$200!! Python Deep Dive, Spark and AI/Machine Learning, Kafka for Data Science and Data Engineering bootcamp training

Erudition is leading provider of training and consulting in Silicon Valley Bay area into emerging technologies with focus on open source technologies and hands on training.

Erudition Inc.

2 Day Python Deep Dive, Spark and AI/Machine Learning for Data Science bootcamp
Erudition Inc. is offering Python for Data Science training on June 1-2 2019
Our mission: Erudition Inc.'s mission is to provide education in emerging technologies to masses at no cost or very affordable rate. What is life's objective at the end of the day? Life is fleeting, and permanence in this world is something we all strive for. The best way to achieve permanence is through sharing knowledge.
Doesn't matter if you are aligned to left brain or right brain you can join Erudition in your Emerging Technologies training!! We will also provide interview help and placement services. 
You will get USD300.00 worth of books for Free!! That are written by Bhairav Mehta and other authors.
Time Date : June 1-2 2019 0AM to 6PM
Location: ITU, 2711 North First Street, San Jose, CA Price: USD 200.00/- Link to Enroll: <A HREF="https://wixsite.us19.list-manage.com/track/click?u=0cb1476a61c7bb12958b80179&id=9a2d27ecd2&e=df3cea22b1" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">http://erudition.eventbrite.com</A>
Instructor: Bhairav Mehta 
Bhairav Mehta is Data Science Manager at Apple Inc. He has 15 years experience in Analytics and Data Science space at various fortune 100 companies. Bhairav Mehta is academician and tenured faculty at various Bay area Universities. Bhairav Mehta has taught 1000s of students in AI, ML and Big Data technologies over last 5 years. He also gives talks at Association of Computing Machinery (ACM), IEEE Computer Science society, Global Big Data and AI conferences, Open Data science conference and other forums. Bhairav Mehta has 5 graduate degrees from top institutes: MS Computer Science (GeorgiaTech), MBA (Cornell University), MS Statistics (Cornell University) etc. 
Linkedin Profile: <A HREF="https://wixsite.us19.list-manage.com/track/click?u=0cb1476a61c7bb12958b80179&id=14ac8fce7c&e=df3cea22b1" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">https://www.linkedin.com/in/mehtabhairav/</A>­
Erudition Website: <A HREF="https://wixsite.us19.list-manage.com/track/click?u=0cb1476a61c7bb12958b80179&id=6a0bdd337e&e=df3cea22b1" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">http://www.eruditionsiliconvalley.com</A>
Bhairav Mehta talk videos and other conference proceedings: <A HREF="https://wixsite.us19.list-manage.com/track/click?u=0cb1476a61c7bb12958b80179&id=26729777a3&e=df3cea22b1" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">https://bit.ly/2MrMbGV</A>­
What is this course about? This course is comprehensive course in Python for Data Science. Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets. This course will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you’ll learn how to use:
python jupyter notebooks pandas numpy matplotlib git and many other tools. You will learn these tools all within the context of solving compelling data science problems.
After completing this course, you’ll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.
By learning these skills, you’ll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Course is 90% hands on and will be taught using Google Colab or Kaggle Kernel Cloud based GPU/TPU based Jupyter type notebook. Students will be confident to build their own full stack end to end Data Science applications by end of the class.
Schedule:
<UL>
<LI>IPython: Beyond Normal Python
<UL>
<LI>Help and Documentation in IPython</LI>
<LI>Keyboard Shortcuts in the IPython Shell</LI>
<LI>IPython Magic Commands</LI>
<LI>Input and Output History</LI>
<LI>IPython and Shell Commands</LI>
<LI>Errors and Debugging</LI>
<LI>Profiling and Timing Code</LI>
<LI>More IPython Resources</LI>
<LI>Version Control and git </LI>
</UL>
</LI>
</UL>
<UL>
<LI>Introduction to NumPy
<UL>
<LI>Understanding Data Types in Python</LI>
