Tabla de Contenidos: “…-- Getting started -- Standard data types -- Strings and Unicode -- Integers and floats -- Booleans and None -- Structured data types -- Lists -- Dictionaries -- Sets and tuples -- Data type conversions -- Files -- Variables -- Understanding scripting flow logic -- Conditionals -- Loops -- For -- While -- Functions -- Summary -- Chapter 2: Python Fundamentals -- Advanced data types and functions -- Iterators -- Datetime objects -- Libraries -- Installing third-party libraries -- Libraries in this book -- Python packages -- Classes and object-oriented programming -- Try and except -- Raise -- Creating our first script - unix_converter.py -- User input -- Using the raw input method and the system module - user_input.py -- Understanding Argparse - argument_parser.py -- Forensic scripting best practices -- Developing our first forensic script - usb_lookup.py -- Understanding the main() function -- Exploring the getRecord() function -- Interpreting the searchKey() function -- Running our first forensic script -- Troubleshooting -- Challenge -- Summary -- Chapter 3: Parsing Text Files -- Setup API -- Introducing our script -- Overview -- Our first iteration - setupapi_parser.v1.py -- Designing the main() function -- Crafting the parseSetupapi() function -- Developing the printOutput() function -- Running the script -- Our second iteration - setupapi_parser.v2.py -- Improving the main() function -- Tuning the parseSetupapi() function -- Modifying the printOutput() function -- Running the script -- Our final iteration - setupapi_parser.py -- Extending the main() function -- Adding to the parseSetupapi() function…”

Tabla de Contenidos: “…Getting started in IPython and Jupyter -- Enhanced interactive features -- Debugging and profiling -- Part II: Introduction to NumPy -- Understanding data types in Python -- The basics of NumPy arrays -- Computation on NumPy arrays: universal functions -- Aggregations: min, max, and everything in between -- Computation on arrays: broadcasting -- Comparisons, masks, and Boolean logic -- Fancy indexing -- Sorting arrays -- Structured data: NumPy's structured arrays -- Part III: Data manipulation with Pandas. …”

Tabla de Contenidos: “…Preliminaries -- Introductory examples -- IPython : an interactive computing and development environment -- NumPy basics : arrays and vectorized computation -- Getting started with pandas -- Data loading, storage, and file formats -- Data wrangling : clean, transform, merge, reshape -- Plotting and visualization -- Data aggregation and group operations -- Time series -- Financial and economic data applications -- Advancded NumPy…”

Tabla de Contenidos: “…Chapter 1: Data Visualization with Leather -- Chapter 2: Introduction to the Scientific Python Ecosystem and NumPy -- Chapter 3: NumPy Routines and Visualization with Matplotlib -- Chapter 4: Visualizing images and 3D Shapes -- Chapter 5 :Visualize Networks and Graphs -- Chapter 6: Getting Started with Pandas -- Chapter 7: Processing and Visualizing COVID-19 Data -- Appendix…”

“…Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key Features Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries Book Description Feature engineering is invaluable for developing and enriching your machine learning models. …”

“…Explore the PyO3 library and its usage. Understand the Rust ownership model and its usage in PyO3. …”

Tabla de Contenidos: “…Table of Contents Giving Computers the Ability to Learn from Data Training Simple Machine Learning Algorithms for Classification A Tour of Machine Learning Classifiers Using Scikit-Learn Building Good Training Datasets - Data Preprocessing Compressing Data via Dimensionality Reduction Learning Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying Machine Learning to Sentiment Analysis Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data - Clustering Analysis Implementing a Multilayer Artificial Neural Network from Scratch Parallelizing Neural Network Training with PyTorch Going Deeper - The Mechanics of PyTorch Classifying Images with Deep Convolutional Neural Networks Modeling Sequential Data Using Recurrent Neural Networks Transformers - Improving Natural Language Processing with Attention Mechanisms Generative Adversarial Networks for Synthesizing New Data Graph Neural Networks for Capturing Dependencies in Graph Structured Data Reinforcement Learning for Decision Making in Complex Environments…”

Tabla de Contenidos: “…Getting started in IPython and Jupyter ; Enhanced interactive features ; Debugging and profiling -- Part II: Introduction to NumPy. Understanding data types in Python ; The basics of NumPy arrays ; Computation on NumPy arrays : universal functions ; Aggregations : min, max, and everything in between ; Computation on arrays : broadcasting ; Comparisons, masks, and Boolean logic ; Fancy indexing ; Sorting arrays ; Structured data : NumPy's structured arrays -- Part III: Data manipulation with Pandas. …”

“…Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key Features Use powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial data Explore unique recipes for financial data analysis and processing with Python Estimate popular financial models such as CAPM and GARCH using a problem-solution approach Book Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. …”

