Machine learning with Python course in Bangalore

Machine learning with python course is designed by Top industry professional to fulfill the industry gap with knowledge base .


    At the end of the course you will learn :

    • Data Science using Python
    • Statistical Analysis
    • Supervised and Unsupervised learning algorithm and implementation using Python
    • Reinforcement learning algorithm using Python code imlementation
    • Neural network  using Google TensorFlow and Keras Framework

    Why Sniffer Search:

    The  Machine Learning certification using Python  is designed and customized as per industry needs.This is a job oriented course where  all qualified candidate are source for interview along with resume preparation.

    Why Machine Learning with Python:

    Machine Learning using Python  is most popular now because of Python popularity,easy to learn and available Data Science package(Numpy,scipy,pandas,Scikit-learn) and compatibility with google tensorflow and keras framework for Deep learning.

    Python for Data Science has more than 60 percent job for Data Science ,Machine learning and Deep learning  job requirement.

    Minimum Qualification for this course:

    Masters in Science ,Maths and Statistic or B.E/B.Tech  or M.C.A

    Course Curriculum

    Introduction to Machine learning ,Deep learning and Artificial Intelligence
    Introduction to Exploratory Data Analysis- learn how to clean and manipulate data best fit for machine learning algorithm
    The process and step to follow to get insight of problem while solving through machine learning
    Python Basic and Python Data Structure
    Python method,class,loop etc 00:00:00
    Python Data Types 00:00:00
    Python Data Structure 00:00:00
    Python popular package for Data Science -Numpy,Scipy ,Pandas and Scikit -Learn
    Matplotlib and different example of data plotting like scatter plot,bar plot,box plot ,line graph etc
    Supervised and Unsupervised learning algorithm
    Linear regression for single and multiple variable 00:00:00
    Logistic Regression with real time use case 00:00:00
    K Nearest Neighbours 00:00:00
    K means clustering 00:00:00
    Naive Bayes Theorem- An easy approach to learn Naive Bayes theorem like playing golf when whether condition is cloudy,rainy or sunny days
    Dimension Reduction and Principal Component Analysis
    Support Vector Machine and Dimension Reduction on Data Set
    Gradient Boosting to increase performance of machine learning algorithm
    Reinforcement learning with use case and sample code
    Neural Network - how to create artificial Intelligence using Neural network
    How to recognize image using neural network- real time example
    How to work with large dataset - a comparison study of different algorithm and rule of thumb to follow while choosing algorithm to find prediction or finding hidden pattern

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