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Linear Regression and Logistic Regression using R Studio

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In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.


What Is Artificial Intelligence Engineering? Prospects, Opportunities, and Career Outlooks - ITChronicles

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Research conducted by Gartner suggests that artificial intelligence or AI will create a business value of US $3.9 trillion by 2022. What's more, artificial intelligence is expected to be the most disruptive technology category for the next decade, due to advances in computing power, capacity, speed, and data diversity, along with the further evolution of deep neural networks (DNN). This growth is fueling a demand for talent in a number of related disciplines, including that of artificial intelligence engineering. But what is artificial intelligence engineering? Before answering that question, it's worth stepping back a little, to look at the evolution of artificial intelligence itself, and how it is enabling new ways of doing things that new require new skill sets to implement.


Deep Learning Prerequisites: Linear Regression in Python

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Online Courses Udemy Data science: Learn linear regression from scratch and build your own working program in Python for data analysis. Created by Lazy Programmer Inc. English [Auto-generated], Spanish [Auto-generated] Students also bought Artificial Intelligence: Reinforcement Learning in Python Data Science: Natural Language Processing (NLP) in Python Natural Language Processing with Deep Learning in Python Cluster Analysis and Unsupervised Machine Learning in Python Complete Python Bootcamp: Go from zero to hero in Python 3 Preview this course GET COUPON CODE Description This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python. Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come.


Hands-On Tutorial On Machine Learning Pipelines With Scikit-Learn

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With increasing demand in machine learning and data science in businesses, for upgraded data strategizing there's a need for a better workflow to ensure robustness in data modelling. Machine learning has certain steps to be followed namely – data collection, data preprocessing(cleaning and feature engineering), model training, validation and prediction on the test data(which is previously unseen by model). Here testing data needs to go through the same preprocessing as training data. For this iterative process, pipelines are used which can automate the entire process for both training and testing data. It ensures reusability of the model by reducing the redundant part, thereby speeding up the process.



An Introduction to Support Vector Machines (SVM)

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A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. You're refining your training data, and maybe you've even tried stuff out using Naive Bayes. But now you're feeling confident in your dataset, and want to take it one step further. Enter Support Vector Machines (SVM): a fast and dependable classification algorithm that performs very well with a limited amount of data to analyze.


Making AI, Machine Learning Work for You!

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Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.


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.


Making AI, Machine Learning Work for You!

#artificialintelligence

Most data organisations hold is not labeled, and labeled data is the foundation of AI jobs and AI projects. "Labeled data, means marking up or annotating your data for the target model so it can predict. In general, data labeling includes data tagging, annotation, moderation, classification, transcription, and processing." Particular features are highlighted by labeled data and the classification of those attributes maybe be analysed by models for patterns in order to predict the new targets. An example would be labelling images as cancerous and benign or non-cancerous for a set of medical images that a Convolutional Neural Network (CNN) computer vision algorithm may then classify unseen images of the same class of data in the future. Niti Sharma also notes some key points to consider.


14 Popular Machine Learning Evaluation Metrics

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Thus far in our journey through Machine Learning Basics, we covered several topics. We investigated some regression algorithms, classification algorithms and algorithms that can be used for both types of problems (SVM, Decision Trees and Random Forest). Apart from that, we dipped our toes in unsupervised learning, saw how we can use this type of learning for clustering and learned about several clustering techniques. Finally, in the previous article, we talked about regularization and machine learning model performance. In all these articles, we used Python for "from the scratch" implementations and libraries like TensorFlow, Pytorch and SciKit Learn.