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Machine Learning with Python Online Training

What is Python?

Python is simple yet powerful and easy to learn the language. It is an object-oriented, high-level programming language. It is also a free and open source.
Python offers a clean syntax, dynamic and its interpreted nature made it to used in wide variety of applications today.
Python offers extensive libraries to work on some of the niche and most upcoming & trending technologies like IOT, Deep Learning, Machine learning & Artificial intelligence.

What is Machine Learning?

Machine learning is an ability to build and train the algorithms to make machines to work as a human. Machine learning is the most talked about subject in the 21st century’s IT world. Today python is becoming the most preferred platform to learn and apply machine learning and deep learning skills which are necessary to build applications with artificial intelligence.

What is Deep Learning?

Deep learning is a class or a broader version of machine learning. It is a technique for learning tasks of Artificial Neural Networks (ANN) that has many layers. Some representations are loosely based on interpretation of information processing and communication patterns in a biological nervous system, such as neural coding that attempts to define a relationship between various stimuli and associated neuronal responses in the brain.
It is emerging as a game changer for the data science industry!

Concepts and tools covered

We would be using Python command line interface, Jupyter and Spyder IDE’s, Tensorflow and Keras libraries.
Python, Numpy, Scipy, Pandas, Scikit learn, supervised and unsupervised machine learning algorithms, deep learning using Tensor Flow, Keras, Neural network algorithms.


The Course content is handcrafted by Experts and it comprises of Presentation slides, Quizzes & Assignments for each Module, Class recording can be accessed in LMS. We would be using a lot of Industry specific use cases to make the learner job ready.

Target Audience

This course is recommended for Professionals from Programming, data analysis, statistics, data management and engineering background, Professionals who work on BI, Reporting, Data warehousing & ETL.
Anybody with an interest in learning python, making a career in analytics and machine learning can choose this course.

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Talk to us for Machine Learning with Python Classroom Training in Bengaluru

  • Basics of Python 

    Learn to program in python Understand variables, classes, expressions, functions etc.

    • Variables
    • Built-in functions
    • Conditions
    • Iterations
  • Data structures in Python 

    Understand different data structures present in python

    • Strings
    • Lists
    • Tuples
    • Dictionaries
  • Data manipulation and exploratory data analytics 

    Learn to juggle with data using Python and do Exploratory data analytics. Explore different libraries in python

    • Pandas
    • Numpy
    • Scipy
  • Introduction to Machine Learning 

    This module lets you know about the various Machine Learning algorithms. The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types

    • Understand difference between supervised and unsupervised learning
    • Learn about different clustering, classification techniques
  • Linear and Logistic Regression 

    This module touches the base with the ‘Regression Techniques’. Linear and logistic regression is explained from the very basics with the examples and it is implemented in Python using two case studies dedicated to each type of Regression discussed

    • Understand difference between Linear and Logistic regression
    • Implement regression techniques on two different datasets
  • Clustering and classification Techniques 

    Learn about unsupervised learning and it’s different clustering techniques and KNN algorithm

    • Understand and implement Kmeans
    • Understand and implement K nearest neighbors classifiers
  • Decision Trees and Random Forest 

    This module covers the concepts of Decision Trees and Random Forest. The Algorithm for creation of trees and forests is discussed in a step wise approach and explained with examples. At the end of the class, these are the concepts implemented on a real-life data set

    • Learn about decision trees
    • Implement decision tree and random forest on a dataset and then visualize the data
  • Deep Learning 

    Learn about artificial neural networks and deep learning

    • Artificial Neural network
    • Deep neural network
  • Fundamentals of Neural Network 

    To understand Text mining algorithms and sentiment analysis using it

    • Text mining algorithms
  • Proof of concept project 

    Working on Sample data set to demonstrate the techniques learned throughout the workshop

    • Demonstrate ability to apply machine learning and deep learning techniques

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