Deep learning with python & TensorFlow

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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.


  • 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 other 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

Course Features

  • Lectures 0
  • Quizzes 0
  • Duration 50 hours
  • Skill level All levels
  • Language English
  • Students 0
  • Assessments Yes

You have 10 weeks remaining for the course

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