| Machine Learning With Python: 3 Books in 1 - The Ultimate Beginners Guide & a Comprehensive Guide of Tips and Tricks & Advanced and Effective Strate Contributor(s): Williams, Ethan (Author) |
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ISBN: ISBN-13: 9798649011815 Publisher: Independently Published OUR PRICE: $41.79 Product Type: Paperback - Other Formats Published: May 2020 |
| Additional Information |
| BISAC Categories: - Computers | Natural Language Processing - Computers | Machine Theory |
| Physical Information: 1.08" H x 5.98" W x 9.02" (1.56 lbs) 534 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Introduction 1MACHINE LEARNING WITH PYTHON - The Ultimate Beginners Guide to Learn Machine Learning with Python Step by StepWe live in a world of data deluge where gigabytes of data are generated daily. It is possible that this data might not be very useful for our daily applications. Major setbacks in the use of such data may be due to the presence of loopholes in data links previously generated or the data might be too vast for the limited human mind. Machine learning in this book presents some of the solutions to the problems above. Being an introductory guide, expect to learn the various basics involved in Machine Learning and Python. This book provides an insight into the new world of big data, then behooves you to learn more about Machine Learning. You will be able to get answers to the following questions: -What is Machine Learning and what does it entail? -How can I apply machine learning to have a glimpse into the new world, power my enterprise or find out how the Internet thinks about my academic research work? Be ready to learn all that it takes to be an expert in the field of Machine Learning Introduction 2MACHINE LEARNING WITH PYTHON - Comprehensive Guide of Tips and Tricks of using Machine Learning Theories with PythonMachine learning is a branch of artificial intelligence that designs algorithms that improve their performance based on empirical data. Machine learning is one of the most active and exciting fields of computer science today, mainly because of its many application options ranging from pattern recognition and in-depth data analysis to robotics, computational vision, bioinformatics, and computational linguistics. Machine learning is above all a discipline that can contribute to many domains and has very challenging applications. This is the area where most publications in academia are concerned with artificial intelligence, and all major companies, such as Google, Facebook or Microsoft, apply machine learning methods in their applications. This book covers the theory, principles and tricks to machine learning and provides an overview of its applications in Python.In the field of data science, it comes quite natural that you should learn Python. If you're wondering why Python is the answer, the answer is that there are already ready packages (statistical and numerical) for analyzing data such as PyBrain, NumPy, and MySQL. Machine learning integrates computers and statistics that allow computers to learn new tasks. There are Python modules - such as Scikit-learn, Tensorflow, and Theano - that support machine learning so that you can do cool things such as spam detection and fingerprint identification. So these are some of the concepts that you will master reading this book.Introduction 3MACHINE LEARNING WITH PYTHON - Advanced and Effective Strategies Using Machine Learning with Python TheoriesAre you eager to use advanced Machine Learning methods with Python? Are you looking forward to automating simple things using the power of the keyboard, but you have no idea how to achieve it?Machine learning is a vast field and expanding at supersonic speed. Python evolution is an ongoing process and lives up to the hype. The field goes beyond robotics and data finance to finance applications.When you use machine learning and python programming in the right way, they have the capability of changing the lives of people around the world. In this advanced book, we are going to break down the advanced features of this new technology to advance your skills as an IT enthusiast. You will discover: -How we classify machine learning algorithms-How we can apply machine learning in different areas-Understanding the artificial neural networks-The use of convoluted neural networks-Building predictive models-Autoencoders in ML and Python-K-Means techniques and Natural Language Processing-The art of feature engineering-The ensemble methods |