Deep Neural Network

Data Science Course Details with Deep Neural Network:

 

Python Basics:

  1.       Introduction to Data Science
  2.       Python Intro: Anaconda, Pip for package management, Virtual Env for isolation
  3.       Python Advanced Data Structures I: Numpy
  4.       Python Advanced Data Structures II: Pandas
  5.       Python Database Connectivity
  6.       Python Data Parsing:
  7.        Request to download data
  8.       Parse HTML
  9.        Parse JSON
  10.       Parse XML
  11.        Web Scrapping Basics
  12.       Regular Expressions
  13.       Natural Language Processing Intro
  14.       Python NLP using NLTK
  15.       Advanced NLTK: Sentiment Analysis techniques
  16.       Advanced Web scrapping & NLTK: Auto News Article Summarizer

 

Statistics:

  1.       Statistics Base
  2.       Continuous, Discrete & Categorical Data
  3.       Shape of The Data & Distribution Analysis
  4.       Anova
  5.       Advanced Statistics

Visualization:

  1.       Visualization In Python I: MatplotLib
  2.       Visualization In Python II: Seaborn
  3.       Advanced Visualization in Python

Exploratory Data Analysis:

  1.       Unclean Data
  2.       Issues with Data
  3.       Data from Multiple sources

 Extract Transform & Load (ETL)

  1.       Data Cleaning I: Normalization
  2.       Data Cleaning II: Missing Values, Outliers
  3.       Preparing data for Machine Learning:
  4.       Comparison of Results with Clean & Unclean data for
  5.       MultiLinear Regression

Machine Learning:

  1.       Machine Learning Intro: Supervised, Unsupervised & Semi
  2.       Train, test: Model creation & Prediction Demo

 

Supervised Learning Algorithms:

 1. Supervised Learning Intro:

a. Classification & Regression

2. Regression Analysis

3. Linear Regression

4. SciKit Learn Intro

5. First ML Program: Linear Regression

a. Multi Linear Regression

b. Polynomial Regression

c. Linear Regression Deep dive: Internal Working

d. Cost Function: Gradient Descent

e. Convergence

f. Trouble shooting Non-Convergence

6. Trouble shooting Accuracy of Model performance:

a. Calculating the Error

b. Underfitting: High Bias

c. Overfitting: High Variance

d. Resolving Issues with Accuracy: Regularization

7. Advanced Polynomial Regression

8. Realtime Project on Regression

9. Classification Introduction

a. Calculating Accuracy In Classification

10. Logistic Regression

a. Logistic Regression: Working & Cost Function

b. Logistic Regression for Classification Project

11. Additional ML Algorithms for Regression:

a. Decision Trees

i. Regression

ii. Classification

b. Ensemble Learning: Random Forrest

i. Regression

ii. Classification

c. Support Vector Machines: Basics & Cost Function, Wide Margin Classifier

i. Support Vector Regression

ii. Support Vector Classifier

iii. Kernel SVM: Linear Kernel

iv. Gaussian Kernel

d. Naive Bayes

e. KNN(K Nearest Neighbours)

f. Naive Bayes MNIST

g. Advanced Naive Bayes: Working with Text data and Text based Classification

12. Advanced Machine Learning Concepts

a. KFold Cross Validation

  

Advanced Machine Learning:

  1.       Feature Selection & Feature Engineering
  2.       Model Persistance, Evaluation, Retraining
  3.       Ensemble Learning with Multiple ML Algorithms
  4.       Bagging to Improve Accuracy
  5.       Boosting to Improve Accuracy
  6.       Gradient Boosting
  7.       AdaBoost(Ensemble Learning): Weigthts
  8.       Upper Confidence Bound(UCB) & Thomson Sampling
  9.       ML As a Service(ML Web Service)
  10.             a. Binary Classifier as a Service

Unsupervised Machine Learning:

1. Clustering

a. KMeans Clustering

b. Hierarchical Clustering: Agglomerative & Devisive

c. Dendo Grams, Hierarchical Trees

2. Dimensionality Reduction: Projections

a. Principal Component Analysis(PCA)

b. Kernel PCA

c. Supervised Dimensionality Reduction: LDANoSQL:

3. Association Rule Mining

a. Apriori or Market Basket Analysis

 

NoSQL:

1. Structured & Semistructured Data
2. MongoDB for Document Store DB
3. NoSQL Databases Role in Machine Learning: MongoDB

 

 

Deep Learning:

1. Advanced Machine Learning:

2. Neural Networks Intro

a. Artificial Neural Networks(ANN)

b. Deep Neural Networks

c. Convolutional Neural Networks(CNN)

d. Recurrent Neural Networks(RNN)

e. Stock Price Prediction using Neural Networks: Demo

f. Neural Net Concepts:

g. Neurons as Nodes: Perceptrons

h. Dense & Sparse Neural Networks

3. Neuron Based approach: Benefits

a. Perceptrons

b. Learning Weights

c. Gradient Descent & Back Propagation

d. Activation Function & Feedforward Neural Networks

4. Installing Prerequisite Softwares:

a. Tensorflow

b. Theano

c. Keras

5. 3 Layer Neural Network for Customer Churn Modeling

6. Online Learning(Reinforcement Learning)

7. Generalized Aditive Modeling(GANS)

8. PyTorch

9. Image Processing Introduction

a. OpenCV for Image Processing in Python

b. Edge Detection

c. Eye & Nose Detection

d. Face Detection using Haar cascades

e. Optical Character Recognition using Neural Networks

f. Text Detection: Sliding Window

g. Character Segmentation

h. Character Classification

10. Synthetic Character Generation: Shearing & Scaling, Rotation

11. Revisiting Perceptrons

a. Coding a Text Classifier in Neural Networks

12. Advanced Neural Nets:

13. Long Short Term Memory(LSTM) in RNN

14. Time Series Data(ARMA, ARIMA)

15. Unsupervised Learning using Hidden Markov Model(Tensorflow and Theano)

16. Tensorflow Deep Dive

17. Speech Recognition

18. Advanced Text Mining

19. Building & Deploying a Intelligent Chatbot

a. Data Preprocessing

b. Seq2Seq

c. Deploying the Chat Application

20. Computer Vision as AI

21. Image Recoginition and Classification

22. Deep Neural Networks Architecture revisited

23. Deep Convolutional Neural Network for Image Recognition:

a. Convolutions

b. Pooling, Flattening

c. LeNet, Fully Connectected Feed Forward Network

d. Face Recognition using Convolutional Neural Network

e. Importing Pretrained Models

f. Running Convolutional Neural Networks on GPU for Image

24. Unsupervised Learning in Deep Neural Networks Revisited

a. Current Advancements:

i. Self Organizing Maps(SOM)

ii. Auto Encoders

iii. Boltzman Machines

25. VGG, SDD, ResNet

26. Future Direction: Self Driving Cars, IIoT with AI, Drone based Parcel Delivery, etc

27. Conclusion

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