A Overview of Machine Learning Tools
This morning I would like to talk you more about machine learning tool, with main focus on PCA (Principal Component Analysis) and SVD(Support Vector Machine). However surfing from the Internet I found a course (without video), but very good that explain much of this concept and another. So to not reinvent the wheel I suggest I will show the principals topics covered in this review course (thank you Rachel Schutt).The course syllabus include: Week 1: What is Data Science?
Explain what a data science doWeek 2: Simulated Chaos, RealDirect, linear regression, k-nearest neighbors
That are the simplest algorithms used in machine learningWeek 3: Naive Bayes, Laplace Smoothing, APIs and Scraping data off the web Another tools used in machine learning Week 4: The Data Science Process, k-means, Classifiers, Logistic Regression and Evaluation
Cover some tools used to classificationWeek 5: GetGlue, time series, financial modeling, advanced regression, and ethics
Applied machine learningWeek 6: Kaggle, crowdsourcing, decision trees, random forests, social networks, and Google’s hybrid research environment
Decision rules and social networksWeek 7: hunch.com, Recommendation Engines, SVD, Alternating Least Squares, Convexity, Filter Bubbles
Some tools used in recommendation systems
I hope you enjoy this course so like me
In another post I talk about a little more about the topics covered on that course and how to apply this tools in computer applied computer science.