“…This video course focuses on Machine Learning and covers a range of analysis tools, such as NumPy, Matplotlib, and Pandas. It is packed full of hands-on code simulating many of the problems and providing working solutions. …”

“…Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features Explore the ins and outs of genetic algorithms with this fast-paced guide Implement tasks such as feature selection, search optimization, and cluster analysis using Python Solve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applications Book Description Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. …”

“…Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book Learn the basics of Python in the Jupyter Notebook Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel Who This Book Is For This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. …”

“…As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. …”

“…After that, you'll learn more techniques and best practices/rules of how to take your Python implementations and develop GUIs for your YOLOv4 apps using PyQT. Then, you'll be labeling your own dataset from scratch, converting standard datasets into YOLOv4 format, amplifying your dataset 10x, and employing data augmentation to significantly increase the diversity of available data for training models, without collecting new data. …”

“…Build your own real-time stream processing applications using Apache Spark 3.x and PySpark About This Video Learn real-time stream processing concepts Understand Spark structured streaming APIs and architecture Work with file streams, Kafka source, and integrating Spark with Kafka In Detail Take your first steps towards discovering, learning, and using Apache Spark 3.0. …”

“…Sie werden Schritt f|r Schritt in die Programmiertechniken eingef|hrt: von den Grundlagen (Datentypen, Kontrollstrukturen, Funktionen) |ber die Entwicklung komplexer Anwendungen mit grafischer Benutzungsoberfl̃che bis hin zur Anbindung an SQL-Datenbanken, Datenvisualisierung und wissenschaftlichem Rechnen mit Arrays (NumPy). Dabei lernen Sie auch, gut lesbare Programmtexte und sauberen Code zu schreiben, und erfahren, wie Sie Fehler finden und von Anfang an vermeiden k̲nnen. …”

“…If you're familiar with pandas and NumPy, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data. …”

“…Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key Features Get to grips with the different reinforcement and DRL algorithms for game development Learn how to implement components such as artificial agents, map and level generation, and audio generation Gain insights into cutting-edge RL research and understand how it is similar to artificial general research Book Description With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. …”

“…Przedstawiono tu możliwości oferowane przez Pythona 3.6 oraz najnowsze funkcje pakietów Pandas i NumPy, a także środowisk IPython i Jupyter. Przy opisie poszczególnych narzędzi analitycznych wyjaśniono ich działanie i zaprezentowano przykłady ich wykorzystania w sposób efektywny i kreatywny. …”

“…Learn how Ray builds on familiar language concepts: functions for stateless tasks and classes for stateful computing Use Ray libraries for reinforcement learning, hyperparameter tuning, distributed training of TensorFlow and PyTorch models, and model serving Work with Ray for general application development, including conventional microservices and serverless applications Get hands-on instruction using live Ray code with Dean Wampler’s Meet the Expert session on O’Reilly online learning…”

“…Along the way you’ll learn how to: •Use libraries like matplotlib, NumPy, Bokeh, pandas, Requests, Beautiful Soup, and turtle •Work with Natural Language Processing and computer vision modules like NLTK and OpenCV •Write a program to detect and track objects moving across a starfield •Scrape speeches from the internet and autosummarize them •Use the Mars Orbiter Laser Altimeter (MOLA) map to select spacecraft landing sites •Survive a zombie apocalypse with the aid of data-plotting and visualization tools The book’s programs are beginner-friendly, but as you progress you’ll learn more sophisticated techniques to help you grow your coding capabilities. …”

“…Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. …”

“…Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Key Features Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python Master the art of data-driven problem-solving with hands-on examples Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms Book Description Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. …”

“…The learning objective for viewers is to understand how to locate, manipulate, and analyse data with Python, with the ability to analyse large and small sets of data using libraries such as Numpy, pandas, IPython and SciPy. This is a two part series. The first series is focused on getting and manipulation sizeable amounts of data using modern techniques. …”

“…Von den Grundlagen Neuronaler Netze über Machine Learning bis hin zu Deep-Learning-Algorithmen Anschauliche Diagramme, Anwendungsbeispiele in Python und der Einsatz von NumPy Keine Vorkenntnisse in Machine Learning oder höherer Mathematik erforderlich Deep Learning muss nicht kompliziert sein. …”

“…The scikit-learn library is built on the foundations of the numeric Python stack. It uses NumPy for its fundamental data structures and optimized performance, and it plays well with pandas and matplotlib. …”

“…You’ll also explore the web3.py library. As you progress, you’ll learn how to connect to smart contracts, read values, and create transactions. …”

“…This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. …”

“…We will start by installing Python and PyCharm on our systems followed by understanding the basics of Python. …”