Python Bookcamp

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Python Bookcamp

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Author : Vaskaran Sarcar
language : en
Publisher:
Release Date : 2021-01-16

Python Bookcamp written by Vaskaran Sarcar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-16 with categories.

Python Bookcamp: Exercises and Projects is a beginner''s book. It is a quick programming guide to the Python programming language. The best way of learning is by doing exercises and projects. Therefore, this book follows the boot camp approach. It enables you to make interesting programs in no time.The world is changing, and we keep extra features developing, but the core concepts are evergreen. We build all additional features on top of those. If you have a sound foundation, you can adopt the upcoming features quickly. You also understand the reason behibrnd those changes. So, the book focuses on core topics in-depth, but it does not cover "A-Z" in Python at the same time. The book has 12 chapters. The first chapter is a simple warm-up session for you. Here you''ll set up your programming environment. The second chapter talks about the Python basics. Here you learn about variables, operators, and comments. Each subsequent chapter contains exercises and hands-on projects for you. As you move on, these projects will be more complex. You implement the case studies using the concepts you learn in a previous chapter. At the beginning of these chapters, you get a description of the projects. Once you finish reading these chapters, you get the complete solutions.The book covers both the common and the advanced data types along with the topic of loop and decision making. It also covers file handling, functions, and modules with exception handling mechanisms too. The last chapters of this book cover the object-oriented programming basics. Here you see the usage of classes, objects, and inheritance. You''ll also learn about static and class methods in Python. In the end, there is a chapter to show you how to write useful tests to verify your code. In most cases, you''ll see the complete programs with output. It means you can continue reading the material without interruption. To write the very short programs, or to test the simple commands, I use a Python command shell. For the remaining cases, you see the usage of PyCharm Community Edition in a Windows10 environment. This is a very popular IDE, and this version is free at the time of this writing. Many of us are afraid of fat books. They do not promise that you can complete the book in one day or 7 days, etc. Here is the twist. You should not forget that learning is a continuous process. We can achieve no real mastery in a short period. So, the motto of the book is "To learn the core topics in Python, whatever efforts I need to put, I am OK with that". I believe that if you have a strong focus, you can complete one chapter in a day with no trouble. So, the simple arithmetic says that you can complete the book in 12 days. But it is secondary! I have designed the book in such a way that upon completion of the book, you will learn the core concepts in depth. And you''ll know how to learn further. In short, you can pick the book if the answer is "yes" to the following questions: *Have you never programmed before, but eager to learn Python? *Do you want to explore the Python essentials step-by-step, but as quickly as possible? *Do you have experience with a high-level programming languages, but want to learn Python ? *Do you know how to install software on a machine and then set up the coding environment? *Do you like to review your knowledge before you use Python in advanced fields such as data science, machine learning? Probably you shouldn''t read this book if the answer is yes to any of the following questions: *Are you confident about the fundamentals of Python? *Are you looking for advanced concepts in Python only? *Do you dislike a book that has an emphasis on exercises? *"I dislike Windows OS, and PyCharm. I want to learn and use Python without them only."-is this statement true for you? The source code and other details are available at https://github.com/Vaskaran/PythonBookcamp

Python Bootcamp

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Author : David Maxwell
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-02-09

Python Bootcamp written by David Maxwell and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-09 with categories.

Want To Master The Basics Of Python Programming In A Short Period? If so, you're in the right place! This book is exactly what you need. If you've wanted to learn how to program using Python you have probably thought it was a difficult and long process. This is actually not the case at all. Python can be an extremely easy and straightforward process. The days of searching countless websites to find what you're looking for are over. With this book you will have everything you could possibly need, all in one place! What This Book Will Give You: Python Basics For Beginners This book will take the process of programming and break it down into straightforward simple steps that anyone can follow along to. The Different Types Of Data This book will present all of the important data you need to know and will walk you through how to use it. The Common Errors This book will show you the most common errors you will experience and how to fix them and avoid them all together. What You Will Learn: The basics of Python Normal vs Interactive mode How to create programs What are variables and strings How to use variables and strings The fundamental concepts Python sequences What are lists and tuples The different types of data Mutable and immutable objects The most common errors and how to handle them And much more! All of this information will be presented to you in easy to understand, straightforward steps. For anyone starting out, this is your best option to learn Python in a quick period of time. Try it out for yourself. You won't be disappointed. Now it's time for you to start your journey into Python programming! Click on the Buy Now button above and get started today! I look forward to hearing about your success! tags: Python, Python course, Python book, learning Python, Python language, Python examples, Python tutorials, Python programming language, Python coding, Python programming for beginners, Python for Dummies

