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Tag: Layman

  • Numsense! Data Science for the Layman: No Math Added


    Price: $15.99
    (as of Jan 18,2025 03:17:22 UTC – Details)




    ASIN ‏ : ‎ B01N29ZEM6
    Publisher ‏ : ‎ ; 1st edition (February 3, 2017)
    Publication date ‏ : ‎ February 3, 2017
    Language ‏ : ‎ English
    File size ‏ : ‎ 13987 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Enabled
    Word Wise ‏ : ‎ Enabled
    Print length ‏ : ‎ 147 pages

    Customers say

    Customers find the book provides an informative overview of the data science job role and de-mystifies the subject. They appreciate the authors’ simplicity and brevity, as well as the clear explanations and examples. Many consider it a must-read and consider it a good value for money.

    AI-generated from the text of customer reviews


    Have you ever wanted to understand data science without having to wade through complex mathematical equations and formulas? Look no further than Numsense! This book breaks down the basics of data science in a way that anyone can understand, no math required.

    From explaining the importance of data analysis to discussing common data science techniques, Numsense is the perfect guide for those looking to gain a better understanding of this rapidly growing field. Whether you’re a business owner, student, or just someone interested in learning more about data science, this book is for you.

    So why wait? Pick up a copy of Numsense today and start your journey into the world of data science – no math added!
    #Numsense #Data #Science #Layman #Math #Added,machine learning: an applied mathematics introduction

  • Machine Learning in Action: A Primer for The Layman, Step by Step Guide for Newbies (Machine Learning for Beginners Book 1)

    Machine Learning in Action: A Primer for The Layman, Step by Step Guide for Newbies (Machine Learning for Beginners Book 1)


    Price: $3.99
    (as of Dec 27,2024 06:44:16 UTC – Details)




    ASIN ‏ : ‎ B07F6Z2ZKJ
    Publication date ‏ : ‎ July 10, 2018
    Language ‏ : ‎ English
    File size ‏ : ‎ 8488 KB
    Simultaneous device usage ‏ : ‎ Unlimited
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 53 pages

    Customers say

    Customers find the book helpful and useful for beginners. It explains different concepts clearly with sketches for better explanations. The writing quality is good and the book is written for non-programmers.

    AI-generated from the text of customer reviews


    Are you interested in learning about machine learning but don’t know where to start? Look no further! In this post, we will provide a step-by-step guide for beginners to understand machine learning concepts and how they are applied in real-life scenarios.

    Chapter 1: Introduction to Machine Learning
    – What is machine learning and why is it important?
    – Types of machine learning: supervised, unsupervised, and reinforcement learning
    – Common machine learning algorithms: linear regression, decision trees, and neural networks

    Chapter 2: Data Collection and Preprocessing
    – Gathering and cleaning data for machine learning models
    – Feature engineering techniques to improve model performance
    – Handling missing values and outlier detection

    Chapter 3: Model Building and Evaluation
    – Splitting data into training and testing sets
    – Training machine learning models using libraries like scikit-learn and TensorFlow
    – Evaluating model performance using metrics like accuracy, precision, and recall

    Chapter 4: Model Deployment and Monitoring
    – Deploying machine learning models in production environments
    – Monitoring model performance and making adjustments as needed
    – Understanding ethical considerations and bias in machine learning

    By the end of this guide, you will have a solid understanding of the basics of machine learning and how to apply them in practice. Whether you are a student, a data enthusiast, or just curious about this exciting field, Machine Learning in Action: A Primer for The Layman is the perfect introduction to get started. Happy learning!
    #Machine #Learning #Action #Primer #Layman #Step #Step #Guide #Newbies #Machine #Learning #Beginners #Book

  • Machine Learning in Action: A Primer for The Layman, Step by Step Guide for Newbies (Machine Learning for Beginners)

    Machine Learning in Action: A Primer for The Layman, Step by Step Guide for Newbies (Machine Learning for Beginners)


    Price: $5.45
    (as of Dec 17,2024 23:27:56 UTC – Details)


    Customers say

    Customers find the book helpful and easy to understand. It provides a good introduction to machine learning in plain English for non-programmers. The writing is clear and well-written, with illustrations for better explanation.

    AI-generated from the text of customer reviews


    Machine Learning in Action: A Primer for The Layman, Step by Step Guide for Newbies (Machine Learning for Beginners)

    Are you interested in diving into the world of machine learning but don’t know where to start? Look no further! In this beginner-friendly guide, we will walk you through the basics of machine learning and provide you with a step-by-step approach to getting started.

    Step 1: Understand the Basics
    Before diving into the world of machine learning, it’s important to have a basic understanding of what it is and how it works. Machine learning is a subset of artificial intelligence that allows computers to learn from data without being explicitly programmed. Essentially, the computer is able to identify patterns in large amounts of data and make predictions or decisions based on those patterns.

    Step 2: Choose a Machine Learning Method
    There are several different methods of machine learning, each suited to different types of problems. Some common methods include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model on labeled data, unsupervised learning involves finding patterns in unlabeled data, and reinforcement learning involves training a model to make decisions based on rewards or penalties.

    Step 3: Gather and Prepare Data
    One of the most important steps in machine learning is gathering and preparing data. Without good quality data, your model will not be able to make accurate predictions. Make sure to clean and preprocess your data before feeding it into your model.

    Step 4: Choose and Train a Model
    Once you have your data prepared, it’s time to choose a machine learning model and train it on your data. There are many different types of models to choose from, including decision trees, neural networks, and support vector machines. Experiment with different models to see which one performs best on your data.

    Step 5: Evaluate and Fine-Tune Your Model
    After training your model, it’s important to evaluate its performance and fine-tune it for optimal results. Use metrics like accuracy, precision, recall, and F1 score to evaluate your model’s performance. If your model is not performing well, try adjusting hyperparameters or using different features to improve its accuracy.

    By following these steps, you will be well on your way to mastering the basics of machine learning. Remember, practice makes perfect, so don’t be afraid to experiment and try out different approaches. Happy learning!
    #Machine #Learning #Action #Primer #Layman #Step #Step #Guide #Newbies #Machine #Learning #Beginners