Jason Brownlee

The Python ecosystem with scikit-learn and pandas is required for operational machine learning. Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it directly to production. In this Ebook, learn exactly how to get started and apply machine learning using the Python ecosystem.

Understand Your Data, Create Accurate Models, and Work Projects End-to-End

Machine Learning Mastery With Python

"For fifteen years the military regime that took power in Egypt in 1952 enjoyed a contentious but respectful bilateral relationship with the United States. After Israel devastated the Egyptian military in the 1967 War, however, Cairo severed diplomatic ties with Washington. , dipYears later, compatible strategic aims brought the two governments back together. While Anwar Sadat strove to restore Egypt's territory and solvency, the White House sought to reduce Soviet influence in the Middle East. A US-Egyptian alliance served both parties, but it took a daring military assault by Sadat to impress the wisdom of the friendship upon the Nixon administration. What followed was one of the most tectonic shifts of the Cold War: the complete return of the Sinai Peninsula to Egypt; a lasting peace between Israel and Egypt, Israel's most formidable regional adversary; and a strategic pact between the United States and Egypt, previously a key client of the Soviet Union. After the Iranian Revolution, Egypt became a component of America's new strategy for preserving its influence over the Persian Gulf"--

the politics of the U.S.-Egyptian alliance

Democracy prevention

You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.

Discover How They Work and Implement Them From Scratch

Master Machine Learning Algorithms

This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete, consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described in this book were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language.

Nature-inspired Programming Recipes

Clever Algorithms

Far from sweeping the globe uniformly, the 'third wave of democratization' left burgeoning republics and resilient dictatorships in its wake. Applying more than a year of original fieldwork in Egypt, Iran, Malaysia, and the Philippines, in this 2007 book Jason Brownlee shows that the mixed record of recent democratization is best deciphered through a historical and institutional approach to authoritarian rule. Exposing the internal organizations that structure elite conflict, Brownlee demonstrates why the critical soft-liners needed for democratic transitions have been dormant in Egypt and Malaysia but outspoken in Iran and the Philippines. By establishing how ruling parties originated and why they impede change, Brownlee illuminates the problem of contemporary authoritarianism and informs the promotion of durable democracy.

Authoritarianism in an Age of Democratization

Andrew Reynolds

Tarek Masoud

Several years after the Arab Spring began, democracy remains elusive in the Middle East. The Arab Spring that resides in the popular imagination is one in which a wave of mass mobilization swept the broader Middle East, toppled dictators, and cleared the way for democracy. The reality is that few Arab countries have experienced anything of the sort. While Tunisia made progress towards some type of constitutionally entrenched participatory rule, the other countries that overthrew their rulersEgypt, Yemen, and Libyaremain mired in authoritarianism and instability. Elsewhere in the Arab world uprisings were suppressed, subsided or never materialized. The Arab Springs modest harvest cries out for explanation. Why did regime change take place in only four Arab countries and why has democratic change proved so elusive in the countries that made attempts? This book attempts to answer those questions. First, by accounting for the full range of variance: from the absence or failure of uprisings in such places as Algeria and Saudi Arabia at one end to Tunisias rocky but hopeful transition at the other. Second, by examining the deep historical and structure variables that determined the balance of power between incumbents and opposition. Brownlee, Masoud, and Reynolds find that the success of domestic uprisings depended on the absence of a hereditary executive and a dearth of oil rents. Structural factors also cast a shadow over the transition process. Even when opposition forces toppled dictators, prior levels of socioeconomic development and state strength shaped whether nascent democracy, resurgent authoritarianism, or unbridled civil war would follow.

Pathways of Repression and Reform

The Arab Spring

Adrian Tam

Matthew Mayo

Abid Ali Awan

Kanwal Mehreen

ChatGPT is one of the leading models in the AI language model arena and is widely used in various fields. With ChatGPT, you can effortlessly harness the power of AI to improve your efficiency with just a few well-crafted prompts. Many productivity-boosting tasks are facilitated by ChatGPT, so understanding how to interact with it paves the way for you to leverage the power of advanced AI. This ebook is written in the engaging and approachable style that you’re familiar with from the Machine Learning Mastery series. Discover exactly how to get started and apply ChatGPT to your own productivity, learning, or creativity projects.

Let Generative AI Help You Work Smarter

Maximizing Productivity with ChatGPT

Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.

Discover How To Harness Uncertainty With Python

Probability for Machine Learning

Optimization happens everywhere. Machine learning is one example of such and gradient descent is probably the most famous algorithm for performing optimization. Optimization means to find the best value of some function or model. That can be the maximum or the minimum according to some metric. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will learn how to find the optimum point to numerical functions confidently using modern optimization algorithms.

Optimization for Machine Learning

Stefania Cristina

Mehreen Saeed

Calculus seems to be obscure, but it is everywhere. In machine learning, while we rarely write code on differentiation or integration, the algorithms we use have theoretical roots in calculus. If you ever wondered how to understand the calculus part when you listen to people explaining the theory behind a machine learning algorithm, this new Ebook, in the friendly Machine Learning Mastery style that you’re used to, is all you need. Using clear explanations and step-by-step tutorial lessons, you will understand the concept of calculus, how it is relates to machine learning, what it can help us on, and much more.

Calculus for Machine Learning

Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms.

Make Better Predictions with Bagging, Boosting, and Stacking

Ensemble Learning Algorithms With Python

Zhe Ming Chng

Daniel Chung

Using clear explanations and step-by-step tutorial lessons, you will learn the underlying mechanics of the Python language, the tools in its ecosystem, tips and tricks, and much more.

Learn Python from Machine Learning Projects

Python for Machine Learning

The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. In this laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about LSTMs. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what LSTMs are, and how to develop a suite of LSTM models to get the most out of the method on your sequence prediction problems.

Develop Sequence Prediction Models with Deep Learning

Long Short-Term Memory Networks With Python

Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

How to Prepare Data and Develop Models to Predict the Future

Introduction to Time Series Forecasting With Python

Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.

Develop Deep Learning Models for your Natural Language Problems

Deep Learning for Natural Language Processing

You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. No longer. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.

Machine Learning Algorithms From Scratch with Python

Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

Image Classification, Object Detection, and Face Recognition in Python

Deep Learning for Computer Vision

Statistics is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in statistics that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, and much more.

Jason Brownlee

Books

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