Data Science for Beginners: Unlocking the Power of Data with Easy-to-Understand Concepts and Techniques. Part 3

By Tom Lesley

"Data Science for Beginners: Unlocking the Power of Data with Easy-to-Understand Concepts and Techniques" is a comprehensive guide for those who are new to the field of data science and machine learning. The book provides an overview of the exciting and rapidly-growing field of data science and the role that machine learning plays within it.

 

The book starts with a clear and concise definition of machine learning, followed by an exploration of its different types and basic concepts. The reader is then introduced to real-world applications of machine learning and the importance of this technology in today's world.

The book then covers the basics of setting up a data science development environment and provides an overview of popular programming languages and tools used in data science. The reader will learn how to access and process data using SQL, Excel, and other data analysis tools, as well as techniques for data visualization.

Data preparation and preprocessing are essential components of the data science process, and the book provides a thorough explanation of these techniques, including data cleaning, transformation, and feature selection. The reader will also learn about data normalization, scaling, and dealing with missing and noisy data.

 

The book concludes with an overview of popular machine learning algorithms, including linear regression, logistic regression, decision trees, random forest, support vector machines, naive bayes, and neural networks. The reader will learn how to evaluate the performance of these algorithms and how to choose the best algorithm for a given problem through model selection and tuning.

 

"Data Science for Beginners" is a comprehensive and accessible guide for anyone looking to start their journey in the exciting and rapidly-growing field of data science and machine learning.

• Language: English
• Publisher: May Reads
• Release date: Apr 20, 2024
• ISBN: 9798224513871

Book preview

Tom Lesley

Table of Content

Introduction

Overview of Data Science and Machine Learning

Importance of Machine Learning in Data Scienc 

Overview of the book and its objectives

Understanding Machine Learning

Definition of Machine Learning

Types of Machine Learning

Basic concepts and terminologies in Machine Learning

Applications of Machine Learning in real-world problems

Getting Started with Data Science

Setting up the development environment

Overview of popular programming languages and tools used in Data Science

Accessing and processing data using SQL, Excel, and other data analysis tools

Introduction to data visualization techniques

Data Preparation and Preprocessing

Overview of data preparation techniques

Understanding the different types of data and their characteristics

Data cleaning, transformation, and feature selection

Dealing with missing and noisy data

Data normalization and scaling

Overview of Machine Learning Algorithms

Linear Regression

Logistic Regression

K-Nearest Neighbors (KNN)

Decision Trees

Random Forest

Support Vector Machines (SVM)

Naive Bayes

Neural Networks

Introduction

Overview of Data Science and Machine Learning

Data Science and Machine Learning are two interrelated fields that have gained significant importance in recent years due to the massive growth of data and the increasing demand for better insights and predictions. This chapter provides an overview of these fields, their importance, and the key concepts and techniques involved.

What is Data Science? Data Science is a field that uses scientific methods, algorithms, and systems to extract insights and knowledge from data. It involves collecting, processing, cleaning, and analyzing large amounts of data to uncover patterns, trends, and relationships that can inform business decisions and drive innovation.

What is Machine Learning? Machine Learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.

Importance of Machine Learning in Data Science Machine Learning plays a critical role in Data Science by providing a means to process and analyze large amounts of data, identify patterns, and make predictions based on that data. With the increasing availability of large amounts of data, Machine Learning has become a crucial tool for businesses, organizations, and researchers to gain insights and make informed decisions.

Types of Machine Learning Machine Learning can be broadly categorized into three main types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Supervised Learning involves training a model using labeled data, where the model is used to make predictions based on the input data.

Unsupervised Learning involves training a model using unlabeled data, where the model is used to identify patterns and relationships in the data.

Reinforcement Learning involves training a model using trial-and-error methods, where the model learns to make decisions based on rewards and punishments.

Basic Concepts and Terminologies in Machine Learning Machine Learning involves several key concepts and terminologies, including features, target variables, training and testing datasets, algorithms, models, overfitting, and underfitting.

Features are the input variables used to train a model.

Target variables are the output variables that the model is trained to predict.

Training and testing datasets are used to train and evaluate a model.

Algorithms are the mathematical techniques used to build models.

Models are the representations of the relationships between the features and target variables.

Overfitting and underfitting refer to situations