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 do

Week 2: Simulated Chaos, RealDirect, linear regression, k-nearest neighbors

That are the simplest algorithms used in machine learning

Week 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 classification

Week 5: GetGlue, time series, financial modeling, advanced regression, and ethics

Applied machine learning

Week 6: Kaggle, crowdsourcing, decision trees, random forests, social networks, and Google’s hybrid research environment

Decision rules and social networks

Week 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.

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