Duration: Hours

Machine Learning is simply recognizing patterns in your data to be able to make improvements and intelligent decisions on its own. Python is the most suitable programming language for this because it is easy to understand and you can read it for yourself.

Training Mode: Online

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    Description

    Python

    • Introduction to Python
    • Python Introduction and IDE
    • Basic Commands in Python
    • Objects, Number and Strings
    • Objects, List, Tuples and Dictionaries
    • If_else and For_loop
    • Functions and Packages
    • Important Packages
    • Python built-in libraries
    • Python third party libraries
    • Object oriented programming
    • classes

    Introduction to machine learning

    • Introduction to analytics and machine learning
    • Why machine learning ?
    • Why Python ?
    • Python stack for data science

    Descriptive analytics

    • Working with DataFrames in Python
    • IPL dataset description using dataframe in python
    • Loading Dataset into pandas dataframe
    • Displaying first few records of the dataframe
    • Finding summary of the dataframe
    • Slicing and indexing of dataframe
    • Value counts and cross tabulations
    • Sorting dataframe by column values
    • Creating new columns
    • Grouping and aggregating
    • Joining dataframes
    • Renaming columns
    • Apply operations to multiple columns
    • Filtering Records based on conditions
    • Removing a column or a row from a dataset
    • Handling Missing values
    • Exploration of Data using Visualization
    • Drawing plots
    • Bar chart
    • Histogram
    • Distribution or density Plot
    • Box plot
    • Comparing distribution
    • Scatter plot
    • Pair Plot

    Probability distributions and hypothesis tests

    • Overview
    • Probability Theory – Terminology

    – Random Experiment

    –  Sample space

    – Event

    • Random Variables
    • Binomial Distribution
    • Poisson Distribution
    • Exponential Distribution
    • Normal Distribution
    • Central Limit Theorem
    • Hypothesis Test
    • Analysis of Variance

    Linear regression

    • Simple linear Regression
    • Steps in building a regression model
    • Building a simple linear regression model
    • Model diagnostics
    • Multiple Linear regression

    Classification problems

    • Classification Overview
    • Binary Logistic Regression
    • Credit Classification
    • Gain Chart and lift chart
    • Classification Tree

    Advanced machine learning

    • Overview
    • Gradient Descent Algorithm
    • Advanced regression models
    • Advanced machine learning algorithms

    Clustering

    • Overview
    • How does clustering work ?
    • K means clustering
    • Creating product segments using clustering
    • Hierarchical clustering

    Forecasting

    • Forecasting overview
    • Components of time series data
    • Moving Average
    • Decomposing time series

    Support Vector Machines

    • Overview
    • Kernel tricks
    • Example

    Recommender systems

    • Overview
    • Association Rules
    • Collaborative filtering
    • Using surprise Library

    Text analytics ( NLTP)

    • Overview
    • Sentiment Classification
    • Naïve-Bayer’s model for sentiment Classification
    • Using TF-IDF victorizer

    Neural Networks

    • Neural Networks Introduction
    • Activation functions
    • Optimizers
    • Loss functions

    Deploying application in Azure

    Deploying ML application in AWS

     

    For more inputs on Machine Learning with Python you can connect here.
    Contact the L&D Specialist at Locus IT.

     

     

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