42-Step Learning-Make you a great Java Big Data scientist!

Author Lighthouse Big Data

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If you are interested in a variety of scientific topics in data classes, you are in the right place. This article will introduce you to 42 steps to become a good data scientist. Deep mastery of data preparation, machine learning, SQL data Science and more.

This article divides these 42 steps into six parts, the first three sections mainly describe the learning process from data preparation to the initial completion of machine learning, including the mastery of theoretical knowledge and the implementation of the Python library.

The forth part mainly explains the method of deep learning from the perspective of how to understand. The last two sections are about SQL data science and NoSQL databases.

Next, let's walk into these 42-step learning.

7 Steps to mastering data preparation (Python)

Data preparation, cleaning, pretreatment, purification, screening. These technologies are used for learning in a range of data activities and different data stages in machine learning, data mining, and data communities. At the same time, this article covers a set of methods that are completely different from our regular data preprocessing.

Based on demand, technology may be used in a given scenario. You will find that this series of methods is suitable for both formal and general approaches.

7 Steps to mastering Python's machine learning (1)

This article focuses on seven steps, including basic Python skills, machine learning basics, a scientific computational Python package overview, Learning machine learning using Python, Python's basic Algorithm for machine learning, Python implementing advanced machine learning algorithms, Python Deep learning.

The main purpose of this article is to help you understand the many methods of machine learning. To be sure, there are a lot of good ways, but which one is best for you? What is the order of the methods used?

7 Steps to mastering Python's machine learning (2)

The previous article is mainly about machine learning basics, this article will focus on the part of machine learning tasks. If you have already studied this series, you should be able to achieve satisfactory learning speed and proficiency, and if not, you might want to review how much time is spent depending on your current level of understanding. By safely skipping some basic module--python basics, machine learning basics, and so on-we can go directly into different machine learning algorithms. This time we can better categorize tutorials based on functionality.

7-Step understanding of deep learning

The purpose of this part of the tutorial is to prepare for the newcomers to the deep neural network and to find and acquire high-quality knowledge from the large and complex subject of machine learning. These seven steps are:

The first step: introduction of deep learning;

The second step: learning technology;

Step three: Reverse propagation and gradient descent;

Fourth step: Practice;

Fifth step: convolutional neural networks and computer vision;

The sixth step: recursion net and language processing;

Seventh step: More in-depth topics.

7 Steps to mastering SQL data Science

Obviously, SQL is a relatively important part of data science. Therefore, this article is intended to help readers grow into skilled practitioners in a short period of time from SQL Novice through free online resources. There are a lot of resources on the Internet, but the path from the beginning to the end, using complementary tools, is not as simple as it seems. I hope this article will give you help in this way.

7 Steps to Understanding NoSQL databases

NoSQL is synonymous with modeless, non-relational data storage solutions. NoSQL is a generic term that covers a number of different technologies. These technologies are not necessarily strongly correlated with NoSQL, and in recent years Structured Query Language (SQL) has converged with relational database management systems.

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42-Step Learning-Make you a great Java Big Data scientist!