Showing posts with label Books. Show all posts
Showing posts with label Books. Show all posts

Sunday 19 May 2024

Introduction to Modern Statistics free pdf

 

Introduction to Modern Statistics is a re-imagining of a previous title, Introduction to Statistics with Randomization and Simulation. The new book puts a heavy emphasis on exploratory data analysis (specifically exploring multivariate relationships using visualization, summarization, and descriptive models) and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches. Other highlights include:

Web native book. The online book is available in HTML, which offers easy navigation and searchability in the browser. The book is built with the bookdown package and the source code to reproduce the book can be found on GitHub. Along with the bookdown site, this book is also available as a PDF and in paperback.

Tutorials. While the main text of the book is agnostic to statistical software and computing language, each part features 4-8 interactive R tutorials (for a total of 32 tutorials) that walk you through the implementation of the part content in R with the tidyverse for data wrangling and visualisation and the tidyverse-friendly infer package for inference. The self-paced and interactive R tutorials were developed using the learnr R package, and only an internet browser is needed to complete them.

Labs. Each part also features 1-2 R based labs. The labs consist of data analysis case studies and they also make heavy use of the tidyverse and infer packages.

Datasets. Datasets used in the book are marked with a link to where you can find the raw data. The majority of these point to the openintro package. You can install the openintro package from CRAN or get the development version on GitHub.

Hard Copy: Introduction to Modern Statistics

Free PDF: Introduction to Modern Statistics




Saturday 4 May 2024

Data Science: The Hard Parts: Techniques for Excelling at Data Science

 

This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.

Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.

With this book, you will:

Understand how data science creates value

Deliver compelling narratives to sell your data science project

Build a business case using unit economics principles

Create new features for a ML model using storytelling

Learn how to decompose KPIs

Perform growth decompositions to find root causes for changes in a metric

Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

PDF: Data Science: The Hard Parts: Techniques for Excelling at Data Science


Hard Copy: Data Science: The Hard Parts: Techniques for Excelling at Data Science


Statistical Inference and Probability

 

An experienced author in the field of data analytics and statistics, John Macinnes has produced a straight-forward text that breaks down the complex topic of inferential statistics with accessible language and detailed examples. It covers a range of topics, including:

·       Probability and Sampling distributions

·       Inference and regression

·       Power, effect size and inverse probability

Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

Hard Copy: Statistical Inference and Probability


PDF: Statistical Inference and Probability (The SAGE Quantitative Research Kit)

Friday 26 April 2024

Book Giveaway ! One Book one Person

 


  1. Software Architecture Patterns for Serverless Systems - Second Edition: Architecting for innovation with event-driven microservices and micro frontends
  2. Modern C++ Programming Cookbook - Third Edition: Master modern C++ including the latest features of C++23 with 140+ practical recipes
  3. Build your own Programming Language - Second Edition: A developer's comprehensive guide to crafting, compiling, and implementing programming languages
  4. Unity Cookbook - Fifth Edition: Over 160 recipes to craft your own masterpiece in Unity 2023
  5. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics Series)
  6. Terraform Cookbook - Second Edition: Provision, run, and scale cloud architecture with real-world examples using Terraform
  7. Dancing with Qubits - Second Edition: From qubits to algorithms, embark on the quantum computing journey shaping our future
  8. Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples
  9. Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs
  10. Transformers for Natural Language Processing and Computer Vision - Third Edition: Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3
  11. Java Coding Problems - Second Edition: Become an expert Java programmer by solving over 250 brand-new, modern, real-world problems
  12. Azure Data Factory Cookbook - Second Edition: A data engineer's guide to building and managing ETL and ELT pipelines with data integration
  13. Systems Engineering Demystified - Second Edition: Apply modern, model-based systems engineering techniques to build complex systems
  14. Extending Power BI with Python and R - Second Edition: Perform advanced analysis using the power of analytical languages
  15. 50 Algorithms Every Programmer Should Know - Second Edition: An unbeatable arsenal of algorithmic solutions for real-world problems
  16. Python for Security and Networking - Third Edition: Leverage Python modules and tools in securing your network and applications
  17. Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster
  18. Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples
  19. Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models
  20. Functional Python Programming - Third Edition: Use a functional approach to write succinct, expressive, and efficient Python code
  21. Mastering Go - Fourth Edition: Leverage Go's expertise for advanced utilities, empowering you to develop professional software


Saturday 6 April 2024

Python Advanced Programming: The Guide to Learn Python Programming. Reference with Exercises and Samples About Dynamical Programming, Multithreading, Multiprocessing, Debugging, Testing and More

 


If you want to learn the most modern programming language in the world, then keep reading.

