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Learn Python machine learning with these essential books and online courses

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Teaching yourself Python machine learning can be a daunting task if you don't know where to start. Fortunately, there are plenty of good introductory books and online courses that teach you the basics. It is the advanced books, however, that teach you the skills you need to decide which algorithm better solves a problem and which direction to take when tuning hyperparameters. A while ago, I was introduced to Machine Learning Algorithms, Second Edition by Giuseppe Bonaccorso, a book that almost falls into the latter category. While the title sounds like another introductory book on machine learning algorithms, the content is anything but.


The Complete Neural Networks Bootcamp: Theory, Applications

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Online Courses Udemy - The Complete Neural Networks Bootcamp: Theory, Applications, Master Deep Learning and Neural Networks Theory and Applications with Python and PyTorch! Including NLP and Transformers Created by Fawaz Sammani | English [Auto] Preview this course GET COUPON CODE Free Coupon Discount Udemy Courses


IBM Machine Learning

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Offered by IBM. Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis. This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning . You will follow along and code your own projects using some of the most relevant open source frameworks and libraries. Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning.


Data Science Course 2021: Complete Machine Learning Training

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Created by Data-Driven Science Preview this Udemy Course - GET COUPON CODE " We will shift from a mobile first to an AI first world." AI will transform every industry similar to electricity over 100 years ago and have a huge impact on how humans live and work in the future. Moving into Data Science is an amazing career choice. There's high demand for Data Scientists across the globe and people working in the field enjoy high salaries and rewarding careers. For instance, average annual salaries are around $125,000 in America and ₹14 lacs in India.


Top Machine Learning Courses Online - Updated [October 2020]

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Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It's useful to have a development environment such as Python so that you don't need to compile and package code before running it each time.


t-arf/Machine-Learning-Scholarship-Udacity-Notes-and-Quizzes

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Educational Advances in Artificial Intelligence

AI Magazine

The emergence of massive open online courses has initiated a broad national-wide discussion on higher education practices, models, and pedagogy. Artificial intelligence and machine learning courses were at the forefront of this trend and are also being used to serve personalized, managed content in the back-end systems. Massive open online courses are just one example of the sorts of pedagogical innovations being developed to better teach AI. This column will discuss and share innovative educational approaches that teach or leverage AI and its many subfields, including robotics, machine learning, natural language processing, computer vision, and others at all levels of education (K-12, undergraduate, and graduate levels).


Python Numpy: Machine Learning & Data Science Course

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Python Numpy: Machine Learning & Data Science Course Learn Numpy and get comfortable with Python Numpy in order to start into Data Science and Machine Learning.New In both cases, you are at the right place! The number of companies and enterprises using Python is increasing day by day. The world we are in is experiencing the age of informatics. Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover, Numpy forms the foundation of the Machine Learning stack.


AI-Powered Education for a Better Tomorrow

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There was a time when Artificial Intelligence (AI) was often portrayed as robots. Machines that exhibited human-like characteristics (learning and decision making) with an artificial brain. Today, AI encompasses anything and everything. Be it vehicles, entertainment, corporations, smart homes, google search algorithms, education, law, or medical services, AI has transformed all the sectors for the better. Until today, artificial intelligence has outperformed humans in specific tasks. The worldwide AI market is expected to grow by $120 billion by the end of 2025.


Importance of Bias in Deep Learning

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Hi! I am working on a CNN project and I have some confusions. I didn't understand the importance of using bias during training a model. What are the effects of bias usage? And if we don't use bias what are the challenges that we are going to face with?