Python machine learning hands on (475 resultados)

Paperback. Condición: Good. BigData and Machine Learning in Python and Spark.

Paperback. Condición: Good. BigData and Machine Learning in Python and Spark.

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Paperback. Condición: Very Good. Unlock the power of artificial intelligence with top Udemy AI instructor Hadelin de Ponteves. Key Features Learn from friendly, plain English explanations and practical activities Put ideas into action with 5 hands-on projects that show step-by-step how to build intelligent software Use AI to win classic video games and construct a virtual self-driving car Book DescriptionWelcome to the Robot World ? and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination.What you will learn Master the basics of AI without any previous experience Build fun projects, including a virtual-self-driving car and a robot warehouse worker Use AI to solve real-world business problems Learn how to code in Python Discover the 5 principles of reinforcement learning Create your own AI toolkit Who this book is forIf you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level).…

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Paperback. Condición: Very Good. A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We?ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You?ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You?ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you?ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is forThis book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It?s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.…

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Paperback or Softback. Condición: New. Python Machine Learning: Learn Python in a Week and Master It. An Hands-On Introduction to Artificial Intelligence Coding, a Project-Based Guid. Book.

Paperback. Condición: Very Good. Unlock the power of artificial intelligence with top Udemy AI instructor Hadelin de Ponteves. Key Features Learn from friendly, plain English explanations and practical activities Put ideas into action with 5 hands-on projects that show step-by-step how to build intelligent software Use AI to win classic video games and construct a virtual self-driving car Book DescriptionWelcome to the Robot World ? and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination.What you will learn Master the basics of AI without any previous experience Build fun projects, including a virtual-self-driving car and a robot warehouse worker Use AI to solve real-world business problems Learn how to code in Python Discover the 5 principles of reinforcement learning Create your own AI toolkit Who this book is forIf you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level).…

Paperback. Condición: Very Good. A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We?ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You?ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You?ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you?ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is forThis book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It?s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.…

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Condición: As New. Unread book in perfect condition.

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Paperback. Condición: Very Good. 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 probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book DescriptionThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You?ll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is forHands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.…

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