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Machine Learning Training 

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

30+ course

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What you'll learn

  • Have a fundamental understanding of the Python programming language.

  • Use SciKit-Learn for Machine Learning Tasks

  • Use Python for Data Science and Machine Learning

  • Implement Machine Learning Algorithms.

  • Logistic Regression,Linear Regression

  • K-Means Clustering

  • Support Vector Machines

  • Neural Networks

  • Random Forest and Decision Trees

  • Natural Language Processing and Spam Filters

  • Learn to use Pandas for Data Analysis

  • Learn to use NumPy for Numerical Data

  • Explore different IDEs to  write  Python programs or Code

  • Learn to use Matplotlib for Python Plotting

  • Learn to use Seaborn for statistical plots

  • Hands - on and Exercise

  • Oops Concept 

₹ 18, 000

₹ 20,000  

Discount 10 % off

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This course includes

  • 45 Days

  • Classroom Training

  • Full lifetime access

  • Certificate of Completion

  • Weekly, Monthly, Weekend classes

 Complete  Machine Learning Course in Python

Machine Learning for Beginners

  • 20 – 25 Lectures

  • 2 hours per day

  • Weekend option available

  • Price : 20000 Rs.

Topics we cover

  • Environment setup of python

  • Setting up machine learning environment (python)

  • Jupiter notebook , anaconda , other local environments

  • Arithmetic operators in Python: Python

  • Strings in Python: Python Basics

  • Lists, Tuples and Dictionaries: Python Basics

  • Working with Numpy

  • Working with pandas

  • Working with Matplotlib

  • Overview of Sklearn

  • Overview of different ML models

Data collection

  • Importing Data in Python     

  • Data Exploration

  • The Dataset and the Data Dictionary

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Data preprocessing

  • Data conversions

  • Bivarient analysis /variable conversions  -non Usable variables 

  • Outliers

  • -EDA
    -Missing value treatment
    -Dummy value creation
    -handling
    -Correlation Analysis
    -Test train split
    -Bias Variance trade-off

  • Feature engineering
    -feature extraction
    -catagorical data encoding
    -onehotencoding

Model selection procedures

  • Regression

  • Decision tree(intro with implementation)

  • Bayesian(intro with implementation

  • SVM(intro with implementation)

Bonus projects

  • Classification on iris dataset

  • Disease prediction (diabetes dataset)

  • Image classification on hand written datasets

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