Data Science Bookcamp

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Author : Leonard Apeltsin
language : en
Publisher: Simon and Schuster
Release Date : 2021-12-07

Data Science Bookcamp written by Leonard Apeltsin and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Computers categories.

Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution

Machine Learning Bookcamp

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Author : Alexey Grigorev
language : en
Publisher: Simon and Schuster
Release Date : 2021-11-23

Machine Learning Bookcamp written by Alexey Grigorev and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-23 with Computers categories.

Time to flex your machine learning muscles! Take on the carefully designed challenges of the Machine Learning Bookcamp and master essential ML techniques through practical application. Summary In Machine Learning Bookcamp you will: Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images Deploy ML models to a production-ready environment The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image analysis, each new project builds on what you’ve learned in previous chapters. You’ll build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Master key machine learning concepts as you build actual projects! Machine learning is what you need for analyzing customer behavior, predicting price trends, evaluating risk, and much more. To master ML, you need great examples, clear explanations, and lots of practice. This book delivers all three! About the book Machine Learning Bookcamp presents realistic, practical machine learning scenarios, along with crystal-clear coverage of key concepts. In it, you’ll complete engaging projects, such as creating a car price predictor using linear regression and deploying a churn prediction service. You’ll go beyond the algorithms and explore important techniques like deploying ML applications on serverless systems and serving models with Kubernetes and Kubeflow. Dig in, get your hands dirty, and have fun building your ML skills! What's inside Collect and clean data for training models Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow Deploy ML models to a production-ready environment About the reader Python programming skills assumed. No previous machine learning knowledge is required. About the author Alexey Grigorev is a principal data scientist at OLX Group. He runs DataTalks.Club, a community of people who love data. Table of Contents 1 Introduction to machine learning 2 Machine learning for regression 3 Machine learning for classification 4 Evaluation metrics for classification 5 Deploying machine learning models 6 Decision trees and ensemble learning 7 Neural networks and deep learning 8 Serverless deep learning 9 Serving models with Kubernetes and Kubeflow

Python Projects For Beginners

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Author : Connor P. Milliken
language : en
Publisher: Apress
Release Date : 2019-11-15

Python Projects For Beginners written by Connor P. Milliken and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-15 with Computers categories.

Immerse yourself in learning Python and introductory data analytics with this book’s project-based approach. Through the structure of a ten-week coding bootcamp course, you’ll learn key concepts and gain hands-on experience through weekly projects. Each chapter in this book is presented as a full week of topics, with Monday through Thursday covering specific concepts, leading up to Friday, when you are challenged to create a project using the skills learned throughout the week. Topics include Python basics and essential intermediate concepts such as list comprehension, generators and iterators, understanding algorithmic complexity, and data analysis with pandas. From beginning to end, this book builds up your abilities through exercises and challenges, culminating in your solid understanding of Python. Challenge yourself with the intensity of a coding bootcamp experience or learn at your own pace. With this hands-on learning approach, you will gain the skills you need to jumpstart a new career in programming or further your current one as a software developer. What You Will Learn Understand beginning and more advanced concepts of the Python languageBe introduced to data analysis using pandas, the Python Data Analysis libraryWalk through the process of interviewing and answering technical questionsCreate real-world applications with the Python languageLearn how to use Anaconda, Jupyter Notebooks, and the Python Shell Who This Book Is For Those trying to jumpstart a new career into programming, and those already in the software development industry and would like to learn Python programming.

