Data Science for Beginners: Intermediate Guide to Machine Learning. Part 2

By Tom Lesley

"Data Science for Beginners: Intermediate Guide to Machine Learning" is a comprehensive guide that covers the fundamentals of data science and machine learning. This book is aimed at beginners and intermediate learners who have a basic understanding of programming and statistics, but want to deepen their understanding of these fields. The book offers a step-by-step approach to learning key concepts and techniques in data science, including data exploration, data visualization, regression analysis, and machine learning algorithms such as decision trees, random forests, and neural networks. With clear explanations, practical examples, and hands-on exercises, this book provides the perfect foundation for anyone looking to start a career in data science or machine learning.

• Language: English
• Publisher: May Reads
• Release date: Apr 19, 2024
• ISBN: 9798224167319

Book preview

Tom Lesley

Table of Content

Chapter 1. Introduction

Definition of Machine Learning

Importance of Machine Learning n today's world

Chapter 2. Machine Learning

Overview of Machine Learning

Supervised Learning

1. Linear Regression

2. Decision Trees

3. Random Forest

Unsupervised Learning

1. K-Means Clustering

2. Principal Component Analysis (PCA)

Reinforcement Learning

Chapter 3. Data Visualization

Overview of Data Visualization

Common Visualizations

1. Bar Charts

2. Line Charts

3. Scatter Plots

Interactive Visualizations

1. Plotly

2. Bokeh

Chapter 4. Big Data and Distributed Computing

Overview of Big Data

Hadoop Ecosystem

1. HDFS

2. MapReduce

Apache Spark

1. RDDs

2. DataFrames

Chapter 5.Conclusion

Summary of Key Points

Career Opportunities in Data Science

Final Thoughts and Recommendations

Chapter 1. Introduction

Definition of Machine Learning

Machine learning is a rapidly growing field of computer science and artificial intelligence that focuses on developing algorithms and models that allow computers to learn from data. It is a subfield of artificial intelligence that involves the development of algorithms and statistical models that enable computers to perform tasks that normally require human intelligence, such as image recognition, speech recognition, and natural language processing. The main goal of machine learning is to enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so.

Machine learning algorithms can be divided into two main categories: supervised learning and unsupervised learning. Supervised learning algorithms are trained on labeled data, where the desired output or target variable is known. These algorithms are used for tasks such as classification and regression, and are designed to make predictions about new, unseen data. Unsupervised learning algorithms, on the other hand, are trained on unlabeled data and are used for tasks such as clustering and dimensionality reduction.

Another important concept in machine learning is overfitting, which occurs when a model is too complex and fits the training data too well. Overfitting can lead to poor performance on unseen data, and is a common challenge faced by machine learning practitioners. To mitigate overfitting, machine learning models can be regularized, or they can be trained on a larger dataset to make them more robust.

There are several popular machine learning algorithms that are widely used in industry and academia, including linear regression, logistic regression, k-nearest neighbors, decision trees, random forests, gradient boosting, support vector machines, and neural networks. Each algorithm has its own strengths and weaknesses, and the choice of which algorithm to use depends on the specific problem being solved and the type of data being used.

Machine learning is a crucial field in computer science and artificial intelligence that enables computers to learn from data and perform tasks that typically require human intelligence. It is an exciting field with numerous applications in areas such as healthcare, finance, and marketing, and has the potential to revolutionize the way we interact with technology and make predictions about the world around us.

Importance of Machine Learning n today's world

Machine learning has become an increasingly important field in recent years, and its impact is being felt in many different areas of our lives. From healthcare to finance, from retail to entertainment, machine learning is changing the way we interact with technology and make decisions. In this chapter, we will explore the various ways in which machine learning is shaping our world and why it is becoming increasingly important in today's fast-paced and data-driven world.

1.  Healthcare: Machine learning is playing a crucial role