# AI News, Level-Up Your Machine Learning

- On Monday, June 4, 2018
- By Read More

## Level-Up Your Machine Learning

Since launching Metacademy, I've had a number of people ask , What should I do if I want to get 'better' at machine learning, but I don't know what I want to learn?

then watch as they grimace in the same way an out-of-shape person grimaces when a healthy friend responds with, 'Oh, I watch what I eat and consistently exercise.'

you'll scribble in the margins and dog-ear commonly referenced areas and look for applications of the topics you learn -- the textbook itself becomes a part of your knowledge (the above image shows my nearest textbook).

Key Chapters: It's a short read, and every chapter is fairly illuminating -- though, you can skip the worksheet examples, and chapters 8 and 10 if you're interested in a basic overview.

This will test your ability to manipulate data for a desired machine learning task, and also your ability to apply the correct machine learning technique to a somewhat vague problem.

Expectations: You'll be able to recognize when fundamental machine learning algorithms apply to certain problems and implement functioning machine learning code in R Necessary Background: No real prerequisites, though the following will help (these can be learned/reviewed as you go): Key Chapters: It's a short book, and I recommend all of the chapters -- be sure to actually think through the examples (and type them into R).

This will test your data munging capabilities, your strategy for analyzing a larger dataset, your knowledge of machine learning techniques, and your ability to write analysis code in R.

This will test your ability to understand and interpret cutting-edge machine learning algorithms, approximate and online inference techniques, as well as your implementation chops, your data munging abilities, and your ability to define an interesting application from a vaguely defined problem.

If you're unfamiliar with Bayesian statistics, I recommend studying the first 5 chapters of Doing Bayesian Data Analysis There's a number of subjects you may want to study in depth at the master level: convex optimization, [measure-theoretic] probability theory, discrete optimization, linear algebra, differential geometry, or maybe computational neurology.

PGMs pervade machine learning, and with a strong understanding of this content, you'll be able to dive into most machine learning specialties without too much pain.

- On Wednesday, March 20, 2019

**How I memorized an entire chapter from “Moby Dick”**

With memory palaces, anyone can look like a memory genius. Subscribe to our channel! I always thought I was born with a bad memory

**R tutorial: Introduction to cleaning data with R**

Learn more about cleaning data with R: Hi, I'm Nick. I'm a data scientist at DataCamp and I'll be your ..

**How to Remember What You Read**

Forget something? Check out these resources for strengthening your memory Unlimited Memory: How to Use Learning Strategies to Learn Faster: ...

**Deep Learning Chapter 10 Sequence Modeling: Recurrent and Recursive Nets presented by Ian Goodfellow**

This is a Deep Learning Book Club discussion of Chapter 10: Sequence Modeling: Recurrent and Recursive Nets. Chapter is presented by author Ian ...

**11 Secrets to Memorize Things Quicker Than Others**

We learn things throughout our entire lives, but we still don't know everything because we forget a lot of information. Bright Side will tell you about 11 simple ...

**FRENCH GRAMMAR HOMEWORKS! - How to finish all available French Grammar Homeworks! | Version 0.14.5.2**

A complete guide and walkthrough focused on getting all FRENCH GRAMMAR VOLUMES from 1 to 3 (Wait until 15.0)! Uncensored Version on my Patreon!

**Online Discussion: Deep Learning Book Ch 1-2**

Bi-Weekly Online Discussion: Deep Learning Book with the Deep Learning Enthusiasts We are ..

**Should You Take Notes on Paper or on a Computer? - College Info Geek**

Which note-taking medium should you use when you're taking notes in class - a paper notebook, or your computer? Companion blog post with notes and links: ...

**Statistical Aspects of Data Mining (Stats 202) Day 1**

Google Tech Talks June 26, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the ...

**Performance 1: Data partitioning for time series**

Data partitioning is a fundamental step in predictive modeling. For time series, partitioning is done differently from cross-sectional data. This video supports the ...