Are you one of those people who are ready to uncover the keys to the future? Perhaps you are seeking a quick way to computer programming course. Would you want to learn about python in a short time?
If your answer is yes, then this bookPython Crash Course: A beginner's guide to master the basics of python and data science. Learn coding with this machine learning tool. Discover the endless possibilities of computers and codes might be suitable for you Allow this book to bring you the Python language without a fuss and explore the realm of artificial intelligence, machine learning, and data science
One of the best things about this book is that it will share with you vital lessons you need to build basic programming structures.
These days, you will find essential tools you should have in place to fix day-to-day problems. Guess what? One of such skills involves learning the proper programming language. Can you visualize the things you could do if you'd knew how to make a simple instruction with the use of your computer?
Imagine the things you could accomplish if you'd build from scratch something that would fix problems in a blink of an eye. That would be amazing, right? No frontiers, no boundaries, no limits. You see, it's a new world of possibilities in front of you
Here's a quick overview of what you will learn in this book:
- What Can You Do With Python Programming?;
- Variables And Simple Data Types;
- Functions In Python;
- Conditional Statements In Python And Control Flow Statements;
- How To Define A Class?;
- Testing Your Code;
- Data Science With Python And Machine Learning;
- Web Applications;
- Tips And Tricks To Get The Most Out Of Python;
- And So Much More
You will surely grasp the fundamentals thanks to easy to understand guide in this book, especially if you have never created a programming code before.
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Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark.
Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You’ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification.
After reading this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models. Additionally you’ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications.
What You Will Learn
Build a spectrum of supervised and unsupervised machine learning algorithmsImplement machine learning algorithms with Spark MLlib librariesDevelop a recommender system with Spark MLlib librariesHandle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model
Chapter Goal: Introduce readers to the PySpark environment, walk them through steps to setup the environment and execute some basic operations
Number of pages: 20
Subtopics:
1. Setting up your environment & data
2. Basic operations
Chapter 2: Basic Statistics and Visualizations
Chapter Goal: Introduce readers to predictive model building framework and help them acclimate with basic data operations
Number of pages: 30
Subtopics:
1. Basic Statistics
2. data manipulations/feature engineering
3. Data visualizations
4. Model building framework
Chapter 3: Variable Selection
Chapter Goal: Illustrate the different variable selection techniques to identify the top variables in a dataset and how they can be implemented using PySpark pipelines
Number of pages: 40
Subtopics:
1. Principal Component Analysis
2. Weight of Evidence & Information Value
3. Chi square selector
4. Singular Value Decomposition
5. Voting based approach
Chapter 4: Introduction to different supervised machine algorithms, implementations & Fine-tuning techniques
Chapter Goal: Explain and demonstrate supervised machine learning techniques and help the readers to understand the challenges, nuances of model fitting with multiple evaluation metrics
Number of pages: 40
Subtopics:
1. Supervised:
- Linear regression
- Logistic regression
- Decision Trees
- Random Forests
- Gradient Boosting
- Neural Nets
- Support Vector Machine
- One Vs Rest Classifier
- Naive Bayes
2. Model hyperparameter tuning:
- L1 & L2 regularization
- Elastic net
Chapter 5: Model Validation and selecting the best model
Chapter Goal: Illustrate the different techniques used to validate models, demonstrate which technique should be used for a particular model selection task and finally pick the best model out of the candidate models
Number of pages: 30
Subtopics:
1. Model Validation Statistics:
- ROC
- Accuracy
- Precision
- Recall
- F1 Score
- Misclassification
- KS
- Decile
- Lift & Gain
- R square
- Adj
Автор: Mishra Raju Kumar, Raman Sundar Rajan Название: Pyspark SQL Recipes: With Hiveql, Dataframe and Graphframes ISBN: 148424334X ISBN-13(EAN): 9781484243343 Издательство: Springer Рейтинг: Цена: 41920.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to optimize your PySpark SQL code.
PySpark SQL Recipes starts with recipes on creating dataframes from different types of data source, data aggregation and summarization, and exploratory data analysis using PySpark SQL. You’ll also discover how to solve problems in graph analysis using graphframes.
On completing this book, you’ll have ready-made code for all your PySpark SQL tasks, including creating dataframes using data from different file formats as well as from SQL or NoSQL databases.
What You Will Learn
Understand PySpark SQL and its advanced featuresUse SQL and HiveQL with PySpark SQLWork with structured streamingOptimize PySpark SQL Master graphframes and graph processing
Who This Book Is For
Data scientists, Python programmers, and SQL programmers.
Автор: So Anthony, Joseph Thomas V., John Robert Thas Название: The Data Science Workshop - Second Edition: Learn how you can build machine learning models and create your own real-world data science projects ISBN: 1800566921 ISBN-13(EAN): 9781800566927 Издательство: Неизвестно Рейтинг: Цена: 64050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The Data Science Workshop equips you with the basic skills you need to start working on a variety of data science projects. You`ll work through the essential building blocks of a data science project gradually through the book, and then put all the pieces together to consolidate your knowledge and apply your learnings in the real world.