Data Science Bookcamp

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Author : Leonard Apeltsin
language : en
Publisher: Simon and Schuster
Release Date : 2021-11-30

Learn data science with Python by building five real-world projects! In Data Science Bookcamp you''ll test and build your knowledge of Python and learn to handle the kind of open-ended problems that professional data scientists work on daily. Downloadable data sets and thoroughly-explained solutions help you lock in what you''ve learned, building your confidence and making you ready for an exciting new data science career. about the technology In real-world practice, data scientists create innovative solutions to novel open ended problems. Easy to learn and use, the Python language has become the de facto language for data science amongst researchers, developers, and business users. But knowing a few basic algorithms is not enough to tackle a vague and thorny problem. It takes relentless practice at cracking difficult data tasks to achieve mastery in the field. That''s just what this book delivers. about the book Data Science Bookcamp is a comprehensive set of challenging projects carefully designed to grow your data science skills from novice to master. Veteran data scientist Leonard Apeltsin sets five increasingly difficult exercises that test your abilities against the kind of problems you''d encounter in the real world. As you solve each challenge, you''ll acquire and expand the data science and Python skills you''ll use as a professional data scientist. Ranging from text processing to machine learning, each project comes complete with a unique downloadable data set and a fully-explained step-by-step solution. Because these projects come from Dr. Apeltsin''s vast experience, each solution highlights the most likely failure points along with practical advice for getting past unexpected pitfalls. When you wrap up these five awesome exercises, you''ll have a diverse relevant skill set that''s transferable to working in industry. what''s inside Five in-depth Python exercises with full downloadable data sets Web scraping for text and images Organise datasets with clustering algorithms Visualize complex multi-variable datasets Train a decision tree machine learning algorithm about the reader For readers who know the basics of Python. No prior data science or machine learning skills required. about the author Leonard Apeltsin is a senior data scientist and engineering lead at Primer AI, a startup that specializes in using advanced Natural Language Processing techniques to extract insight from terabytes of unstructured text data. His PhD research focused on bioinformatics that required analyzing millions of sequenced DNA patterns to uncover genetic links in deadly diseases.

Feature Engineering Bookcamp

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Author : Sinan Ozdemir
language : en
Publisher: Simon and Schuster
Release Date : 2022-10-04

Feature Engineering Bookcamp written by Sinan Ozdemir and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-04 with Computers categories.

Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book's practical case-studies reveal feature engineering techniques that upgrade your data wrangling--and your ML results. Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book's practical case-studies reveal feature engineering techniques that upgrade your data wrangling--and your ML results. Feature Engineering Bookcamp delivers hands-on experience with important techniques for optimizing your training data. As you practice your skills in cleaning and transforming data, working with unstructured image and text data, and implementing bias mitigation, you'll quickly see improvements in your end results. You'll learn by exploring real-world scenarios from different domains, including healthcare, finance, and natural language processing. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Data Science Bookcamp

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Author : Leonard Apeltsin
language : en
Publisher:
Release Date : 2021

Data Science Bookcamp written by Leonard Apeltsin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

Data Science Bookcamp doesn't stop with surface-level theory and toy examples. As you work through each project, you'll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don't quite fit the model you're building. You'll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you'll be confident in your skills because you can see the results.

Feature Engineering Bookcamp

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Author : Sinan Ozdemir
language : en
Publisher: Simon and Schuster
Release Date : 2022-10-18

