Machine Learning (ML), a type of artificial intelligence (AI)
is that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, that extracts patterns out of raw data by using methods or an algorithm.

Why Python?
For Data Science and Machine Learning, it is necessary to master at least one coding language.
Python is a perfect choice for beginners to make your focus on to jump into the field of data science and machine learning.

Stages of machine learning
• data collection
• data sorting
• data analysis
• algorithm development
• checking algorithm
To look for patterns, various algorithms are used. They are divided into two groups:
• Unsupervised learning
• Supervised learning

1) Unsupervised
• In unsupervised learning, your machine receives only a set of input data.
• After that, the machine is up to determine the relationship between the other hypothetical data and entered data.
• Unsupervised learning implies that the computer will find patterns on its own and relationships between different data sets. Unsupervised learning can be further divided into association and clustering.

2)Supervised

• Supervised learning implies the computer ability to recognize elements based on the provided examples or samples.
• The computer studies it and develops the ability to recognize new data based on the data provided earlier.
• For example, you can prepare your computer to filter spam emails based on the previously received information.

Supervised learning algorithms include:
• Decision trees
• Support-vector machine
• Naive Bayes classifier
• k-nearest neighbors
• linear regression

Step1: Brush up Math Skills Needed for Python Mathematical Libraries
Data Science and Machine Learning projects need leastwise minor math knowledge basis.
Here are 3 steps to learn the mathematics needed for machine learning and analysis.
1)Linear algebra for data analysis: Scalars, Vectors, Matrices, and Tensors

2) Mathematical Analysis: Derivatives and Gradients

3) Gradient descent: building a simple Neural Network from scratch

Step 2. Learn the Basics of Python Syntax
Below are some great resources to explore:
• Learn Python the Hard Way — a manual-like book that explains both basics and more complex applications.
• Dataquest — this resource teaches syntax and also teaching data science
• The Python Tutorial — official documentation

Step 3. Discover the Main Data Analysis Libraries

Libraries are purely a collection of ready-made objects and functions that you can import into your script to invest less time.
How to use libraries?
Below are some recommendations:
1. Open Jupyter Notebook
2. Go over the library documentation in about half an hour.
3. Import the library into your Jupyter Notebook.
4. Follow the step-by-step guide to see the library in action.
5. Examine the documentation to see what else it is capable of.

Python libraries
• NumPy
• Pandas
• Matplotlib
• Scikit-Learn

Step 4. Develop Structured Projects
The resources that offer topics for structured projects are:

• Dataquest - Interactively teaches data science and Python.
• Python for Data Analysis - A book written by the author of many papers on the analysis of data on Python.
• Scikit — documentation - The main computer training library on Python.
• CS109- Courses from Harvard University for Data Science.

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