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[FreeCoursesOnline.Me] [Packt] Hands-On Machine Learning Using JavaScript [FCO]
TORRENT SUMMARY
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Learn all the CSS Grid concepts and create professional responsive website designs - multiple website layout projects.
Video Details
ISBN 9781789613360
Course Length 2 hours 4 minutes
Table of Contents
• GETTING READY!
• DIVING HEADFIRST INTO SUPERVISED LEARNING
• IMPROVING MODELS
• USING SUPPORT VECTOR MACHINE AND RANDOM FORESTS FOR COMPLEX PROBLEMS
• FINDING HIDDEN VALUE IN UNLABELED DATA
• DEEP NEURAL NETWORKS
Video Description
Machine Learning is a growing and in-demand skill but until now JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. This course shows you various machine learning techniques in a practical way and helps you implement them using the JavaScript language.
Hands-On Machine Learning using JavaScript gives you the opportunity to use the power of machine learning (without installing additional software on the customer's computer) and make them feel safe as the data resides in the system. This course covers basic as well as advanced topics in Machine Learning and gives a holistic picture of the JavaScript machine learning ecosystem by making use of libraries to design smarter applications.
By the end of this course, you'll have gained hands-on experience in evaluating and implementing the right model using the power of JavaScript.
Code files for the course can be found here: https://github.com/PacktPublishing/Hands-On-Machine-Learning-using-JavaScript
Style and Approach
This application-focused course offers practical and actionable guidance with step-by-step instructions, and will enable you to develop your own ML models and methods and use them efficiently in a browser or a Node.js server.
What You Will Learn
• Understanding the machine learning JavaScript ecosystem
• Implement different approaches to problem-solving in machine learning
• Use JavaScript libraries to build neural network models
• Decide, analyze, and make predictions from real-world data
• Use machine learning tools to build models and solve problems
• Use JavaScript to build fun applications in the browser
Authors
Arish Ali
Arish Ali started his machine learning journey 5 years ago by winning an all India machine learning competition conducted by IISC and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has worked on some of the cutting-edge problems of Multi-Touch Attribution Modeling, Market Mix Modeling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at Bridge School of Management, which offers its course in Predictive Business Analytics along with Northwestern University (SPS).
He worked at a mental health start-up called Bemo as an AI developer where his role was to help automate the therapy provided to users and make it more personalized. He is currently the CEO at Neurofy Pvt Ltd, a people analytics start-up.
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FILE LIST
Filename
Size
01.Getting ready!/0101.The Course Overview.mp4
9.8 MB
01.Getting ready!/0102.Introduction to Machine Learning.mp4
59.1 MB
01.Getting ready!/0103.Tour of the JavaScript Machine Learning Landscape.mp4
29.8 MB
01.Getting ready!/0104.Setting Up Our Machine Learning Environment.mp4
95.7 MB
02.Diving Headfirst into Supervised Learning/0201.Understand Regression with Linear Regression.mp4
47.5 MB
02.Diving Headfirst into Supervised Learning/0202.Understanding How Linear Regression Works.mp4
19.7 MB
02.Diving Headfirst into Supervised Learning/0203.Predicting Salaries after College Using Linear Regression.mp4
69.9 MB
02.Diving Headfirst into Supervised Learning/0204.Understand Classification with Logistic Regression.mp4
29.8 MB
02.Diving Headfirst into Supervised Learning/0205.Classifying Clothes Using Logistic Regression.mp4
22.5 MB
03.Improving Models/0301.Model Evaluation.mp4
25.8 MB
03.Improving Models/0302.Better Measures than Accuracy.mp4
29.9 MB
03.Improving Models/0303.Understanding the Results.mp4
18.9 MB
03.Improving Models/0304.Improving the Models.mp4
22.6 MB
04. Using Support Vector Machine and Random Forests for Complex Problems/0401.What are Support Vector Machines.mp4
18.4 MB
04. Using Support Vector Machine and Random Forests for Complex Problems/0402.Using SVM Kernels to Transform Problems.mp4
12 MB
04. Using Support Vector Machine and Random Forests for Complex Problems/0403.Image Classifier Using SVM.mp4
33.3 MB
04. Using Support Vector Machine and Random Forests for Complex Problems/0404.Making Better Decision with Decision Trees.mp4
70.5 MB
04. Using Support Vector Machine and Random Forests for Complex Problems/0405.Combining Decision Trees to Make Better Predictions.mp4
23.6 MB
04. Using Support Vector Machine and Random Forests for Complex Problems/0406.Predicting Customer Churn Using Random Forests.mp4
20.1 MB
05.Finding Hidden Value in Unlabeled Data/0501.Introduction and Advantage of Unsupervised Learning.mp4
26.3 MB
05.Finding Hidden Value in Unlabeled Data/0502.Grouping Unlabeled Data in Meaningful Ways Using K-means Clustering.mp4
37.5 MB
05.Finding Hidden Value in Unlabeled Data/0503.Using Principal Component Analysis to Speed-up Machine Learning Algorithms.mp4
39.5 MB
05.Finding Hidden Value in Unlabeled Data/0504.Analyzing Plant Species Using K-means Clustering.mp4
75.8 MB
06.Deep Neural Networks/0601.Introduction to Neural Networks.mp4
15.8 MB
06.Deep Neural Networks/0602.How a Neural Network Works.mp4
58.1 MB
06.Deep Neural Networks/0603.Neural Networks in Tensorflow.js.mp4
68.4 MB
06.Deep Neural Networks/0604.Multiclass Classification Using TensorFlow.js.mp4
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