Python is an high-level programming language. It's a modern language, easy to learn and understand but very powerful.

It's a versatile programming language that is now being used on a lot of different projects, from world-class internet companies to small hobbyists, Python is extremely flexible and can be useful in a lot of different fields.

With Python, you can develop apps, games and any kind of software.

In fact, Python is one of the highest-demand skill for professional developers.

Python Advanced Programming approaches this programming language in a very practical method to make sure you can learn everything you need to start working with Python as soon as possible and to handle advanced feature of this unique language.

You will learn...

▸ Advanced procedural programming techniques

▸ What is Dynamic Code Execution

▸ Advanced OOP functions most developers are not aware of

▸ Functional-style programming with Python

▸ How to debug, test and profile your software

▸ How to handle multiple processes

▸ The best techniques to spread the workload on different threads

Paper Back : Python Advanced Programming: The Guide to Learn Python Programming. Reference with Exercises and Samples About Dynamical Programming, Multithreading, Multiprocessing, Debugging, Testing and More

PDF: Python Advanced Programming: The Guide to Learn Python Programming. Reference with Exercises and Samples About Dynamical Programming, Multithreading, Multiprocessing, Debugging, Testing and More

Saturday 23 March 2024

Python Books for Kids

 



Think like a programmer with this fun beginner's guide to Python for ages 10 to 14

Kids can learn to code with the power of Python! Python Programming for Beginners is the perfect way to introduce aspiring coders to this simple and powerful coding language. This book teaches kids all about Python and programming fundamentals—and is packed full of fun and creative activities that make learning a blast!

In Python Programming for Beginners, kids will start off with the basics, learning all about fundamental coding concepts and how they can put these concepts together in Python to build their own games and programs. Each chapter focuses on a different coding concept—like variables, data types, and loops—and features three awesome coding activities to try. These activities get more difficult as they go, so young coders can see just how much their skills are growing. By the end of Python Programming for Beginners, they'll create their own fully functional sci-fi game and crack the code to a secret message!

Python Programming for Beginners features:
No coding experience needed!—Designed just for kids, this Python programming book is filled with step-by-step directions, simple explanations, and detailed code breakdowns.
Build a coding toolbox—Kids will build their programming skills, learn how to troubleshoot bugs with a handy bug-hunting guide, and practice their Python programming knowledge with cool activities.
Why Python programming?—Python is an awesome starting language for kids! It's a powerful programming language that can be used for lots of projects but features simple syntax so beginners can focus on learning programming logic.

Set kids up for a lifetime of programming success with Python Programming for Beginners .

Buy : Python Programming for Beginners: A Kid's Guide to Coding Fundamentals





Build and play your own computer games, from creative quizzes to perplexing puzzles, by coding them in the Python programming language!

Whether you're a seasoned programmer or a beginner hoping to learn Python, you'll find Coding Games in Python fun to read and easy to follow. Each chapter shows you how to construct a complete working game in simple numbered steps. Using freely available resources such as Pygame, Pygame Zero, and a downloadable pack of images and sounds, you can add animations, music, scrolling backgrounds, scenery, and other exciting professional touches.

After building the game, find out how to adapt it to create your own personalised version with secret hacks and cheat codes!

You'll master the key concepts that programmers need to write code - not just in Python, but in all programming languages. Find out what bugs, loops, flags, strings, and turtles are. Learn how to plan and design the ultimate game, and then play it to destruction as you test and debug it.

Before you know it, you'll be a coding genius!

Buy : Coding Games in Python (DK Help Your Kids)



Games and activities that teach kids ages 10+ to code with Python

Learning to code isn't as hard as it sounds—you just have to get started! Coding for Kids: Python starts kids off right with 50 fun, interactive activities that teach them the basics of the Python programming language. From learning the essential building blocks of programming to creating their very own games, kids will progress through unique lessons packed with helpful examples—and a little silliness!

