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Best Machine Learning Book for Beginners

You need a mixof different technologies for Data Science projects. Instead of learning many skills, just learn a few. The four main steps of any project are extracting the data, model development, artificial intelligence, and presentation. Attending interviews with many skills is not so easy. So keep the skills short.
A person with many skills can't perform all the work. You had better learn a few skills like Python, MATLAB, Tableau, and RDBMS. So that you can get a job quickly in the data-science project.
Out of Data Science skills, Machine learning is a new concept. Why because you can learn Python, like any other language. Tableau also the same. Here is the area that needs your 60% effort is Machine learning.  Machine Learning best book to start.

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Python: Libraries You Need to Create ML Model

To Create a Model of Machine Learning in Python, you need TWO libraries. One is 'NUMPY' and the other one is 'PANDAS'.

Libraries You Need to Create ML Model

Two Top Libraries You Need

1: NUMPY - It has the capabilities of Calculations.

2: PANDAS- It has the capabilities of Data processing.
To Build a model of Machine learning you need the right kind of data. So, to use data for your project, the Data should be refined. Else, it will not give accurate results. The key steps are Data Analysis and Data Preprocessing.

Command to Install Machine Learning Libraries in Python

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

How to Check NumPy/Pandas installed

After '.' you need to give double underscore on both the sides of the version. 
how to check numpy or pandas version

How Many Types of Data You Need

You need two types of data. One is data to build a model and the other one is data you need to test the model.
  1. Raw data
  2. Evaluate-data

How to Build a Model Flowchart

I have given a flowchart to build a model along with sample data.

References
  1. Sample ML Model Project using Python

Comments

  1. Big data solutions developer should understand the need of Data, and they should work to build more appropriate services to meet the requirements of their clients.


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