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Python Basics

      Introduction to Data Science

      Python Intro: Anaconda, Pip for package management, Virtual Env for isolation

      Python Advanced Data Structures I: Numpy

      Python Advanced Data Structures II: Pandas

      Python Database Connectivity

      Python Data Parsing:

       Request to download data

      Parse HTML

       Parse JSON

      Parse XML

       Web Scrapping Basics

      Regular Expressions

      Natural Language Processing Intro

      Python NLP using NLTK

      Advanced NLTK: Sentiment Analysis techniques

      Advanced Web scrapping & NLTK: Auto News Article Summarizer

Statistics

      Statistics Base

      Continuous, Discrete & Categorical Data

      Shape of The Data & Distribution Analysis

      Anova

      Advanced Statistics

Visualization

      Visualization In Python I: MatplotLib

      Visualization In Python II: Seaborn

      Advanced Visualization in Python

Exploratory Data Analysis

      Unclean Data

      Issues with Data

      Data from Multiple sources

Extract Transform & Load (ETL)

      Data Cleaning I: Normalization

      Data Cleaning II: Missing Values, Outliers

      Preparing data for Machine Learning:

      Comparison of Results with Clean & Unclean data for

      MultiLinear Regression

Machine Learning

      Machine Learning Intro: Supervised, Unsupervised & Semi

      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

Feature Selection & Feature Engineering

      Model Persistance, Evaluation, Retraining

      Ensemble Learning with Multiple ML Algorithms

      Bagging to Improve Accuracy

      Boosting to Improve Accuracy

      Gradient Boosting

      AdaBoost(Ensemble Learning): Weigthts

      Upper Confidence Bound(UCB) & Thomson Sampling

      ML As a Service(ML Web 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

Structured & Semistructured Data
MongoDB for Document Store DB
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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Duratech Solutions

 Duratech Solutions is incorporated in 2012 and has successfully operated in the global software development industry for 7 Years.

We are the leaders in Coimbatore offering Trainings in Bigdata and Data Science, we are the only training provider in Coimbatore offering Deep Learning, the highest level of Machine Learning & Artificial Intelligence Technology. Our students have got placed in various companies like IBM, Sonata Software, Deloitte, etc

Reach Us

enquiry@duratechsolutions.in 

 +0422-4200383 
 +91-89400 03640 

  320N,Arpee Complex, NSR Road, SaiBaba Colony, Coimbatore-641 011. Tamil Nadu, India 

 256, 2nd Floor Sathy Rd,DPK Complex,Sathy Main Road,Opp. to Perumal Kovil,Saravanampatti, Coimbatore, Tamil Nadu - 641035