Kids will follow along by starting to code (and debug their code) step by step, seeing the results of their coding in real time. Activities at the end of each chapter help test their new knowledge by combining multiple concepts. For young programmers who really want to show off their creativity, there are extra tricky challenges to tackle after each chapter. All kids need to get started is a computer and this book.

This beginner's guide to Python for kids includes:
50 Innovative exercises—Coding concepts come to life with game-based exercises for creating code blocks, drawing pictures using a prewritten module, and more.
Easy-to-follow guidance—New coders will be supported by thorough instructions, sample code, and explanations of new programming terms.
Engaging visual lessons—Colorful illustrations and screenshots for reference help capture kids' interest and keep lessons clear and simple.

Encourage kids to think independently and have fun learning an amazing new skill with this coding book for kids.


Buy : Coding for Kids: Python: Learn to Code with 50 Awesome Games and Activities




The second edition of the best-selling Python for Kids—which brings you (and your parents) into the world of programming—has been completely updated to use the latest version of Python, along with tons of new projects!

Python is a powerful programming language that’s easy to learn and fun to use! But books about programming in Python can be dull and that’s no fun for anyone.

Python for Kids brings kids (and their parents) into the wonderful world of programming. Jason R. Briggs guides you through the basics, experimenting with unique (and hilarious) example programs featuring ravenous monsters, secret agents, thieving ravens, and more. New terms are defined; code is colored and explained; puzzles stretch the brain and strengthen understanding; and full-color illustrations keep you engaged throughout.

By the end of the book, you’ll have programmed two games: a clone of the famous Pong, and “Mr. Stick Man Races for the Exit”—a platform game with jumps and animation.

This second edition is revised and updated to reflect Python 3 programming practices. There are new puzzles to inspire you and two new appendices to guide you through Python’s built-in modules and troubleshooting your code.

As you strike out on your programming adventure, you’ll learn how to:

Use fundamental data structures like lists, tuples, and dictionaries
Organize and reuse your code with functions and modules
Use control structures like loops and conditional statements
Draw shapes and patterns with Python’s turtle module
Create games, animations, and other graphical wonders with tkinter

Why should serious adults have all the fun? Python for Kids is your ticket into the amazing world of computer programming.

Covers Python 3.x which runs on Windows, macOS, Linux, even Raspberry Pi

Buy : Python for Kids, 2nd Edition: A Playful Introduction to Programming


Friday 8 March 2024

Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate

 


What you'll learn

Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.

Employ BigQuery to carry out interactive data analysis.

Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.

Choose between different data processing products on Google Cloud.

Join Free: Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate 

Professional Certificate - 6 course series

Google Cloud Professional Data Engineer certification was ranked #1 
on Global Knowledge's list of 15 top-paying certifications in 2021
! Enroll now to prepare!

---

87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized
 Google Cloud Professional Data Engineer
 certification.

Here's what you have to do

1) Complete the Coursera Data Engineering Professional Certificate

2) Review other recommended resources for the Google Cloud Professional Data Engineer certification
 exam

3) Review the Professional Data Engineer exam guide

4) Complete Professional Data Engineer sample questions

5)Register for the Google Cloud certification exam (remotely or at a test center)

Applied Learning Project

This professional certificate incorporates hands-on labs using Qwiklabs platform.These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud Platform products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.

Applied Learning Project

 This Professional Certificate incorporates hands-on labs using our Qwiklabs platform.

These hands on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as Google BigQuery, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules.

Tableau Business Intelligence Analyst Professional Certificate

 


What you'll learn

Gain the essential skills necessary to excel in an entry-level Business Intelligence Analytics role.

Learn to use Tableau Public to manipulate and prepare data for analysis.

Craft and dissect data visualizations that reveal patterns and drive actionable insights.

Construct captivating narratives through data, enabling stakeholders to explore insights effectively.

