Data Science For Non Programmers | Machine Learning For Non Programmers | Edureka

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This Live session on 'Data Science For Non Programmers' will help you explore the basics of Data Science and Machine Learning and in the process also tell you how to use different Python Libraries that let you implement Machine Learning Deep Learning, Data Visualization for Data Science and other purposes Non Programmatically.

?About Speaker: Mr. Krishna Prasad P is
1. Director Consulting at CGI.
2. Mentor for W2RT cohort as part of Nasscom initiative.
3. Recipient of prestigious “CGI Builder Award” at CGI level & “Sirius Award” at India level.
4. Submitted various white papers on Data related topics in DCAL (IIMB & IISC).
5. Architected and implemented comprehensive Data platforms for leading Banking firms.
6. Excellent experience in managing programs globally across delivery locations involving multiple time-zones.
7. Driving Pre-sales as Solution Architect & Solution Lead involving large deals.
8. Overall 22+ Years of experience with a strong focus on leading high performing teams & managing delivery excellence.
9. An Alumnus of the Indian School of Business.

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About the Master's Program

This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles.

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Topics Covered in the curriculum:

Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc.

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