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Über dieses Buch

Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources.
Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs.
By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The book is equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems.What You Will Learn
Gain insights into machine learning concepts
Work on real-world applications of machine learningLearn concepts of model selection and optimizationGet a hands-on overview of Python from a machine learning point of view

Who This Book Is For
Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.



Chapter 1. Statistics and Probability

The purpose of this chapter is to instill in you the basic concepts of traditional statistics and probability. Certainly many of you might be wondering what it has to do with machine learning. Well, in order to apply a best fit model to your data, the most important prerequisite is for you to understand the data in the first place. This will enable you to find out distributions within data, measure the goodness of data, and run some basic tests to understand if some form of relationship exists between dependant and independent variables. Let’s dive in.

Danish Haroon

Chapter 2. Regression

Regression and time series analysis make predictions for quantitative target variables possible. This chapter aims to highlight the core concept of regression and its variants. The emphasis here is to take the readers through the journey of model selection when solving for real-world problems. Moreover, this chapter also features statistical tests to evaluate the findings of these regression techniques.

Danish Haroon

Chapter 3. Time Series

The goal of this chapter is to get you started with time series forecasting. A time series forecast is different from regression in that time acts as an exploratory variable and should be continuous along equal intervals. The chapter will cover the concept of stationary, its importance, and methodologies to check its existence in a time series object. Several time series models will be applied, and their forecasts will be checked using the most effective evaluation techniques.

Danish Haroon

Chapter 4. Clustering

The goal of this chapter is to solve real-world problems with the aid of supervised and unsupervised learning algorithms. First we will start off with the concept of clustering, determine how to organize the data, decide upon the number of components, and then end up seeing if the cluster outputs make any intuitive sense.

Danish Haroon

Chapter 5. Classification

This chapter aims to solve real-world problems that depend on a finite number of outcomes. These outcomes can be Boolean in nature, with only two choices (i.e., True/False or Yes/No). They can also be nominal in nature (boat name on which a passenger will embark, maximum education attainment a group of students will have, etc.). Throughout this chapter we will be talking about supervised learning whereby labeled data will be used to train the model. Training the models on this data will enable label predictions on an unseen dataset (i.e., future predictions). In this chapter we will be talking about feature extraction and selection and will conclude with methods to evaluate classification accuracy.

Danish Haroon


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