Join Free: Tableau Business Intelligence Analyst Professional Certificate

Professional Certificate - 8 course series

Whether you are just starting out or looking for a career change, the Tableau Business Intelligence Analytics Professional Certificate will prepare you for entry-level roles that require fundamental Tableau skills, such as business intelligence analyst roles. If you are detail-oriented and have an interest in looking for trends in data, this program is for you. Through hands-on, real-world scenarios, you learn how to use the Tableau platform to evaluate data to generate and present actionable business insights. Upon completion, you will be prepared to take the 
Tableau Desktop Specialist Exam
.  With this certification, you will be qualified to apply for a position in the business intelligence analyst field.  

In this program, you’ll: 

 Craft problem statements, business requirement documents, and visual models.

 Connect with various data sources and preprocess data in Tableau for enhanced quality and analysis.

 Learn to utilize the benefits of Tableau’s analytics features for efficient reporting.

Learn to create advanced and spatial analytics data visualizations by integrating motion and multi-layer elements to effectively communicate insights to stakeholders.

Employ data storytelling principles and design techniques to craft compelling presentations that empower you to extract meaningful insights.

This program was developed by Tableau experts, designed to prepare you for a career in Business Intelligence Analytics and help you learn the most relevant skills.

Applied Learning Project

 Throughout the program, you’ll engage in applied learning through hands-on activities to help level up your knowledge. Each course contains ungraded quizzes throughout the content, a graded quiz at the end of each module, and a variety of hands-on projects. The program activities will give you the skills to : 

Preprocess, manage, and manipulate data for analysis using Tableau. 

Create and customize Visualizations in Tableau. 

Learn best practices for creating presentations to communicate data analysis insights to stakeholders. 

Thursday 7 March 2024

Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

 


A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores

Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods

Analyze and extract insights from complex models from CNNs to BERT to time series models

Book Description

Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.

Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.

In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.

By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.

What you will learn

Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty

Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers

Use monotonic and interaction constraints to make fairer and safer models

Understand how to mitigate the influence of bias in datasets

Leverage sensitivity analysis factor prioritization and factor fixing for any model

Discover how to make models more reliable with adversarial robustness

Who this book is for

This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.

Table of Contents

Interpretation, Interpretability and Explainability; and why does it all matter?

Key Concepts of Interpretability

Interpretation Challenges

Global Model-agnostic Interpretation Methods

Local Model-agnostic Interpretation Methods

Anchors and Counterfactual Explanations

Visualizing Convolutional Neural Networks

Interpreting NLP Transformers

Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

Feature Selection and Engineering for Interpretability

Bias Mitigation and Causal Inference Methods

Monotonic Constraints and Model Tuning for Interpretability

Adversarial Robustness

What's Next for Machine Learning Interpretability?

Hard Copy: Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

 


Get to grips with the LangChain framework from theory to deployment and develop production-ready applications.

Code examples regularly updated on GitHub to keep you abreast of the latest LangChain developments.

Purchase of the print or Kindle book includes a free PDF eBook.

Key Features

Learn how to leverage LLMs' capabilities and work around their inherent weaknesses

Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges

Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality

Book Description

ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis - illustrating the expansive utility of LLMs in real-world applications.

Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.

What you will learn

Understand LLMs, their strengths and limitations

Grasp generative AI fundamentals and industry trends

Create LLM apps with LangChain like question-answering systems and chatbots

Understand transformer models and attention mechanisms

Automate data analysis and visualization using pandas and Python

Grasp prompt engineering to improve performance

Fine-tune LLMs and get to know the tools to unleash their power

Deploy LLMs as a service with LangChain and apply evaluation strategies

Privately interact with documents using open-source LLMs to prevent data leaks

Who this book is for

The book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena.

Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.

Table of Contents

What Is Generative AI?

LangChain for LLM Apps

Getting Started with LangChain

Building Capable Assistants

Building a Chatbot like ChatGPT

Developing Software with Generative AI

LLMs for Data Science

Customizing LLMs and Their Output

Generative AI in Production

The Future of Generative Models

Hard Copy: Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

Wednesday 6 March 2024

Introduction to Data Science Specialization

 


What you'll learn

Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists  

Gain hands-on familiarity with common data science tools including JupyterLab, R Studio, GitHub and Watson Studio 

Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems

Write SQL statements and query Cloud databases using Python from Jupyter notebooks

Join Free: Introduction to Data Science Specialization

Specialization - 4 course series

Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field.  

This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals.

You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets.

In addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in data science foundations.

