Friday 26 April 2024
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
Python Coding April 06, 2024 Books No comments
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
Python Coding March 23, 2024 Books, Python No comments
Buy : Python Programming for Beginners: A Kid's Guide to Coding Fundamentals
Buy : Coding Games in Python (DK Help Your Kids)
Buy : Coding for Kids: Python: Learn to Code with 50 Awesome Games and Activities
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
Python Coding March 08, 2024 Books, Data Science, Google No comments
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
Tableau Business Intelligence Analyst Professional Certificate
Python Coding March 08, 2024 Books, Data Science No comments
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
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
Python Coding March 07, 2024 Books, Machine Learning, Python No comments
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
Python Coding March 07, 2024 AI, Books, Python No comments
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
Python Coding March 06, 2024 Books, Coursera, Data Science No comments
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
Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples
Python Coding March 06, 2024 Books, Machine Learning No comments
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
- Introduction to ML Engineering
- The Machine Learning Development Process
- From Model to Model Factory
- Packaging Up
- Deployment Patterns and Tools
- Scaling Up
- Deep Learning, Generative AI, and LLMOps
- Building an Example ML Microservice
- 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)
Python Coding March 05, 2024 Books, Python, SQL No comments
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
Python Coding March 05, 2024 Books, data management, Machine Learning, Python No comments
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
Python Coding March 05, 2024 Books, Python No comments
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
Python Coding March 05, 2024 Books, Data Science, Python No comments
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)
Python Coding March 05, 2024 Books, Data Science, Machine Learning, Python No comments
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
Python Coding February 29, 2024 Books, Machine Learning, Python No comments
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 2: Inference
Python Coding February 29, 2024 Books, Python No comments
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 1: Representation
Python Coding February 29, 2024 Books, Coursera, Python No comments
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
Evaluations of AI Applications in Healthcare
Python Coding February 29, 2024 AI, Books, Coursera, Python No comments
What you'll learn
Principles and practical considerations for integrating AI into clinical workflows
Best practices of AI applications to promote fair and equitable healthcare solutions
Challenges of regulation of AI applications and which components of a model can be regulated
What standard evaluation metrics do and do not provide
Join Free: Evaluations of AI Applications in Healthcare
There are 7 modules in this course
Fundamentals of Machine Learning for Healthcare
Python Coding February 29, 2024 Books, Machine Learning, Python No comments
What you'll learn
Define important relationships between the fields of machine learning, biostatistics, and traditional computer programming.
Learn about advanced neural network architectures for tasks ranging from text classification to object detection and segmentation.
Learn important approaches for leveraging data to train, validate, and test machine learning models.
Understand how dynamic medical practice and discontinuous timelines impact clinical machine learning application development and deployment.
Join Free: Fundamentals of Machine Learning for Healthcare
There are 8 modules in this course
Tuesday 27 February 2024
Python Data Science Handbook: Essential Tools for Working with Data
Python Coding February 27, 2024 Books, Data Science, Python No comments
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all—IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools.
Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
With this handbook, you'll learn how:
IPython and Jupyter provide computational environments for scientists using Python
NumPy includes the ndarray for efficient storage and manipulation of dense data arrays
Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data
Matplotlib includes capabilities for a flexible range of data visualizations
Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
Join Free: Python Data Science Handbook: Essential Tools for Working with Data
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