manned Security Building Yes
  • Security Cams Facility Yes
Academic Profile
  • Hours 45
  • Online Query Support Yes
  • Online Tests Yes
  • Telephonic Query Support No
  • Video Classes No
  • Study Content Yes
  • Class Hand Outs Yes
Participants
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