This Specialization can also be applied toward the 
IBM Data Science Professional Certificate. 

Applied Learning Project

All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets and tools like Jupyter, GitHub, and R Studio. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:

Creating and sharing a Jupyter Notebook containing code blocks and markdown

Devising a problem that can be solved by applying the data science methodology and explain how to apply each stage of the methodology to solve it

Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools

Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples

 


Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems

Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain

Key Features

  • This second edition delves deeper into key machine learning topics, CI/CD, and system design
  • Explore core MLOps practices, such as model management and performance monitoring
  • Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools

Book Description

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field.

The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift.

Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques.

With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.

Hard Copy : Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples

What you will learn

  • Plan and manage end-to-end ML development projects
  • Explore deep learning, LLMs, and LLMOps to leverage generative AI
  • Use Python to package your ML tools and scale up your solutions
  • Get to grips with Apache Spark, Kubernetes, and Ray
  • Build and run ML pipelines with Apache Airflow, ZenML, and Kubeflow
  • Detect drift and build retraining mechanisms into your solutions
  • Improve error handling with control flows and vulnerability scanning
  • Host and build ML microservices and batch processes running on AWS

Who this book is for

This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you’re not a developer but want to manage or understand the product lifecycle of these systems, you’ll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.

Table of Contents

  1. Introduction to ML Engineering
  2. The Machine Learning Development Process
  3. From Model to Model Factory
  4. Packaging Up
  5. Deployment Patterns and Tools
  6. Scaling Up
  7. Deep Learning, Generative AI, and LLMOps
  8. Building an Example ML Microservice
  9. Building an Extract, Transform, Machine Learning Use Case

Tuesday 5 March 2024

Python & SQL Mastery: 5 Books in 1: Your Comprehensive Guide from Novice to Expert (2024 Edition) (Data Dynamics: Python & SQL Mastery)

 


Are you poised to elevate your technical expertise and stay ahead in the rapidly evolving world of data and programming?

Look no further!

Our 5 Books Series is meticulously crafted to guide you from the basics to the most advanced concepts in Python and SQL, making it a must-have for database enthusiasts, aspiring data scientists, and seasoned coders alike.

Comprehensive Learning Journey:

Mastering SQL: Dive deep into every facet of SQL, from fundamental data retrieval to complex transactions, views, and indexing.

Synergizing Code and Data: Explore the synergy between Python and SQL Server Development, mastering techniques from executing SQL queries through Python to advanced data manipulation.

Python and SQL for Data Solutions: Uncover the powerful combination of Python and SQL for data analysis, reporting, and integration, including ETL processes and machine learning applications.

Advanced Data Solutions: Delve into integrating Python and SQL for data retrieval, manipulation, and performance optimization.

Integrating Python and SQL: Master database manipulation, focusing on crafting SQL queries in Python and implementing security best practices.

Empower Your Career: Gain the skills that are highly sought after in today's job market. From database management to advanced analytics, this series prepares you for a multitude of roles in tech, data analysis, and beyond.

Practical, Real-World Application: Each book is packed with practical examples, real-world case studies, and hands-on projects. This approach not only reinforces learning but also prepares you to apply your knowledge effectively in professional settings.

Expert Insight and Future Trends: Learn from experts with years of experience in the field. The series not only teaches you current best practices but also explores emerging trends, ensuring you stay at the forefront of technology.

For Beginners and Experts Alike: Whether you're just starting out or looking to deepen your existing knowledge, our series provides a clear, structured path to mastering both Python and SQL.

Embark on this comprehensive journey to mastering Python and SQL. With our series, you'll transform your career, opening doors to new opportunities and achieving data excellence.

Hard Copy: Python & SQL Mastery: 5 Books in 1: Your Comprehensive Guide from Novice to Expert (2024 Edition) (Data Dynamics: Python & SQL Mastery)

Finance with Rust: The 2024 Quantitative Finance Guide to - Financial Engineering, Machine Learning, Algorithmic Trading, Data Visualization & More

 


Reactive Publishing

"Finance with Rust" is a pioneering guide that introduces financial professionals and software developers to the transformative power of Rust in the financial industry. With its emphasis on speed, safety, and concurrency, Rust presents an unprecedented opportunity to enhance financial systems and applications.