<LI>The Basics of NumPy Arrays</LI>
<LI>Computation on NumPy Arrays: Universal Functions</LI>
<LI>Aggregations: Min, Max, and Everything In Between</LI>
<LI>Computation on Arrays: Broadcasting</LI>
<LI>Comparisons, Masks, and Boolean Logic</LI>
<LI>Fancy Indexing</LI>
<LI>Sorting Arrays</LI>
<LI>Structured Data: NumPy's Structured Arrays</LI>
</UL>
</LI>
</UL>
<UL>
<LI>Data Manipulation with Pandas
<UL>
<LI>Introducing Pandas Objects</LI>
<LI>Data Indexing and Selection</LI>
<LI>Operating on Data in Pandas</LI>
<LI>Handling Missing Data</LI>
<LI>Hierarchical Indexing</LI>
<LI>Combining Datasets: Concat and Append</LI>
<LI>Combining Datasets: Merge and Join</LI>
<LI>Aggregation and Grouping</LI>
<LI>Pivot Tables</LI>
<LI>Vectorized String Operations</LI>
<LI>Working with Time Series</LI>
<LI>High-Performance Pandas: eval() and query()</LI>
<LI>Further Resources</LI>
</UL>
</LI>
</UL>
<UL>
<LI>Visualization with Matplotlib
<UL>
<LI>Simple Line Plots</LI>
<LI>Simple Scatter Plots</LI>
<LI>Visualizing Errors</LI>
<LI>Density and Contour Plots</LI>
<LI>Histograms, Binnings, and Density</LI>
<LI>Customizing Plot Legends</LI>
<LI>Customizing Colorbars</LI>
<LI>Multiple Subplots</LI>
<LI>Text and Annotation</LI>
<LI>Customizing Ticks</LI>
<LI>Customizing Matplotlib: Configurations and Stylesheets</LI>
<LI>Three-Dimensional Plotting in Matplotlib</LI>
<LI>Geographic Data with Basemap</LI>
<LI>Visualization with Seaborn</LI>
<LI>Further Resources</LI>
</UL>
</LI>
<LI>Python Deep Dive
<UL>
<LI>Iterators</LI>
<LI>Python Lists and Generators</LI>
<LI>Functions</LI>
<LI>Logic, Control Flow and Filtering</LI>
<LI>Loops</LI>
<LI>Default arguments, variable-length arguments and scope</LI>
<LI>Lambda functions and error-handling</LI>
</UL>
</LI>
<LI>Working with Data and Database
<UL>
<LI>SQL</LI>
<LI>SQLite database</LI>
<LI>Connecting to database</LI>
<LI>Database to dataframes and dictionaries</LI>
</UL>
</LI>
<LI>Python and Spark
<UL>
<LI>Python Spark Intro</LI>
<LI>Deepdive into Spark Framework</LI>
<LI>Python Spark RDDs</LI>
<LI>Python SparkSQL</LI>
<LI>Python Spark Streaming</LI>
<LI>Spark MLlib </LI>
<LI>Python Kafka and Flume</LI>
</UL>
</LI>
</UL>
<UL>
<LI>Working with text and unstructured data
<UL>
<LI>Delimiters</LI>
<LI>Regular Expressions</LI>
<LI>Webscrapping</LI>
</UL>
</LI>
<LI>Machine / Deep Learning and AI based learning
<UL>
<LI>What Is Machine Learning?</LI>
<LI>Introducing Scikit-Learn</LI>
<LI>Hyperparameters and Model Validation</LI>
<LI>Feature Engineering</LI>
<LI>In Depth: Naive Bayes Classification</LI>
<LI>In Depth: Linear Regression</LI>
<LI>In-Depth: Support Vector Machines</LI>
<LI>In-Depth: Decision Trees and Random Forests</LI>
<LI>In Depth: Principal Component Analysis</LI>
<LI>In-Depth: Manifold Learning</LI>
<LI>In Depth: k-Means Clustering</LI>
<LI>In Depth: Gaussian Mixture Models</LI>
<LI>In-Depth: Kernel Density Estimation</LI>
<LI>In-Depth: Deep Learning with Keras</LI>
<LI>Application: A Face Detection Pipeline</LI>
<LI>Further Machine Learning Resources</LI>
</UL>
</LI>
<LI>Python Data Science Projects
<UL>
<LI>Python Stats App</LI>
<LI>Python ML App</LI>
<LI>Python Deep Learning App</LI>
</UL>
</LI>
</UL>
Each topic includes codes and explanation step-by-step. Students will get 4 books on Python Data Science, Data Munging. ETL and Python Application development.
Thanks
Bhairav Mehta
URL:  <A HREF="https://wixsite.us19.list-manage.com/track/click?u=0cb1476a61c7bb12958b80179&id=5206f1c673&e=df3cea22b1" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">http://www.eruditionsiliconvalley.com</A>
Phone# 4086608118
Email: <A HREF="mailto:eruditionbayarea@gmail.com" TARGET="_blank" REL="nofollow noopener noreferrer">eruditionbayarea@gmail.com</A>

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$200!! Python Deep Dive, Spark and AI/Machine Learning, Kafka for Data Science and Data Engineering bootcamp training

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$200!! Python Deep Dive, Spark and AI/Machine Learning, Kafka for Data Science and Data Engineering bootcamp training

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