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Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras

Key Features

  • Implement machine learning algorithms to build, train, and validate algorithmic models
  • Create your own algorithmic design process to apply...
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

  • Explore statistics and complex mathematics for data-intensive applications
  • Discover new developments in EM algorithm, PCA, and bayesian...
Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud
Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud

Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book?Amazon, Microsoft, Google, and PythonAnywhere.

You will work through a series of common Python data science problems...

Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models
Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models

Explore and master the most important algorithms for solving complex machine learning problems.

Key Features

  • Discover high-performing machine learning algorithms and understand how they work in depth
  • One-stop solution to mastering supervised, unsupervised, and...
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn

Key Features

  • Build a variety of Hidden Markov Models (HMM)
  • Create and apply models to any sequence of data to analyze, predict, and extract valuable...
Machine Learning for Finance: Principles and practice for financial insiders
Machine Learning for Finance: Principles and practice for financial insiders

A guide to advances in machine learning for financial professionals, with working Python code

Key Features

  • Explore advances in machine learning and how to put them to work in financial industries
  • Clear explanation and expert discussion of how machine learning works, with...
Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 2, Inference
Introduction to Probability and Statistics from a Bayesian Viewpoint, Part 2, Inference

The two parts of this book treat probability and statistics as mathematical disciplines and with the same degree of rigour as is adopted for other branches of applied mathematics at the level of a British honours degree. They contain the minimum information about these subjects that any honours graduate in mathematics ought to know. They are...

Data Mining: Concepts, Models, Methods, and Algorithms, Second Edition
Data Mining: Concepts, Models, Methods, and Algorithms, Second Edition
Now updated—the systematic introductory guide to modern analysis of large data sets

As data sets continue to grow in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more complex and sophisticated software tools. This book...

Data Mining and Statistics for Decision Making
Data Mining and Statistics for Decision Making
Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify...
Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and...

Uncertainty and Intelligent Information Systems
Uncertainty and Intelligent Information Systems
Intelligent systems are necessary to handle modern computer-based technologies managing information and knowledge. This book discusses the theories required to help provide solutions to difficult problems in the construction of intelligent systems. Particular attention is paid to situations in which the available information and data may be...
Building Probabilistic Graphical Models with Python
Building Probabilistic Graphical Models with Python

Solve machine learning problems using probabilistic graphical models implemented in Python with real-world applications

About This Book

  • Stretch the limits of machine learning by learning how graphical models provide an insight on particular problems, especially in high dimension areas such as image...
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