tony f. charles has 2 audiobooks on Listento.it, narrated by 1 narrator. The most-rated is Python Machine Learning.
Python for Data Science
Summary
Are you looking for an ultimate python step-by step guide in an efficient way? Do you want to implement a variety of supervised and unsupervised learning algorithms and techniques quickly and accurately? If you cannot wait to explore the fundamental concepts and entire process on python data science, listen to this audiobook! You will start by learning the basics of working with Python and the wide variety of data science packages and extensions. You will be guided on how to setup you work environment before diving into the world of data science. In each section you will learn a great deal of theory backed up by practical examples that contain well-explained Python code. Once you have the fundamentals down, you will get to the core of data science learning algorithms and techniques that are industry-standard in this field. Studying data science and working with supervised and unsupervised algorithms, as well as neural networks, doesn’t have to be as complicated as it sounds. Explore the world of data science using clear, simple, real-world examples and enjoy the power and versatility of Python and machine learning algorithms! You will explore: How to install Python and setup a scientific distribution. The most popular Python packages and library used in data science and machine learning, such as Scikit-learn, Numpy, Matplotlib, and Pandas. Data munging with pandas and how to import and prepare your dataset for preprocessing and exploration. How to further prepare your data for the data science pipeline by fully understanding concepts such as data exploration, dimensionality reduction, and outlier detection. How to implement supervised and unsupervised machine learning algorithms such as regression algorithms, the Naïve Bayes classifier, K-nearest neighbors, support vector machines, decision trees, and K-means clustering. Neural networks and how to work with feedforward and recurrent networks, with a focus on the restricted Boltzmann machine. Big Data and why it is the path for the future in data science. Even if python for data science is a brand new field to you, this audiobook is the key to introduce you into the python world. Python for Data Science can guide you step-by-step through the entire learning process. Get the audiobook now!
©2020 Tony F. Charles (P)2020 Tony F. Charles
Python Machine Learning
Summary
If you need a go-to guide with numerous examples and coded programs, Python Machine Learning will be the answer. If you want to train your own models and enhance them to achieve your demand, Python Machine Learning is the exact tool. Do you want to train the machines and model to carry out complex jobs with accurate date and predictions in a more efficient way? Do you want to have a quick start in the right direction to enter into the Python world? Machine learning is one of the most demanded fields in existence and this book aims to provide you with a reference into the more advanced world of Python and machine learning. The book will provide you with various examples and algorithms to learn and experiment with. With carefully selected topics, this book aims to serve as an expert’s guide into the world of machine learning. Whether you are looking to apply machine learning in financial institutions to check for fraudulent transactions, or use the same to train intelligent models to detect authentic currencies, machine learning is what you seek and this book aims to provide you with a quick start in the right direction. The central idea of this book is to cover relevant examples from real world and provide aspiring learners with a chance to experiment in a controlled environment, where even the worst of errors should pose you no issues. All you need is a good understanding of the Python language, a decent machine and a will to learn. This book will allow you to see how we can use data to create plots and graphs. We will mostly be visiting programs using a few datasets and comparing those using various methods. This would allow every programmer to learn the differences and gain valuable understanding of the accuracy of applied methods and functions. Getting started with machine learning What is machine learning? Installing machine libraries in your system Supervised machine learning for discrete class label Machine learning methods K-nearest neighbors Decision tree Support vector machine Naive Bayes classification Logistic regression Neural network Supervised machine learning for continuous class label Regression models Unsupervised machine learning Understanding and challenges Dimension reduction Clustering models Working with text data Representing text data as bags of words Stopwords Machine learning real world applications Even if you’ve got limited knowledge on numerous examples and coded programs currently, this book gives a proper chance to enrich your horizon on Python machine learning. Scroll up and click buy now!
©2019 Tony F. Charles (P)2019 Tony F. Charles