Written by an accomplished software developer and entrepreneur, this book bridges the gap between complex financial processes and cutting-edge technology. It offers a comprehensive exploration of Rust's application in finance, from developing faster algorithms to ensuring data security and system reliability.

Within these pages, you'll discover:

An introduction to Rust for those new to the language, focusing on its relevance and benefits in financial applications.

Step-by-step guides on using Rust to build scalable and secure financial models, algorithms, and infrastructure.

Case studies demonstrating the successful integration of Rust in financial systems, highlighting its impact on performance and security.

Practical insights into leveraging Rust for financial innovation, including blockchain technology, cryptocurrency platforms, and more.

"Finance with Rust" empowers you to stay ahead in the fast-evolving world of financial technology. Whether you're aiming to optimize financial operations, develop high-performance trading systems, or innovate with blockchain and crypto technologies, this book is your essential roadmap to success.

Hard Copy: Finance with Rust: The 2024 Quantitative Finance Guide to - Financial Engineering, Machine Learning, Algorithmic Trading, Data Visualization & More

PYTHON PROGRAMMING FOR BEGINNERS: Mastering Python With No Prior Experience: The Ultimate Guide to Conquer Your Coding Fear From Crash and Land Your First Job in Tech

 


Learn Python Programming Fast - A Beginner's Guide to Mastering Python from Home

Grab the Bonus Chapter Inside with 50 Coding Journal

Python is the most in-demand programming language in 2024. As a beginner, learning Python can open up high-paying remote and freelance job opportunities in fields like data science, web development, AI, and more.

This hands-on Python Programming is designed specifically for beginners with no prior coding experience. It provides a foundations-first introduction to Python programming concepts using simplified explanations, practical examples, and step-by-step tutorials.

Programming is best learned by doing, and thus, this book incorporates numerous practical exercises and real-world projects.

This is not Hype; you will learn something new in this Python Programming for Beginners.

What You Will Learn in this Python Programming for Beginners Book:

Python Installation - How to download Python and set up your coding environment

Python Syntax - Key programming constructs like variables, data types, functions, conditionals and loops

Core Programming Techniques - Best practices for writing clean, efficient Python code

Built-in Data Structures - Hands-on projects using Python lists, tuples, dictionaries and more

Object-Oriented Programming - How to work with classes, objects and inheritance in Python

Python for Web Development - Build a web app and API with Python frameworks like Django and Flask

Python for Data Analysis - Use Python for data science and work with Jupyter Notebooks

Python for Machine Learning - Implement machine learning algorithms for prediction and classification

Bonus: Python Coding Interview Questions - Practice questions and answers to prepare for the interview

This beginner-friendly guide will give you a solid foundation in Python to build real-world apps and land your first Python developer job.

Hard Copy: PYTHON PROGRAMMING FOR BEGINNERS: Mastering Python With No Prior Experience: The Ultimate Guide to Conquer Your Coding Fear From Crash and Land Your First Job in Tech

Econometric Python: Harnessing Data Science for Economic Analysis: The Science of Pythonomics in 2024

 


Reactive Publishing

In the rapidly evolving landscape of economics, "Econometric Python" emerges as a groundbreaking guide, perfectly blending the intricate world of econometrics with the dynamic capabilities of Python. This book is crafted for economists, data scientists, researchers, and students who aspire to revolutionize their approach to economic data analysis.

At its center, "Econometric Python" serves as a beacon for those navigating the complexities of econometric models, offering a unique perspective on applying Python's powerful data science tools in economic research. The book starts with a fundamental introduction to Python, focusing on aspects most relevant to econometric analysis. This makes it an invaluable resource for both Python novices and seasoned programmers.

As the narrative unfolds, readers are led through a series of progressively complex econometric techniques, all demonstrated with Python's state-of-the-art libraries such as pandas, NumPy, and statsmodels. Each chapter is meticulously designed to balance theory and practice, providing in-depth explanations of econometric concepts, followed by practical coding examples.

Key features of "Econometric Python" include:

Comprehensive Coverage: From basic economic concepts to advanced econometric models, the book covers a wide array of topics, ensuring a thorough understanding of both theoretical and practical aspects.

