This post will introduce the reader to the basics of machine learning and more importantly, show you how to do machine learning with python! You’ll walk away from this post with a firm understanding of what machine learning is and be able to answer this statement: “Can a computer decide without being told?”. No, it is not possible without a Human. Learn What are the Libraries and Algorithms used in machine learning with Python? from Machine Learning With Python Online Course

The purpose of machine learning is to make machines more intelligent and able to make decisions based on their observation. We’ll also go through some of the most popular libraries for python, pandas, matplotlib, scipy, and seaborn.

**Libraries in Machine Learning with Python**

Pandas

Matplotlib

Scipy

Seaborn

**Pandas**

Python library for rendering high-performance, easy-to-use data structures, and data interpretation tools for the Python language.

**Benefits of Pandas**

- Data structures that are expressive, quick, and versatile.
- Aggregations, concatenations, iteration, flexible data structures are supported.
- When used in combination with other Python packages, it is quite versatile.
- Intuitive data manipulation with few instructions.
- Supports several commercial and academic sectors.
- Performance-optimized indexing and visualization procedures.

**Matplotlib**

It is a two-dimensional charting library that is interactive and cross-platform. It is capable of producing high-quality graphs, charts, and plots in a variety of hardcopy formats.

**Benefits of** **Matplotlib**

- A MATLAB-like interface is available as an option for easy graphing.
- The object-oriented interface allows full control over axis attributes, font properties, line styles, and so on.
- Compatibility with a wide range of graphics backends and OS systems.
- Matplotlib is used in conjunction with other libraries, such as Pandas.
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**Scipy**

The well-known ML library includes modules for optimization, linear algebra, integration, and analytics.

**Benefits of Scipy**

- Excellent for picture alteration.
- Allows for the simple processing of mathematical operations.
- It provides efficient numerical algorithms such as numerical integration and optimization.
- Signal processing is supported.

**Seaborn**

Seaborn is a module for creating statistical graphs. It is based on matplotlib and integrates with pandas data structures.

**Benefits of Seaborn**

- It produces more visually appealing graphs than matplotlib.
- Has built-in plots that matplotlib does not have.
- Less code is used to show graphs.
- Pandas integration is seamless: data visualization and analysis are combined.

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**The Important algorithm in machine learning**

- Unsupervised
- Supervised
- Reinforcement Learning

**Unsupervised Learning **

An algorithm that does not require training data to function. This can be done with clustering models, label propagation models, and outlier detection/filtering algorithms. Most clustering algorithms are unsupervised learning.

**Supervised Learning **

An algorithm that requires training data to create the initial model, before applying the model to new data.

**Reinforcement Learning**

An algorithm that can be used to learn a model without a set of data from which to train the model. This could be the case with optimization.