Автор: Liu Yuxi (Hayden) Название: Python Machine Learning by Example - Third Edition: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn ISBN: 1800209711 ISBN-13(EAN): 9781800209718 Издательство: Неизвестно Рейтинг: Цена: 47810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Equipped with the latest updates, this third edition of Python Machine Learning By Example provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, and algorithms.
Автор: Sanders Finn Название: Python Machine Learning For Beginners: Handbook For Machine Learning, Deep Learning And Neural Networks Using Python, Scikit-Learn And TensorFlow ISBN: 3903331708 ISBN-13(EAN): 9783903331709 Издательство: Неизвестно Рейтинг: Цена: 24820.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Imagine a world where you can make a computer program learn for itself? What if you were able to create any kind of program that you wanted, even as a beginner programmer, without all of the convoluted codes and other information that makes your head spin?
DO YOU WANT TO LEARN THE BASICS OF PYTHON PROGRAMMING QUICKLY?
Imagine a world where you can make a computer program learn for itself? What if it could recognize who is in a picture or the exact websites that you want to look for when you type it into the program? What if you were able to create any kind of program that you wanted, even as a beginner programmer, without all of the convoluted codes and other information that makes your head spin?
This is actually all possible. The programs that were mentioned before are all a part of machine learning. This is a breakthrough in the world of information technology, which allows the computer to learn how to behave, rather than asking the programmer to think of every single instance that may show up with their user ahead of time. it is taking over the world, and you may be using it now, without even realizing it.
Some of the topics that we will discuss include:
The Fundamentals of Machine Learning, Deep learning, And Neural Networks
How To Set Up Your Environment And Make Sure That Python, TensorFlow And Scikit-Learn Work Well For You
How To Master Neural Network Implementation Using Different Libraries
How Random Forest Algorithms Are Able To Help Out With Machine Learning
How To Uncover Hidden Patterns And Structures With Clustering
How Recurrent Neural Networks Work And When To Use
The Importance Of Linear Classifiers And Why They Need To Be Used In Machine Learning
And Much More
This guidebook is going to provide you with the information you need to get started with Python Machine Learning. If you have an idea for a great program, but you don't have the technical knowledge to make it happen, then this guidebook will help you get started. Machine learning has the capabilities, and Python has the ease, to help you, even as a beginner, create any product that you would like.
If you have a program in mind, or you just want to be able to get some programming knowledge and learn more about the power that comes behind it, then this is the guidebook for you.
Автор: Ketkar Nikhil, Moolayil Jojo Название: Deep Learning with Python: Learn Best Practices of Deep Learning Models with Pytorch ISBN: 1484253639 ISBN-13(EAN): 9781484253632 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. What You'll Learn
Review machine learning fundamentals such as overfitting, underfitting, and regularization.
Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.
Apply in-depth linear algebra with PyTorch
Explore PyTorch fundamentals and its building blocks
Work with tuning and optimizing models
Who This Book Is For Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
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Do you want to know everything about Data science?
This guidebook is going to provide you with all of the information that you need to learn more about data science, what this process is all about, and how you can use the Python language to put it all to work for you Even if you have no idea how to program or any idea of what to do with all of that data you have been collecting, this guidebook will give you all of the tools you need to be successful
There are a lot of different parts that come with data science and being able to put them all together can really help us to do better with helping our customers, finding new products to bring to market, and more. And with the help of this guidebook, we can hopefully find the best ways to beat out the competition and see the results that will work for us. It takes some time, and a good data analysis with the right algorithms from Python, but it can be one of the best ways to make some smart and sound decisions for your business.
Working with data science is becoming even more prevalent as the years go on, and businesses all over the world, and in many different industries, are using this to help them see more success. Whether you want to make predictions, provide better customer service, or learn other valuable insights about your business, data science with the help of Python, can make this happen. When you are ready to see what Python data science can do for your business, make sure to check out this guidebook to get started.
The process of Python data science is not an easy one and learning how to make this work for your needs, and to put all of the parts together can make a big difference in the way that you run your business, and how much success you will see when it comes to your business growing in the future. When you are ready to learn more about working with Python data science and how to make this work for your business, make sure to check out this guidebook to get started.
There are so many parts that come with a data science project, and we are going to take some time to discuss them all in this guidebook. We are going to look at some of the basics that come with this data science project, and why it is so beneficial to so many companies to at least check it out and see what it has to offer them. At the same time, we are also going to explore how to set up your own environment to get started with data science, and some of the best libraries that are out there to help us succeed with the use of data science and Python put together.
This book covers:
What Is Data Science?
How Can I Use Data Science?
The Best Python Libraries for Data Science
Setting Up Your Virtual Environments for Data Science
The Importance of the NumPy Arrays
Gathering and Collecting Your Data
Loading and Preparing Your Dataset
Data Mining
Completing the Data Analysis
How Machine Learning Can Help
How to Work with Data Visualization
Many businesses are able to benefit when they work with data analysis for some of their own needs. It will help them to learn more about their customers, their industry, and so much more. When you are ready to learn more about what data science can do for you and to figure out whether this is a process your business should spend some time on, make sure to check out this guidebook to help you get started.
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