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Über dieses Buch

Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.

You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data.

Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.

What You'll Learn

Understand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNN

Who This Book Is For

Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Step 1: Getting Started in Python 3

Abstract
In this chapter you will get a high-level overview about Python language and its core philosophy, how to set up the Python 3 development environment, and the key concepts around Python programming to get you started with basics. This chapter is an additional step or the prerequisite step for nonPython users. If you are already comfortable with Python, I would recommend you to quickly run through the contents to ensure you are aware of all the key concepts.
Manohar Swamynathan

Chapter 2. Step 2: Introduction to Machine Learning

Abstract
Machine learning (ML) is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence (AI). Let's look at a few other versions of definition that exist for ML:
Manohar Swamynathan

Chapter 3. Step 3: Fundamentals of Machine Learning

Abstract
This chapter focuses on different algorithms of supervised and unsupervised machine learning (ML) using two key Python packages.
Manohar Swamynathan

Chapter 4. Step 4: Model Diagnosis and Tuning

Abstract
In this chapter, we’ll learn about the different pitfalls that one should be aware and will encounter while building a machine learning (ML) system. We’ll also learn industry standard efficient design practices to solve it.
Manohar Swamynathan

Chapter 5. Step 5: Text Mining and Recommender Systems

Abstract
One of the key areas of artificial intelligence is natural language processing (NLP), or text mining as it’s generally known, which deals with teaching computers how to extract meaning from text. Over the last 2 decades, with the explosion of the Internet world and the rise of social media, there is plenty of valuable data being generated in the form of text. The process of unearthing meaningful patterns from text data is called text mining. This chapter covers an overview of the high-level text mining process, key concepts, and common techniques involved.
Manohar Swamynathan

Chapter 6. Step 6: Deep and Reinforcement Learning

Abstract
Deep learning has been the buzzword in the machine learning (ML) world in recent times. The main objective of deep learning algorithms so far has been to use ML to achieve artificial general intelligence (AGI) (i.e., replicate human level intelligence in machines to solve any problems for a given area). Deep learning has shown promising outcomes in computer vision, audio processing, and text mining. The advancements in this area led to breakthroughs such as self-driving cars. In this chapter, you’ll learn about deep leaning’s core concepts, evolution (perceptron to convolutional neural network [CNN]), key applications, and implementation.
Manohar Swamynathan

Chapter 7. Conclusion

Abstract
I hope you have enjoyed the six-step, simplified machine learning (ML) expedition. You started your learning journey with step 1, where you learned the core philosophy and key concepts of Python 3 programming language. In step 2 you learned about ML history, high-level categories (supervised/unsupervised/reinforcement learning), and three important frameworks for building ML systems (SEMMA, CRISP-DM, KDD data mining process), primary data analysis packages (NumPy, Pandas, Matplotlib) and their key concepts, and comparison of different core ML libraries. In step 3 you learned different data types, key data quality issues and how to handle them, exploratory analysis, and core methods of supervised/unsupervised learning and their implementation with an example. In step 4 you learned the various techniques for model diagnosis, bagging for overfitting, boosting for underfitting, ensemble techniques; and hyperparameter tuning (grid/random search) for building efficient models. In step 5 you got an overview of the text mining process: data assembled, data preprocessing, data exploration or visualization, and various models that can be built. You also learned how to build collaborative/content-based recommender systems to personalize user experience. In step 6 you learned about artificial neural networks through perceptron, convolutional neural networks (CNNs) for image analytics, recurrent neural networks (RNNs) for text analytics, and a simple toy example for learning the reinforcement learning concept. These are the advanced topics that have seen great development in the last few years.
Manohar Swamynathan

Backmatter

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