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Data Science with Deep Learning, Machine Learning, Artificial Neural Networks Modelling with Advance Analytics tools ( Python, R) -Chennai, India

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Data Science with Deep Learning, Machine Learning, Artificial Neural Networks Modelling with Advance Analytics tools ( Python, R) -Chennai, India


**This is an online event**

Training will start on 5th October and end on 27 October. Every Saturday and Sunday Morning ( EST ) classes for 2 hours with Live Instructor from Banglore, India

*Meeting URL will be sent separately -- Don't panic if you don't see as part of Event Ticket*


Where: Virtual

When: 5th October 2018 - 27 October 2018  ( 11 AM EST )

Instructor: Shailendra Pathak

Content: 

General Admission is ($399.00)

Tired of Cookie cutter AI/Machine learning course?  We feel your pain where Instructor is worse than Wikipedia?

Don't get fooled by all cookie cutter courses where they only talk about ready to use Libraries and tools, our program is a ground up approach where we teach the Math behind each algorithm and then teach the technology/Tool to use it. 

 

Who is the target audience?



No programming experience is required.



Downloading and installing IDE and tools are included in the course



High-school Math 




Intro and set up







Setting up python at your machines



introducing python IDE







Basics about python







Basics about python and its advantages



How python handles data



Lists, arrays and dataframes



types of data and variables - float, int



Dictionaries



Loops and inumeration variables










Data Handling and mining in python with example







Importing csv files



Writing external files



Functions



Intro to libraries like scipy, numpy, pandas etc










Descriptive analysis using Python







Summarising basic data -- Mean, Median , percentiles etc



Creating new variables in a dataframe



group by using SQL in python



aggreagation using python functions



Practical example and exercise










Linear regression using Python - OLS regression







Creating covariance matrix of variables



correlation and multicollinearity



Assumptions of OLS and its interpretation



R-square and goodness of fit



Practical Development of a OLS model and its performance evaluation







Logistic Regression using python - Probability distributions and Parametric modeling







Introduction to the concept of probability distributions



Dummy variables



Difference between OLS and a proababilistic model like logistic



Hosmer-Lemeshov statistics and KS statistics and concordance



Practical Development of a logistic model and its performance evaluation










Intro to Machine learnign and Deep learning concepts - Non-parametric modeling







Difference from parametric modeling



Classes of machine learning models - supervised learning and unsupervised learning



What is the logic behind neural networks



What are classification trees



Random forests and gradient boosting - an introduction










Practical example of Random Forest modeling







Practical development of a RF model using data



Model parameter interpretation



Model performance evaluation - AUC and ROC



Model tuning










Practical example of Artificial Neural Networks modeling







Practical development of a ANN model using data



Model parameter interpretation



Model performance evaluation - AUC and ROC



Model tuning







Practical example of Stochastic Gradient Boosting Models







Practical development of a GBM model using data



Model parameter interpretation



Model performance evaluation - AUC and ROC



Model tuning



REFUND POLICY



100% refund only, if you can't attend it due to medical emergencies with proof.



If the training has not been conducted for any reason, full refund.



if you just change your mind then you will be assigned to next batch.



Contact us at dHJhaW5pbmcgfCBiZWNsb3VkcmVhZHkgISBjb20= for any questions. Group discounts are available. Corporate training is also available. 

See our course catalog at - https://www.becloudready.com/training



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