Machine Learning For Computer Scientists And Data Analysts
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Machine Learning for Computer Scientists and Data Analysts
This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a problem they wish to solve. In addition, this book includes numerous case studies, ranging from simple time-series forecasting to object recognition and recommender systems using massive databases. Lastly, this book also provides practical implementation examples and assignments for the readers to practice and improve their programming capabilities for the ML applications.
Algorithms: Discover The Computer Science and Artificial Intelligence Used to Solve Everyday Human Problems, Optimize Habits, Learn Anything and Organize Your Life
Now, you might look at this title and shy away, thinking that a book with "algorithms" in its title must be just for techies and computer scientists. However, this book is very accessible to those with no background in computer science. In fact it is a must-listen for anyone interested in what our digital future looks like. Today, many decisions that could be made by human beings, from predicting earthquakes to interpreting languages, can now be made by computer algorithms with advanced analytic capabilities. Every day we make millions of decisions, from selecting a life partner, to organizing your closet, to scheduling your life, to having a conversation. However, these decisions may be imperfect due to limited experience, implicit biases, or faulty probabilistic reasoning. Algorithms can better predict human behavior than trained psychologists and with much simpler criteria. Studies continue to show that the algorithms can do a better job than experts in a range of fields. Everywhere you look, artificial intelligence is beginning to permeate all types of industries, and expectations are that it will continue to grow in the future. Imagine the possibilities: More accurate medical diagnoses. Better military strategies that could save lives. Detect abnormal genes in an unborn child. Predict changes in weather and earthquake. Safer self-driving cars that have learned your personal preferences. Analyze DNA samples and identify potential medical risks. Smart homes that will anticipate your every needs. Predicting where cyber hackers and online threats may occur. Artificial intelligence is reshaping health care, science, engineering, and life. The results will make our lives more productive, better organized, and essentially much happier. Get started Now!
Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease
Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.