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Machine Learning: Cleaning data

In this post we will be understanding various techniques used to clean dataset. Before cleaning data we need to understand our data. This post also explains about way to understand the data we are dealing with.

Cleaning of data is one of the crucial step in the entire process of creating machine learning model.

Let's get started and master the crucial process of cleaning datasets.

Importing data using Python

In today's world, there is a lot of data being generated from various devices. The format of data varies from flat files to tabular structure. In this post, we will be looking into python packages, for importing data using python. We will be looking at techniques to import following file types using python packages:

  1. Flat files - .txt, .csv files
  2. Pickled file
  3. Excel files
  4. SAS files
  5. STATA file
  6. HDF5 files
  7. mat file
  8. Relational database
  9. Reading data from web
Let's get started with importing data from various file formats.

Python for Data Science - part 3

This is the final part of understanding Python. Here in this post, I will be covering following topics:

  1. Random Generators using numpy
  2. Functions in Depth
  3. Error Handling
  4. Iterators
  5. zip keyword
  6. List comprehension
  7. Dictionary Comprehension
  8. Generators
  9. yield keyword
Let's get started.
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Python packages for Data Science

It's really important to know about packages that are useful when we are solving data science problem. I will be talking about following packages that are available in Python, and are used to solve data related problems:

  1. Numpy
  2. Matplotlib
  3. Pandas
  4. scikit-learn
We will be using pip to install above packages. Let's get started.

Introduction to machine learning

Machine Learning is really making a buzz in the industry. So its right time to get familiar with it. I will be starting up with the basics and in my upcoming posts, I will be going to advance topics of machine learning.

Excited! Let's get started.

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Angular 5

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