Data Science Course | Data Science With Python

Data Science Deep learning with Python & TensorFlow

Data Science Course using Python Programming

Data Science Course Deep Learning with Python introduces the field of deep learning using the Python language, Statistical thoughts and the practical execution using python. Explore challenging concepts and practice with applications.

TARGET AUDIENCE:

Data Scientist
Researcher
Data Analyst
Fresher from Maths, statistics and engineering
Statistician
Data Science professionals

Data Science Course Content:

module 1 : Introduction to Python ?

What are Python Language and features
Why Python and why it is different from other languages
Installation of Python
Anaconda Python distribution for Windows, Mac, Linux.
Run a sample python script, working with Python IDE’s.
Running basic python commands – Data types, Variables, Keywords, etc

module 2 : Basic constructs of Python language ?

Indentation(Tabs and Spaces) and Code Comments (Pound # character)
Variables and Names
Built-in Data Types in Python – Numeric: int, float, complex – Containers: list, tuple, set, dict – Text Sequence: Str (String) – Others: Modules, Classes, Instances, Exceptions, Null Object, Ellipsis Object – Constants: False, True, None, NotImplemented, Ellipsis, __debug__
Basic Operators: Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, Identity
Slicing and The Slice Operator [n:m]
Control and Loop Statements: if, for, while, range(), break, continue, else

module 3 : Writing Object-Oriented Program in Python and connecting with Database ?

Classes – classes and objects, access modifiers
Instance and class members OOPS paradigm – Inheritance
Polymorphism and Encapsulation in Python
Functions: Parameters and Return Types
Lambda Expressions, Making a connection with Database for pulling data.

module 4 : File Handling, Exception Handling in Python ?

Open a File, Read from a File, Write into a File
Resetting the current position in a File
The Pickle (Serialize and Deserialize Python Objects)
The Shelve (Overcome the limitation of Pickle)
What is an Exception
Raising an Exception
Catching an Exception;

module 5 : Deep Learning Techniques ?

Introduction to Deep Learning within machine learning
How it differs from all others methods of machine learning
Training the system with training data
Supervised and unsupervised learning
Classification and Regression
Supervised learning
Clustering and association unsupervised learning
The algorithms used in these types of learning.

module 6 : Tensor Flow for Training Deep Learning Model ?

Introduction to TensorFlow open source software library for designing
Building and training Deep Learning models
Python Library behind TensorFlow
Tensor Processing Unit (TPU) programmable AI accelerator by Google.

module 7 : Introduction to Neural Networks ?

Mapping the human mind with Deep Neural Networks
The various building block of Artificial Neural Networks
The architecture of DNN, its building blocks, the concept of reinforcement learning in DNN, the various parameters, layers, activation functions and optimization algorithms in DNN.

module 8 : Using GPUs to train Deep Learning networks ?

Introduction to GPUs and how they differ from CPUs
The importance of GPUs in training Deep Learning Networks
The forward pass and backward pass training technique
The GPU constituent with simpler core and concurrent hardware

Data Science

Data Science Deep learning with Python & TensorFlow

Course Description

Data Science Course using Python Programming

module 1 : Introduction to Python ?

module 2 : Basic constructs of Python language ?

module 3 : Writing Object-Oriented Program in Python and connecting with Database ?

module 4 : File Handling, Exception Handling in Python ?

module 5 : Deep Learning Techniques ?

module 6 : Tensor Flow for Training Deep Learning Model ?

module 7 : Introduction to Neural Networks ?

module 8 : Using GPUs to train Deep Learning networks ?

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