Data-Science-ML-Udemy

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In This Tutorials You will learn all about Data Sciecne and Machine Learning.

Table of Contents

1. Python
2. Numpy
3. Pandas
4. Matplotlib
5. Seaborn
6. Plotly
7. Machine Learning Algorithms

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1. Python

Please note, this is not meant to be a comprehensive of Python or programming in general.

This just go through the basic topics in order:

• Data types
  • Numbers
  • Strings
  • Printing
  • Lists
  • Dictionaries
  • Booleans
  • Tuples
  • Sets
• Comparison Operators
• if,elif,else Statements
• for Loops
• while Loops
• range()
• list comprehension
• function
• lambda expressions
• map and filter
• methods
  
  
• Python Docs

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2. Numpy

• Numpy is a Linear Algebra Library for python, the reason it is so important for Data Science with Python is that almost all of the libraries in the PyData Ecosystem rely on Numpy as one of their main building blocks

• Numpy is also incredibly fast, as it has bindings to C libraries.

• if you have Anaconda, install Numpy by going to your terminal or command prompt and typing:
  
  conda install numpy
  or
  pip install numpy

• Numpy arrays essentially come in two flavors:

  1. vectors 1-d arrays
  2. matrices 2-d arrays

• Numpy Docs

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3. Pandas

• Pandas is an open source library built on top of Numpy
• It allows for fast analysis and data cleaning and preparation
• It excels in performance and productivity
• It also has built-in visualizaion features
• It can work with data from a wide variety of sources
  

You'll need to install pandas by going to your command line or terminal and using either:

conda install pandas
or
pip install pandas

Throughout this Tutorial:

• Series

• DataFrames

• Missing Data

• GroupBy

• Mergin, Joining and Concatenating

• Operations

• Data Input and Output

• Pandas Docs

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4. Matplotlib

• Matplotlib is the most popular plotting library for Python.
• It gives you control over every aspect of a figure.
• It was designed to have a similar feel to MatLab's graphical plotting. You'll need to install it with pip or conda at your command line or terminal with:

conda install matplotlib
or
pip install matplotlib

• Matplotlib Docs

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5. Seaborn

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
You'll need to install it with pip or conda at your command line or terminal with:

conda install seaborn
or
pip install seaborn

• Seaborn Docs

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6. Plotly

plotly.py is an interactive, open-source, and browser-based graphing library for Python ✨

Built on top of plotly.js, plotly.py is a high-level, declarative charting library. plotly.js ships with over 30 chart types, including scientific charts, 3D graphs, statistical charts, SVG maps, financial charts, and more.

plotly.py is MIT Licensed. Plotly graphs can be viewed in Jupyter notebooks, standalone HTML files, or integrated into Dash applications.

conda install plotly
or
pip install plotly

• Plotly Docs

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7. Machine Learning Algorithms

In this section we'll be implementing all Machine Learning algorithms

Throughout this Tutorial:

• Classification

• Regression

• KNN

• Decision Trees

• Random Forests

• KMeans

• Support Vector Machines

• Principle Component Analysis

• Scikit-learn Docs

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