Python for Data Science and ML

Introduction to Python Mutable and Immutable Objects Control Structure Functions with *arg and **kwarg lambda, filter, map, reduce, set,enumerate, sorted, reversed, range, xrange,zip,sum,max,min etc." Data Structures List Comprehension & Dictionary Comprehension" "Modules Importing Types, Creation and Accessing os, sys, system, random,glob etc." "Object Oriented Programming self, Class Variable and Object Variables Overriding, private data, Inheritance etc"

Files, readlines and writelines, iteration "Exception Handling User Defined Exception using raise" Regular expressions Itertools and Collections framework "Anaconda, Ipython Numpy" Scipy "Pandas Data Series and Data Frame Predictive Modeling Descriptive analysis on the Data Data treatment (Missing value and outlier fixing)" Visualisaion of data, Matplotlib "Introduction to Machine Learning Supervised, Unsupervised and Reinforcement Learning with Examples" "Linear Regression Logistic Regression Decision Trees and Random Forests" "Data Modeling Estimation of performance Evaluation of Models Testing of Models Case Study and Examples" "Introduction to Hadoop Big Data" "Natural Language Processing ( NLP Overview ) NLTK examples "

This course is for the intermediate to Advance level participants. The participants need not have any prior exposure to Python programming language. Prior familiarity with some other programming language (such as Java or C++) would be useful, but it is not mandatory for audience. The course coverage and pace would vary slightly, depending on the composition of the batch. If the training is for participants who are already familiar with some other object-oriented programming language, such as C++ or Java, the initial parts covering the basic language constructs as well as introduction to the OO concepts could be completed faster, and more time could be spent on some of the advanced aspects of the course. If the training is for a batch of participants who are new to any programming language, then even the basic language constructs would require more detailed explanation and practice work, and coverage of some of the later, advanced topics would be curtailed.

Computer with the following soft wares: Operating System: Ubuntu14.04 [ Latest One ] (Most Recommended) / MacOs 10.10.X Windows7 ( Not Recommended) Anaconda 3.3/3.4 ( Latest ) http://continuum.io/downloads#34 http://continuum.io/downloads#all Active Python 2.7/3.3/3.4 ( Anaconda3.3/3.4 will installed almost all packages needed for training). Numpy, Scipy, Pandas, Matplotlib etc. http://docs.continuum.io/anaconda/pkg-docs.html#python-3-4 ( Reference Python Packages ). Acrobat Reader / Libre Office / MSOffice etc. Hardware : RAM: Minimum 4GB / 8GB ( Recommended ). Internet Connectivity. ( Needed to Install Packages and Run Anaconda Server ). 80 GB HDD.

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