Python for Data Scientist and Machine Learning

Python for Data Scientist and Machine Learning Practitioners This is a 5 - day course that provides a ramp - up to using Python for data science/machine learning. Starting with the basics, it progresses to the most important Python modules for working with data, from arrays, to statistics, to plotting results. The material is geared towards data scientists and engineers. This is an intense, hands - on, programming class. All concepts are reinforced by informal practice during the lecture followed by lab exercises. Many labs build on earlier labs which helps students retain the earlier material. Python for Programming, Scikit-Learn and Tensorflow is a practical introduction to a working programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world.PrerequisitesStudents must have at least 1 year of hands on data science experience and must be comfortable working with a variety of machine learning algorithms. Students should also be comfortable working with files and folders, and should not be afraid of the command line in Linux, Windows, or MacOS. Course Outline The Python Environment Starting Python If the interpreter is not in your PATHs Using the interpreter Trying a few commands The help() function Running a Python script Python scripts on UNIX Python editors and IDEs Getting Started Using variables Keywors Built-in functions Strings Single-quoted string literals Triple-quoted string literals Raw string literals Unicode literals String operators and expressions Converting among types Writing to the screen String formatting Legacy string formatting Command line parameters Reading from the keyboard Flow Control About flow control What’s with the white space? if andelif Conditional expressions Relational and Boolean operators while loops Alternateways to exit as loop Lists and Tuples About Sequences Lists Tuples Indexing and slicing Iterating through a sequence Functions for all sequences Using enumerate() Operators and keywords for sequences The xrange()function Nested sequences List comprehensions Generator expressions Working with Files Text file I/O Opening a text file The with block Reading a text file Writing a text file Python for Scientists “Binary” (raw, or non-delimited) data Dictionaries and Sets About dictionaries When to use dictionaries Creating dictionaries Getting dictionary values Iterating through a dictionary Reading file data into a dictionary Counting with dictionaries About sets Creating sets Working with sets Functions Defining a function Function parameters Global variables Variable scope Returning values Exception Handling Syntax errors Exceptions Handling exceptions with try Handling multiple exceptions Handling generic exceptions Ignoring exceptions Using else Cleaning up with finally Re-raising exceptions Raising a new exception The standard exception hierarchy OS Services The os module Environment variables Launching external processes Paths, directories, and filenames Walking directory trees Dates and times Sending email Pythonic Idioms The Zen of Python Common Python idioms Packing and unpacking Lambda functions List comprehensions Generators vs. iterators Generator expressions String tricks Modules and Packages What is a module? The import statement Where did the.pyc file come from? Module search path Zipped libraries Creating Modules Packages Module aliases When the batteries aren’t included Objectives Defining classes Instance objects Instance attributes Methods __init__ Properties Class data Inheritance Multiple Inheritance Base classes Special methods Pseudo-private variables Static methods Developer Tools Program development Comments pylint Customizing pylint Unit testing The unittest module Creating a test class Establishing success or failure Startup and Cleanup Running the tests The Python debugger Starting debug mode Stepping through a program Setting breakpoints Debugging command reference Benchmarking XML and JSON About XML Normal approaches to XML Which module to use? Getting started with ElementTree How ElementTree works Creating a new XML Document Parsing an XML Document Navigating the XML Document Using XPath Advanced XPath iPython About iPython Features of iPython Starting iPython Tab completion Magic commands Benchmarking External commands Enhanced help Notebooks numpy Python’s scientific stack numpy overview Creating arrays Creating ranges Working with arrays Shapes Slicing and indexing Indexing with Booleans Stacking Iterating Tricks with arrays Matrices Data types numpy functions scipy About scipy Polynomials Vectorizing functions Subpackages Getting help Weave A Tour of scipy subpackages cluster constants fftpack integrate interpolate io linalg ndimage odr optimize signal sparse spatial special stats pandas About pandas Pandas architecture Series DataFrames Data Alignment Index Objects Basic Indexing Broadcasting Removing entries Time series Reading Data matplotlib About matplotlib matplotlib architecture matplotlib Terminology matplotlib keeps state What else can you do? Python Imaging Library The PIL Supported image file types The Image class Reading and writing Creating thumbnails Coordinate system Cropping an d pasting Rotating, resizing, and flipping Enhancing A Tour of Scikit-Learn subpackages Tensorflow Installation Class and Function Exploration Creating First Graph and Running Session Managing Graphs Lifecycle of a Node Value Linear Regression Convolutional Neural Network Architecture Convolutional Layer CNN Architectures HSG courses are taught by the experienced instructors who are proven experts in their field. Our instructors are highly knowledgeable, friendly, reliable and inspiring. They speak and teach industry's best practices and often customize classes to meet individual needs. Students are encouraged to ask questions and participate in discussions and training-labs.