This course is designed to provide a complete introduction to Deep Learning with Python. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems.

4 days - $2,100.00

Course taught by an expert Python coder.

Prerequisites:

Knowledge of Python, familiarity with control flow (if/else, for loops) and pythonic constructs (functions, classes, iterables, generators) are required.

Course Outline 

Python Environment Setup and Essentials
Install Anaconda Python Distribution
Work with Python Data Types
Mathematical Computing with Python
Comprehend mathematical functions of Numpy
Scientific Computing with Python
SciPy sub package
Data Manipulation with Pandas -
Understand Pandas SQL operation
Machine Learning with Scikit–Learn
Supervised Learning Model Considerations
Supervised Learning Models
Unsupervised Learning Models, Pipeline
Natural Language Processing with Scikit
Applications, and libraries of NLP
Data Visualization in Python
Web Scraping with BeautifulSoup
Web Scraping and Parsing, Navigating options
Python integration with Hadoop MapReduce and Spark
Why Big Data Solutions are Provided for Python, Hadoop Core Components, Python Integration with Spark using PySpark
Data types, tuples, lists, dicts,
Natural language processing.
In-depth understanding of data science process, data wrangling, data exploration, data visualization,
Hypothesis building
Machine learning using the Scikit-Learn package

Introduction to Python for Deep Learning
Overview of Python
Different Applications where Python is used
Values, Types, Variables
Conditional Statements
Command Line Arguments
Python Scripts on UNIX/Windows
Operands and Expressions
Loops
Writing to the screen

Deep Dive, Functions, OOps, Modules, Errors & Exceptions
Functions
Function Prameters
Variable Scope and Returning Values
Global Variables
Lambda Functions
Object-Oriented Concepts
Standard Libraries
Modules Used in Python
Modules Search Path
Handling Multiple Exceptions
Functions - Syntax, Arguments, Keyword Arguments, Return Values
Sorting - Sequences, Dictionaries, Limitations of Sorting
Packages and Module - Modules, Import Options, sys Path
Error and Exception management in Python
Data Manipulation
Module Search Path
Lambda - Features, Syntax, Options, Compared with the Functions
Errors and Exceptions - Types of Issues, Remediation
Working with functions in Python

Data Manipulation
Basic Functionalities of a data object
Concatenation of data objects
Exploring a Dataset
Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
Aggregation
Merging of Data objects Types of Joins on data objects
Analysing a dataset
GroupBy operations
Concatenation