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Introduction to Python & Machine Learning (with Analytics Vidhya Hackathons)

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2 hours
3,250 XP
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Course Description

This course introduces basic concepts of data science, data exploration, preparation in Python and then prepares you to participate in exciting machine learning competitions on Analytics Vidhya.
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  1. 1

    Introduction to Python for Data Analysis

    Free

    This chapter will get you started with Python for Data Analysis. We will cover the reasons to learn Data Science using Python, provide an overview of the Python ecosystem and get you to write your first code in Python!

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    Why learn Python for data analysis?
    50 xp
    Python 2.7 vs. Python 3.5?
    50 xp
    Python installation
    50 xp
    Run a few simple programs in Python
    100 xp
    Printing "Hello World!" in Python!
    100 xp
  2. 2

    Python Libraries and data structures

    Free

    In this chapter, we will introduce some of the most common data structures in Python to you and take you through some of the libraries we commonly use in data analysis.

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    Create a List
    100 xp
    Create a String
    100 xp
    Create a Dictionary
    100 xp
    How to use Python libraries?
    50 xp
    Why are conditional statements required?
    100 xp
    How iterative statements help?
    100 xp
  3. 3

    Exploratory analysis in Python using Pandas

    Free

    We start with the first step of data analysis - the exploratory data analysis.

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    Case study - Who is eligible for loan?
    100 xp
    Understanding the Data
    100 xp
    Understanding distribution of numerical variables
    100 xp
    Understanding distribution of categorical variables
    100 xp
  4. 4

    Data Munging in Python using Pandas

    Free

    Pandas is at the heart of data analysis in Python. This chapter gets you started with Data Munging in Python using Pandas

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    The curious case of missing values
    100 xp
    How many variables have missing values?
    100 xp
    Imputing missing values of LoanAmount
    100 xp
    Impute missing values of SelfEmployed
    100 xp
    Treat / Tranform extreme values of LoanAmount and ApplicantIncome
    100 xp
    iPython / Jupyter notebook for Data Exploration
    50 xp
  5. 5

    Building a Predictive model in Python

    Free

    We build our predictive models and make submissions to the AV DataHack platform in this section.

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    First Step of Model Building
    50 xp
    Label categories of Gender to number
    100 xp
    Selecting the right algorithm
    50 xp
    Have you performed data preprocessing step?
    50 xp
    Logistic Regression Introduction
    100 xp
    Build your first logistic regression model
    100 xp
    Prediction and submission to DataHack
    100 xp
    Decision Tree Introduction
    100 xp
    Train model and do prediction using Decision Tree
    100 xp
    Random Forest Introduction
    100 xp
    Train model and do prediction using Random Forest
    100 xp
    Selecting important variables for model building
    50 xp
  6. 6

    Expert advice to improve model performance

    Free

    This chapter will help to understand the approach of data science experts, "How they do approach a challenge?", "How to select a right algorithm?", "How to combine outputs of multiple algorithms?" and "How to select the right value of model parameter also known as parameter tuning?".

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    How to approach a challenge?
    50 xp
    Feature Engineering
    50 xp
    Feature Selection
    50 xp
    How to select the right value of model parameter?
    50 xp
    Use ensemble methods to combine output of more than one models?
    50 xp
    Cross validtion helps to improve your score on out of sample data set
    50 xp
    iPython / Jupyter notebook for Predictive Modeling
    50 xp
    Thank You & Further studies
    50 xp

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