Hands-On Approach: With real-world datasets and step-by-step coding tutorials, readers gain hands-on experience in applying econometric theories using Python.

Latest Trends and Techniques: Stay abreast of the latest developments in both econometrics and Python programming, including machine learning applications in economic data analysis.

Expert Insights: The authors, renowned in the fields of economics and data science, provide valuable insights and tips, enhancing the learning experience.

"Econometric Python" is more than just a textbook; it's a journey into the future of economic analysis. By the end of this book, readers will not only be proficient in using Python for econometric analysis but will also be equipped with the skills to contribute innovatively to the field of economics. Whether it's for academic purposes, professional development, or personal interest, this book is an indispensable asset for anyone looking to merge the power of data science with economic analysis.

Hard Copy: Econometric Python: Harnessing Data Science for Economic Analysis: The Science of Pythonomics in 2024

Python Data Science 2024: Explore Data, Build Skills, and Make Data-Driven Decisions in 30 Days (Machine Learning and Data Analysis for Beginners)

 


Data Science Crash Course for Beginners with Python...

Uncover the energy of records in 30 days with Python Data Science 2024!

Are you searching for a hands-on strategy to study Python coding and Python for Data Analysis fast?

This beginner-friendly route offers you the abilities and self-belief to discover data, construct sensible abilities, and begin making data-driven selections inside a month.

On the program:

Deep mastering

Neural Networks and Deep Learning

Deep Learning Parameters and Hyper-parameters

Deep Neural Networks Layers

Deep Learning Activation Functions

Convolutional Neural Network

Python Data Structures

Best practices in Python and Zen of Python

Installing Python

Python

These are some of the subjects included in this book:

Fundamentals of deep learning

Fundamentals of probability

Fundamentals of statistics

Fundamentals of linear algebra

Introduction to desktop gaining knowledge of and deep learning

Fundamentals of computer learning

Deep gaining knowledge of parameters and hyper-parameters

Deep neural networks layers

Deep getting to know activation functions

Convolutional neural network

Deep mastering in exercise (in jupyter notebooks)

Python information structures

Best practices in python and zen of Python

Installing Python

At the cease of this course, you may be in a position to:

Confidently deal with real-world datasets.

Wrangle, analyze, and visualize facts the usage of Python.

Turn records into actionable insights and knowledgeable decisions.

Speak the language of data-driven professionals.

Lay the basis for in addition studying in statistics science and computing device learning.

Hard Copy: Python Data Science 2024: Explore Data, Build Skills, and Make Data-Driven Decisions in 30 Days (Machine Learning and Data Analysis for Beginners)



Thursday 29 February 2024

Probabilistic Graphical Models 3: Learning

 


Build your subject-matter expertise

This course is part of the Probabilistic Graphical Models Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Probabilistic Graphical Models 3: Learning

There are 8 modules in this course

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. 

This course is the third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.

Probabilistic Graphical Models 2: Inference

 


Build your subject-matter expertise

This course is part of the Probabilistic Graphical Models Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Probabilistic Graphical Models 2: Inference

There are 7 modules in this course

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. 

This course is the second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.

Probabilistic Graphical Models 1: Representation

 


Build your subject-matter expertise

This course is part of the Probabilistic Graphical Models Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Probabilistic Graphical Models 1: Representation

There are 7 modules in this course

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. 

This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.

Popular Posts

Categories

AI (27) Android (24) AngularJS (1) Assembly Language (2) aws (17) Azure (7) BI (10) book (4) Books (118) C (77) C# (12) C++ (82) Course (62) Coursera (180) Cybersecurity (22) data management (11) Data Science (96) Data Strucures (6) Deep Learning (9) Django (6) Downloads (3) edx (2) Engineering (14) Excel (13) Factorial (1) Finance (6) flutter (1) FPL (17) Google (19) Hadoop (3) HTML&CSS (46) IBM (25) IoT (1) IS (25) Java (92) Leet Code (4) Machine Learning (44) Meta (18) MICHIGAN (5) microsoft (4) Pandas (3) PHP (20) Projects (29) Python (757) Python Coding Challenge (238) Questions (2) R (70) React (6) Scripting (1) security (3) Selenium Webdriver (2) Software (17) SQL (40) UX Research (1) web application (8)

Followers

Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses