Statistics Crash Course for Beginners
Machine Learning (ML) is the lifeblood of businesses worldwide. ML tools empower organizations to identify profitable opportunities fast and help them to understand potential risks better. The ever-expanding data, cost-effective data storage, and competitively priced powerful processing continue to drive the growth of ML.
This is the best time you could enter the exciting machine learning universe. Industries are reinventing themselves constantly by developing more advanced data analysis models. These models analyze larger and more complex data than ever while delivering instantaneous and more accurate results on enormous scales.
In this backdrop, it is evident that hands-on practice is everything in machine learning. Tons of theory will amount to nothing if you don’t have enough hands-on practice. Textbooks and online classes mislead you into a false sense of mastery. The easy availability of learning resources tricks you, and you become overconfident. But when you try to apply the theoretical concepts you learned, you realize it’s not that simple.
This is where projects play a crucial role in your learning journey. Projects are doubtless the best investment of your time. You’ll not only enjoy learning, but you’ll also make quick progress. And unlike studying boring theoretical concepts, you’ll find that working on projects is easier to stay motivated.
The 10 projects in this book cover 10 different interesting topics. Each project will help you refine your ML skills and apply them in the real world. These projects also present you with an opportunity to enrich your portfolio, making it simpler to find a great job, explore interesting career paths, and even negotiate a higher pay package. Overall, this learning by doing book will help you accomplish your machine learning career goals faster.
How Is This Book Different?
This book presents you with a hands-on experience in ML. It is divided into two sections and follows a very simple approach.
The first section consists of two chapters. Chapter 1 provides a roadmap for step by step learning approach to data science and machine learning. The process for environment setup, including the software needed to run scripts in this book, is also explained in this chapter. Chapter 2 contains a crash course on Python for beginners.
The second section consists of 10 compelling machine learning and data science-based projects. In each project, a brief explanation of the theoretical concepts is given, followed by practical examples. The Python notebook for each project is provided in the Source Codes folder in the GitHub and SharePoint repositories.
The datasets used in this book are easily accessible. You can download them at runtime. Alternatively, you can access them via the Datasets folder in the GitHub and SharePoint repositories.
The projects covered include:
- House Price Prediction Using Linear Regression
- Filtering Spam Email Messages Using Naïve Bayes Algorithm
- Predicting Used Car Sale Price Using Feedforward Artificial Neural Networks
- Predicting Stock Market Trends with RNN (LSTM)
- Language Translation using Seq2Seq Encoder-Decoder LSTM
- Classifying Cats and Dogs Images Using Convolutional Neural Networks
- Movie Recommender System Using Item-Based Collaborative Filtering
- Face Detection with OpenCV in Python
- Handwritten English Character Recognition with CNN
- Customer Segmentation Based on Income and Spending
The scripts, images, and graphs are clear and provide visuals to the text description. If you’re new to ML and self-study is your only option, then this book is a must.
There are no reviews yet.