Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Benjamin Johnston, Ishita Mathur

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Applied Unsupervised Learning with Python. Discover hidden patterns and relationships in unstructured data with Python

Benjamin Johnston, Aaron Jones, Christopher Kruger

Unsupervised learning is a useful and practical solution in situations where labeled data is not available.Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products.By the end of this book, you will have the skills you need to confidently build your own models using Python.

SQL. Analiza danych za pomocą zapytań. Warsztaty praktyczne. Wydanie II

Matt Goldwasser, Upom Malik, Benjamin Johnston

Obecnie mamy dostęp do terabajtów danych. To nieprzebrane źródło cennych informacji, które mogą decydować o upadku albo o rozkwicie firmy. Aby jednak wydobyć z danych potrzebną wiedzę, trzeba się wykazać kompetencjami. Są to cenne umiejętności - profesjonalny analityk danych może przebierać w atrakcyjnych ofertach pracy. Spośród różnych technik analizy danych warto się przyjrzeć zastosowaniu zapytań SQL. SQL to język tworzony i rozwijany dla potrzeb pracy z bazami danych, jest więc szczególnie przydatnym narzędziem w przyborniku analityka danych. Ta książka jest znakomitym przewodnikiem dla początkującego analityka danych. Dzięki niej dowiesz się, jak skutecznie przesiewać i uzyskiwać informacje z surowych danych. Nauczysz się formułować hipotezy i generować opisowe statystyki, a także pisać złożone zapytania SQL, które pozwalają na zagregowanie danych z bazy SQL z danymi pochodzącymi z innych źródeł. Zobaczysz, jak pracować z danymi w różnych formatach, i nauczysz się analizy geoprzestrzennej i analizy tekstu. Poznasz też tajniki pozyskiwania informacji z wykorzystaniem takich metod jak profilowanie i automatyzacja. W książce: przygotowanie danych za pomocą zapytań SQL funkcje agregujące i funkcje okna w SQL bazy danych i Excel oraz kod w R i w Pythonie praca ze złożonymi typami danych optymalizacja zapytań SQL metodyczne rozwiązywanie problemów SQL: znakomite narzędzie w profesjonalnej analizie danych!

SQL dla analityków danych. Opanuj możliwości SQL-a, aby wydobywać informacje z danych. Wydanie III

Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston

Język SQL zwykle służy do pracy z bazami danych, jednak można go używać również do wydajnego przetwarzania ich wielkich zbiorów. W tym celu trzeba dobrze poznać to narzędzie. Wysiłek włożony w zrozumienie SQL-a na pewno się opłaci - dzięki analizie danych można wydobywać z nich bezcenną wiedzę, która bezpośrednio przekłada się na zyski firmy. Ta książka stanowi świetne wprowadzenie do analizy danych. Dzięki niej nauczysz się korzystać z surowych danych, nawet jeśli nie masz odpowiedniego doświadczenia. Zaczniesz od formułowania hipotez i generowania statystyk opisowych, a następnie przystąpisz do pisania zapytań w języku SQL w celu agregowania, przeliczania i łączenia danych z różnych zbiorów. Zapoznasz się też z zaawansowanymi technikami, takimi jak analiza geoprzestrzenna i analiza tekstu. W książce omówiono również profilowanie i automatyzację, które umożliwiają szybsze i wydajniejsze pobieranie informacji. To wszystko pozwoli Ci na skuteczne korzystanie z SQL-a w codziennych scenariuszach biznesowych. Najciekawsze zagadnienia: gruntowne wprowadzenie do analityki danych przygotowywanie danych do analizy optymalizacja kwerend i złożone typy danych funkcje agregujące, funkcja okna i inne metody analizy danych w SQL jak odkrywać prawdę za pomocą SQL-a SQL: spójrz na dane okiem wyszkolonego analityka!

SQL for Data Analytics. Harness the power of SQL to extract insights from data - Third Edition

Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston

Every day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level.SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience.You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional. 

SQL for Data Analytics. Perform fast and efficient data analysis with the power of SQL

Upom Malik, Matt Goldwasser, Benjamin Johnston

Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain the most effective business insights from data, this book is for you.SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You'll discover how to explore and understand data by identifying trends and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll learn how to increase your productivity with the help of profiling and automation.By the end of this book, you'll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional.Please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the GitHub repository. The link to the GitHub repository can be found in the book's preface.

The Applied SQL Data Analytics Workshop. Develop your practical skills and prepare to become a professional data analyst - Second Edition

Matt Goldwasser, Upom Malik, Benjamin Johnston

Every day, businesses operate around the clock and a huge amount of data is generated at a rapid pace. Hidden in this data are key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. Are you ready to enter the exciting world of data analytics and unlock these useful insights?Written by a team of expert data scientists who have used their data analytics skills to transform businesses of all shapes and sizes, The Applied SQL Data Analytics Workshop is a great way to get started with data analysis, showing you how to effectively sieve and process information from raw data, even without any prior experience.The book begins by showing you how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you'll learn how to write SQL queries to aggregate, calculate and combine SQL data from sources outside of your current dataset. You'll also discover how to work with different data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you'll finally be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation.By the end of The Applied SQL Data Analytics Workshop, you'll have the skills you need to start identifying patterns and unlocking insights in your own data. You will be capable of looking and assessing data with the critical eye of a skilled data analyst.

The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition

Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society.Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models.Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks.By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.

The Unsupervised Learning Workshop. Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

Aaron Jones, Christopher Kruger, Benjamin Johnston

Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner.The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding.As you progress, you’ll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you’ll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area.By the end of this book, you’ll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights.