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Top Artificial Intelligence Books to Read in 2021

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A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work. He argues persuasively that emotions, intuitions, and feelings are not distinct things, but different ways of thinking. Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades.


Andrew Ng Launches A Campaign For Data-Centric AI

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Data is eating the world so Andrew Ng wants to make sure we radically improve its quality. "Data is food for AI," says Ng, and he is launching a campaign to shift the focus of AI practitioners from model/algorithm development to the quality of the data they use to train the models. Landing AI, the startup Ng founded to bring AI to traditional industries, today announced a competition to get the best performance out of a fixed model by improving the quality of the data. The top three winners will be invited to a private roundtable event with Andrew Ng to share ideas and explore how to grow the data-centric movement. In addition, DeepLearning.AI, an education startup Ng also founded, is launching an online course to teach his data-centric approach to a worldwide audience on Coursera (which Ng co-founded in 2012).


Complete Machine Learning & Data Science with Python

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Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, my course on Udemy here to help you apply machine learning to your work. Welcome to the "Complete Machine Learning & Data Science with Python A-Z" course. Do you know data science needs will create 11.5 million job openings by 2026? Do you know the average salary is $100.000 for data science careers!


Top 10 Data Science Projects for Beginners - KDnuggets

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As an aspiring data scientist, you must have heard the advice "do data science projects" over a thousand times. Not only are data science projects a great learning experience, they also help you stand out from the crowd of data science enthusiasts looking to break into the field. In this article, I am going to walk you through the projects that are must-haves on your resume. I will also provide you with sample datasets to experiment with for each project, along with associated tutorials that will help you complete the project. Data collection and pre-processing is one of the most important skills to have as a data scientist.


Deep learning: A Natural Language Processing Bootcamp

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You will also learn how to use TensorFlow For NLP and Deep Learning. By end of this course, you will learn how to build a Sentiment Classifier and a program that can write like a real poet! This course is very hands-on and you will be learning everything there is about basic NLP. For those of you who like to dig deep into the theory to understand how things really work, you know this is my specialty and there will be no shortage of that in this course. We'll be covering the state of the art algorithms like word embeddings, tokenization, and deep learning.


Explainable Machine Learning with LIME and H2O in R

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Welcome to this hands-on, guided introduction to Explainable Machine Learning with LIME and H2O in R. By the end of this project, you will be able to use the LIME and H2O packages in R for automatic and interpretable machine learning, build classification models quickly with H2O AutoML and explain and interpret model predictions using LIME. Machine learning (ML) models such as Random Forests, Gradient Boosted Machines, Neural Networks, Stacked Ensembles, etc., are often considered black boxes. However, they are more accurate for predicting non-linear phenomena due to their flexibility. Experts agree that higher accuracy often comes at the price of interpretability, which is critical to business adoption, trust, regulatory oversight (e.g., GDPR, Right to Explanation, etc.). As more industries from healthcare to banking are adopting ML models, their predictions are being used to justify the cost of healthcare and for loan approvals or denials.


The Complete Self-Driving Car Course - Applied Deep Learning

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Free Coupon Discount - The Complete Self-Driving Car Course - Applied Deep Learning, Learn to use Deep Learning, Computer Vision and Machine Learning techniques to Build an Autonomous Car with Python Created by Rayan Slim English [Auto], French [Auto] Preview this Udemy Course - GET COUPON CODE Self-driving cars have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward and creating new opportunities in the mobility sector. Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.


neuralmagic/sparseml

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Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved. Neural Magic's ML team created recipes encoding the necessary hyperparameters and instructions to create highly accurate pruned and pruned-quantized YOLOv3 models to simplify the process. These recipes allow anyone to plug in their data and leverage SparseML's recipe-driven approach on top of Ultralytics' robust training pipelines. The examples listed in this tutorial are all performed on the VOC dataset.


How to Do Hierarchical Clustering in Python ? 5 Easy Steps Only

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Hierarchical Clustering uses the distance based approach between the neighbor datapoints for clustering. Each data point is linked to its nearest neighbors. There are two ways you can do Hierarchical clustering Agglomerative that is bottom-up approach clustering and Divisive uses top-down approaches for clustering. In this tutorial, I will use the popular approach Agglomerative way. In order to find the number of subgroups in the dataset, you use dendrogram. It allows you to see linkages, relatedness using the tree graph. You will find many use cases for this type of clustering and some of them are DNA sequencing, Sentiment Analysis, Tracking Virus Diseases e.t.c. Popular Use Cases are Hospital Resource Management, Business Process Management, and Social Network Analysis. Here we are importing dendrogram, linkage, cluster, and cophenet from the scipy.cluster.hierarchy


10 Best + Free Python Bootcamp 2021-Take This Course

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With the help of this list, all those learners who wish to learn all about Python Bootcamp can enroll in any of the suitable courses and start learning from it from the comfort of their homes, and that too for free. Below are the names and short descriptions of the 10 best and free Python Bootcamp courses for 2021. A Free Python Bootcamp Course course that will make you learn Python like a professional in no time. The Free Python Bootcamp course starts with the basics and then go all the way to creating your own applications and games. Throughout the Free Python Bootcamp course, you will be learning a variety of topics that will make you a professional at developing different applications and games. The instructor has delivered all the learning content that will help you learn both Python 2 and Python 3. Starting the Free Python Bootcamp course, you will learn to create games with Python.