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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.

Python for Data Science - part 2

This post is continuation of Python for Data Science - part 1. Following are the list of topics covered in this post:

  1. Creating functions in Python
  2. Understand build-in functions
  3. Keywords Argument
  4. Accepting input
  5. Nested Functions
  6. Files in Python
  7. Lambda Functions
  8. Object-oriented programming in Python
  9. Modules in Python
  10. Introduction to Python libraries in Machine learning
  11. pip - Package Manager used in Python
Let's get started and one more step ahead on our track of Machine Learning.
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Python for Data Science - part 1

This post is about introduction to Python. Here in this post we will be learning concepts that are useful to us before getting started with Machine Learning. Following are the list of topics covered in this post:

  1. Downloading and Installing Python on system
  2. Executing Python Scripts
  3. Variables and types (Integer, Float, String, Boolean, None, Lists, Dictionaries)
  4. If/else blocks
  5. Truthy Values
  6. Exception Handling
  7. Other data Types (Complex, bytes and bytearray, tuple, set and frozenset)
Let's get started on our existing journey to Machine Learning.
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