Deliver huge improvements to your machine learning pipelines without spending hours fine-tuning parameters! This book’s practical case-studies reveal feature engineering techniques that upgrade your data wrangling—and your ML results. In Feature Engineering Bookcamp you will learn how to: Identify and implement feature transformations for your data Build powerful machine learning pipelines with unstructured data like text and images Quantify and minimize bias in machine learning pipelines at the data level Use feature stores to build real-time feature engineering pipelines Enhance existing machine learning pipelines by manipulating the input data Use state-of-the-art deep learning models to extract hidden patterns in data Feature Engineering Bookcamp guides you through a collection of projects that give you hands-on practice with core feature engineering techniques. You’ll work with feature engineering practices that speed up the time it takes to process data and deliver real improvements in your model’s performance. This instantly-useful book skips the abstract mathematical theory and minutely-detailed formulas; instead you’ll learn through interesting code-driven case studies, including tweet classification, COVID detection, recidivism prediction, stock price movement detection, and more. About the technology Get better output from machine learning pipelines by improving your training data! Use feature engineering, a machine learning technique for designing relevant input variables based on your existing data, to simplify training and enhance model performance. While fine-tuning hyperparameters or tweaking models may give you a minor performance bump, feature engineering delivers dramatic improvements by transforming your data pipeline. About the book Feature Engineering Bookcamp walks you through six hands-on projects where you’ll learn to upgrade your training data using feature engineering. Each chapter explores a new code-driven case study, taken from real-world industries like finance and healthcare. You’ll practice cleaning and transforming data, mitigating bias, and more. The book is full of performance-enhancing tips for all major ML subdomains—from natural language processing to time-series analysis. What's inside Identify and implement feature transformations Build machine learning pipelines with unstructured data Quantify and minimize bias in ML pipelines Use feature stores to build real-time feature engineering pipelines Enhance existing pipelines by manipulating input data About the reader For experienced machine learning engineers familiar with Python. About the author Sinan Ozdemir is the founder and CTO of Shiba, a former lecturer of Data Science at Johns Hopkins University, and the author of multiple textbooks on data science and machine learning. Table of Contents 1 Introduction to feature engineering 2 The basics of feature engineering 3 Healthcare: Diagnosing COVID-19 4 Bias and fairness: Modeling recidivism 5 Natural language processing: Classifying social media sentiment 6 Computer vision: Object recognition 7 Time series analysis: Day trading with machine learning 8 Feature stores 9 Putting it all together

Powerful Python

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Author : Aaron Maxwell
language : en
Publisher:
Release Date : 2017-05-07

Powerful Python written by Aaron Maxwell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-07 with Python (Computer program language) categories.

There are many books for those new to Python, new to programming, or both. Powerful Python is different. Written for experienced developers like you, its carefully crafted chapters teach intermediate and advanced strategies, patterns, and tools for modern Python. Focused on Python 3, with full support for 2.7. DRM-free digital upgrade: powerfulpython.com/book-upgrade "Feels like Neo learning Jiu jitsu in the Matrix." - John Beauford (@johnbeauford) "I just wanted to let you know what an excellent book this is... I keep going back to your book to learn Python." - Fahad Qazi, London, UK "Thanks. Keep up the good work. Your chapter on decorators is the best I have seen on that topic." - Leon Tietz, Minnesota, USA "Powerful Python is already helping me get huge optimization gains." - Timothy Dobbins (@TmthyDobbins) "What have I found good and valuable about the book so far? Everything honestly. The clear explanations, solid code examples have really helped me advance as a Python coder... Thank you! It has really helped me grasp some advanced concepts that I felt were beyond my abilities." - Nick S., Colorado, USA For data scientists, back-end engineers, web developers, sysadmins, devops, QA testers and more. What's included: An unrelenting selective spotlight on what's most valuable and impactful to working, full-time, professional Python developers Well-researched, detailed, realistic code on almost every page, powerfully illustrating key points. Very little "toy code" How to use decorators to add rich features to functions and classes; untangle distinct, frustratingly intertwined concerns in your code; and build powerful, extensible software frameworks How to use Python in ways that incentivize other developers to use and re-use your code, again and again... amplifying the impact of the code you write, and boosting your reputation among your peers Powerfully and easily weave iterators and generators throughout your applications, making them massively scalable, highly performant, and far more readable and maintainable How to fully leverage Python's exception and error model... giving you a detailed understanding even experienced Pythonistas often lack, and putting some of the most powerfully Pythonic exception-handling patterns in your toolbox How "magic methods" imbue natural, readable, expressive syntax into your classes and objects... and how to "break the rules" to craft stunningly intuitive, compellingly reusable library interfaces Valuable and powerful design patterns, and how Python's special language features give you uniquely powerful implementations not possible in other languages Deep and detailed instruction on how to write practical, realistic unit tests... using test-driven development to easily get into a state of flow... where you find yourself implementing feature after feature, keeping your focus with ease for long periods of time How to rapidly set up effective logging for scripts, sprawling Python applications, and everything in between An enthusiastic and unapologetic focus on Python 3, and what makes it great... with full explanation and support for getting the same results with Python 2.7 More at PowerfulPython.com.

Python Bookcamp

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