Tabla de Contenidos: “…-- Step-by-step installation -- Installing the necessary packages -- Package upgrades -- Scientific distributions -- Anaconda -- Leveraging conda to install packages -- Enthought Canopy -- WinPython -- Explaining virtual environments -- Conda for managing environments -- A glance at the essential packages -- NumPy -- SciPy -- pandas -- pandas-profiling -- Scikit-learn -- Jupyter -- JupyterLab -- Matplotlib -- Seaborn -- Statsmodels -- Beautiful Soup -- NetworkX -- NLTK -- Gensim -- PyPy -- XGBoost -- LightGBM -- CatBoost -- TensorFlow -- Keras -- Introducing Jupyter -- Fast installation and first test usage -- Jupyter magic commands -- Installing packages directly from Jupyter Notebooks -- Checking the new JupyterLab environment -- How Jupyter Notebooks can help data scientists -- Alternatives to Jupyter -- Datasets and code used in this book -- Scikit-learn toy datasets -- The MLdata.org and other public repositories for open source data -- LIBSVM data examples -- Loading data directly from CSV or text files -- Scikit-learn sample generators -- Summary -- Chapter 2: Data Munging -- The data science process -- Data loading and preprocessing with pandas -- Fast and easy data loading -- Dealing with problematic data -- Dealing with big datasets -- Accessing other data formats -- Putting data together -- Data preprocessing -- Data selection -- Working with categorical and textual data -- A special type of data - text -- Scraping the web with Beautiful Soup -- Data processing with NumPy -- NumPy's n-dimensional array -- The basics of NumPy ndarray objects -- Creating NumPy arrays -- From lists to unidimensional arrays…”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Introduction to Python 3 -- Introducing the Python 3 Programming Language -- History of the Python Programming Language -- Python Enhancement Proposals -- Philosophy of the Python Programming Language -- Applications of Python -- Installing Python on Various Platforms -- Installing on a Windows Computer -- Installing on Ubuntu/Debian Derivatives -- Using Python Modes -- Interactive Mode -- Script Mode -- Using Python IDEs -- Exploring the Scientific Python Ecosystem -- Introducing Jupyter Notebook -- Setting Up Jupyter Notebook -- Running Code in Jupyter Notebook -- Anaconda -- Summary -- Chapter 2: Getting Started with NumPy -- NumPy and Ndarrays -- Indexing in Ndarrays -- Indexing in Ndarrays of More Than One Dimension -- Ndarray Properties -- NumPy Constants -- Slicing Ndarrays -- Summary -- Chapter 3: NumPy Routines and Getting Started with Matplotlib -- Routines for Creating Ndarrays -- Matplotlib -- Visualization with NumPy and Matplotlib -- Running the Matplotlib Program as a Script -- Summary -- Chapter 4: Revisiting Matplotlib Visualizations -- Single-Line Plots -- Multiline Plots -- Grid, Axes, and Labels -- Colors, Styles, and Markers -- Object-Oriented Plotting -- Subplots -- Summary -- Chapter 5: Styles and Layouts -- Styles -- Layouts -- Summary -- Chapter 6: Lines, Bars, and Scatter Plots -- Lines and Logs -- Error Bars -- Bar Graphs -- Scatter Plot -- Summary -- Chapter 7: Histograms, Contours, and Stream Plots -- Histograms -- Contours -- Visualizing Vectors with Stream Plots -- Summary -- Chapter 8: Image and Audio Visualization -- Visualizing Images -- Image Masking -- Interpolation Methods -- Audio Visualization -- Audio Processing -- Summary -- Chapter 9: Pie and Polar Charts -- Pie Charts…”

Tabla de Contenidos: “…Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch…”

Tabla de Contenidos: “…Python Machine Learning by Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn…”

Tabla de Contenidos: “…""Tuples as Immutable Lists""""Slicing""; ""Why Slices and Range Exclude the Last Item""; ""Slice Objects""; ""Multidimensional Slicing and Ellipsis""; ""Assigning to Slices""; ""Using + and * with Sequences""; ""Building Lists of Lists""; ""Augmented Assignment with Sequences""; ""A += Assignment Puzzler""; ""list.sort and the sorted Built-In Function""; ""Managing Ordered Sequences with bisect""; ""Searching with bisect""; ""Inserting with bisect.insort""; ""When a List Is Not the Answer""; ""Arrays""; ""Memory Views""; ""NumPy and SciPy""; ""Deques and Other Queues""; ""Chapter Summary""…”

Tabla de Contenidos: “…""The MapReduce operation with PyCuda""…”

“…This programming language is known for its simplicity and beauty, as well as its large ecosystem of domain-specific tools such as NumPy, SciPy, and Pandas. If you’re looking for a brief but comprehensive tour of Python and its capabilities, this article will help…”

“…Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. …”

“…Leverage the power of Python to clean, scrape, analyze, and visualize your data About This Book Clean, format, and explore your data using the popular Python libraries and get valuable insights from it Analyze big data sets; create attractive visualizations; manipulate and process various data types using NumPy, SciPy, and matplotlib; and more Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data Who This Book Is For This course is for developers, analysts, and data scientists who want to learn data analysis from scratch. …”