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ADVANCED DATA SCIENCE Course Details
 

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Batch Date: Nov 24th @ 8:30AM

Faculty: Mrs. Sasmitha

Duration: 45 Days

Venue :
DURGA SOFTWARE SOLUTIONS at Maitrivanam
Plot No : 202, IInd Floor ,
HUDA Maitrivanam,
Ameerpet, Hyderabad-500038.

Ph.No: +91 - 9246212143, 80 96 96 96 96


Syllabus:

ADVANCED DATA SCIENCE

Module-1: Introduction to Deep learning

  1. What is Artificial intelligence
  2. What is Deep Learning
  3. Difference between Dee Learning and Machine Learning
  4. Software Installation

Module-2: Introduction to PyTorch

  1. PyTorch Installation
  2. Tensorflow Installation
  3. What is Tensor
  4. Vector Operation
  5. Dot Product, Matrix Multiplication
  6. What is Gradient
  7. What is Cost and Loss function
  8. Linear Algebra using PyTorch and Tensors
  9. What is GPU?
  10. GPU coding using PyTorch
  11. Linear Algebra Operation Speed Comparison between CPU, GPU, Numpy
  12. Assignment-1 (Linear Algebra Coding Questions Set of 3)

Module-3: Image Processing Using OpenCV

  1. Installation of OpenCV
  2. What is Computer Vision and What makes it Hard
  3. Images in Computer Vision Understanding Color Spaces (Gray Scale, RGB, HSV etc.)
  4. Converting one Color Space to Another
  5. Scaling, Resizing and Interpolations
  6. Understand how resizing affect quality
  7. Blurring an Image
    • Gaussian Blur
    • Median Blur
  8. Thresholding
  9. Smoothing Images
  10. 2D Convolution(Image Filtering), Image Averaging

Module-4: Time Series Analysis

  1. What is a Time Series Analysis
  2. Date Time Index
  3. Time Resampling
  4. Time Shifting
  5. Rolling and Expanding
  6. Simple Moving Average Method
  7. Visualizing Time Series Data
  8. What is Trend?
  9. What is Seasonality?
  10. ETS Decomposition

Module-5: Artificial Neural Network

  1. What is a biological neuron look like
  2. What is a Perceptron
  3. Activation Function
    • Threshold Function
    • Hyperbolic tangent(tanh)
    • Rectifier
    • Sigmoid
  4. Multilayer Neural Network Architecture
  5. Cost Function of ANN
  6. What is Gradient Descent?
  7. Detail Step by Step Mathematical Derivation
  8. Epochs, Batch Size
  9. What is Stochastic Gradient Descent
  10. Difference between Batch, Stochastic and Mini Batch Gradient Descent
  11. What is Overfitting
  12. Dropouts
  13. What is vanishing Gradient Problem

Module-6: Project-1 (Fuel Price Prediction)

  1. Data Collection
  2. Data Pre Processing
  3. Build first Deep Learning Model using PyTorch
  4. Model Validation

Module-7: Convolution Neural Network

  1. Why Convolution
  2. Convolution Operation
  3. Padding
  4. Convolution Operation with Multiple Filters
  5. Poling Layer
    • Max pooling
    • Avg pooling
  6. Fully Connected Layer

Module-8: Project-2(Covid X-Ray Classification)

  1. Data Collection
  2. Image Pre processing
  3. Data Augmentation
  4. Build CNN Model using Keras
  5. Model Validation

Module-9: RNN, LSTM

  1. Recurrent Neural network Overview
  2. RNN network Architecture
  3. Why LSTM?
  4. LSTM Architecture

Module-10: Project-3 (Forecast the Corona Cases in India using RNN and LSTM for next Quarter 2021-Q1 – Time Series)

  1. Data Collection
  2. Data Pre processing Build Model using Keras
  3. Model Validation

Module-11: Text Mining- Web Scraping

  1. Software Installation
  2. Introduction to Selenium
  3. Introduction to Beautiful soup
  4. Scaping data from Website-1
  5. Scaping data from Website-2

Module-12: Pattern Recognition - Regx

  1. Special Characters in Regular Expression
  2. Search(), find(), findall(), sub(), split()
  3. Meta Characters

Module-13: Data Preprocessing- NLP

  1. Tokenization
  2. Stop words
  3. Introduction to Spacy
  4. n-grams(bi grams, tri grams)
  5. Text data Cleaning using Spacy and NLTK
  6. Bag of Words
  7. Corpus
  8. What is Tf and Idf?
  9. Count Vectorizer
  10. Tf-Idf Vecorizer

Module-14: Project-4(Sentiment Analysis)

  1. Data Collection
  2. Data Pre processing
  3. Build Model using Keras
  4. Model Validation

Module-15: ApacheSpark Installation- 3.0.1

  1. Introduction to Big data
  2. What is Spark
  3. Spark Installation- Local mode
  4. Spark Installation- in Cloud (AWS)
  5. Integrate Jupyter Notebook with Spark

Module-16: Apache Spark Architecture

  1. Introduction to Spark
  2. Spark Advantages
  3. Apache Spark Architecture
    • What is worker node
    • What is Driver Program
    • What is Cluster Manager
    • Master Node
    • DAG
  4. How Spark is Fault Tolerant
  5. What is a RDD
  6. Lazy Evaluation
  7. Actions
  8. Transformations

Module-17: Spark SQL , DataFrames

  1. Create DataFrame
  2. Register DataFrame as table
  3. Selecting data
  4. Filter Operation
  5. Join DataFrames
  6. Drop data, Drop duplicates
  7. groupby
  8. Convert Spark DataFrames to Python DataFrames
  9. Rename column
  10. Fill Missing Values
  11. Find Statistical Summary of any data

Module-18: Spark MLlib

  1. Introduction to Spark MLlib
  2. What is Linear Regression
  3. Understand Linear Regression
  4. Build Machine learning Regression model using Spark MLlib using PySpark