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Data Science and Machine Learning 2.0 Using Python 3.7: : with Live OpenCV and TensorFlow Projects
Data science is an umbrella term that includes data analytics, data mining, ML, and any specific domain expertise pertaining to the field of work. Machine learning (ML) is an amazing subfield of Artificial Intelligence (AI) that tries to mimic the learning behavior of humans.
The first section of this book explains Machine Learning, Data Science 2.0, Big Data and Data Analysis. The second section of this book explains the Data Science Process and Analysis and Big Data Analytics and Ethnography. The third section of this book explains the Python 3.7 Tutorial with examples. The fourth section of this book explains how to Create Game and Live Voting App using Pygame and Python with Web Crawling. The fifth section of this book explains Create Computer Vision based Robot using OpenCV. The sixth section of this book explains Create Reinforcement Learning based Robot. The last section of this book explains TensorFlow Tutorial.
1.Machine Learning and Data Science 2.0
2.Big Data and Data Analysis
3.The Data Science Process and Analysis
4.Big Data Analytics and Ethnography
5.Python 3.7 Tutorial with Examples
6.User-Defined Functions In Python
7.Testing the Python Projects
8.Create Game using Pygame and Python
9.Create Live Voting App
10.Web Crawling with Python
11.Data Transforming with Data Science 2.0
12.Create Computer Vision based Robot using OpenCV
13.Radio Frequency Identification (RFID)
14.Create Reinforcement Learning based Robot
15.Artificial Intelligence and eLearning
16.TensorFlow Tutorial
17.How to use Built-in Fixtures in Python
Data science is a concept that includes several aspects of handling the data such as acquiring the data from one or more sources, data cleansing, data preparation, and creating new data points based on existing data. It includes performing data analytics. It also encompasses using one or more data mining or ML techniques on the data to infer knowledge to create an algorithm that performs a task on unseen data. This concept also includes deploying the algorithm in a way that it is useful to perform the designated tasks in the future.
This chapter aims to present other definitions for Big Data, as well as technologies, analysis techniques, issues, challenges and trends related to Big Data. It also looks at the role and profile of the Data Scientist, in reference to functionality, academic background, and required skills.
The result is a global overview of what Big Data is, and how this new form is leading the world towards a new way of social construction, consumption, and processes.
Machine learning refers to the methods or algorithms that are used as an alternative to traditional statistical methods. When we apply these methods in the analysis of data, these are termed data mining.
Recommender systems, collaborative filtering, association rules, optimization methods based on heuristics, as well as a myriad of methods for regression, classification, and clustering are all examples of machine learning.
Machine learning, however, is model specification is defined by applying algorithms to the data. With machine learning, a few assumptions are made about the underlying distributions of the data.
Computer vision allows us to perform much more complex tasks than we can with microcontrollers alone. To prepare for working with vision, we installed a basic webcam on the robot. This took special consideration since these webcams are not designed to be mounted. Of course, your solution is likely different from mine, so you were able to exercise some creativity in mounting the camera. After that, we were ready to install OpenCV.
The first section of this book explains Machine Learning, Data Science 2.0, Big Data and Data Analysis. The second section of this book explains the Data Science Process and Analysis and Big Data Analytics and Ethnography. The third section of this book explains the Python 3.7 Tutorial with examples. The fourth section of this book explains how to Create Game and Live Voting App using Pygame and Python with Web Crawling. The fifth section of this book explains Create Computer Vision based Robot using OpenCV. The sixth section of this book explains Create Reinforcement Learning based Robot. The last section of this book explains TensorFlow Tutorial.
1.Machine Learning and Data Science 2.0
2.Big Data and Data Analysis
3.The Data Science Process and Analysis
4.Big Data Analytics and Ethnography
5.Python 3.7 Tutorial with Examples
6.User-Defined Functions In Python
7.Testing the Python Projects
8.Create Game using Pygame and Python
9.Create Live Voting App
10.Web Crawling with Python
11.Data Transforming with Data Science 2.0
12.Create Computer Vision based Robot using OpenCV
13.Radio Frequency Identification (RFID)
14.Create Reinforcement Learning based Robot
15.Artificial Intelligence and eLearning
16.TensorFlow Tutorial
17.How to use Built-in Fixtures in Python
Data science is a concept that includes several aspects of handling the data such as acquiring the data from one or more sources, data cleansing, data preparation, and creating new data points based on existing data. It includes performing data analytics. It also encompasses using one or more data mining or ML techniques on the data to infer knowledge to create an algorithm that performs a task on unseen data. This concept also includes deploying the algorithm in a way that it is useful to perform the designated tasks in the future.
This chapter aims to present other definitions for Big Data, as well as technologies, analysis techniques, issues, challenges and trends related to Big Data. It also looks at the role and profile of the Data Scientist, in reference to functionality, academic background, and required skills.
The result is a global overview of what Big Data is, and how this new form is leading the world towards a new way of social construction, consumption, and processes.
Machine learning refers to the methods or algorithms that are used as an alternative to traditional statistical methods. When we apply these methods in the analysis of data, these are termed data mining.
Recommender systems, collaborative filtering, association rules, optimization methods based on heuristics, as well as a myriad of methods for regression, classification, and clustering are all examples of machine learning.
Machine learning, however, is model specification is defined by applying algorithms to the data. With machine learning, a few assumptions are made about the underlying distributions of the data.
Computer vision allows us to perform much more complex tasks than we can with microcontrollers alone. To prepare for working with vision, we installed a basic webcam on the robot. This took special consideration since these webcams are not designed to be mounted. Of course, your solution is likely different from mine, so you were able to exercise some creativity in mounting the camera. After that, we were ready to install OpenCV.
943 pages, Kindle Edition
Published June 22, 2019
7 people are currently reading
17 people want to read
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