# 100 Days of ML — Day 13 — Skills Needed to Learn AI at Every Level

As I remarked in the opening post of this journey, I taught myself AI, Machine Learning, and Deep Learning in 30 days. Having said that, I did have the math and programming background to dive as deep as I did, but I made a lot of mistakes that cut into my time. So just follow this path and you’ll be way more efficient.

BEGINNER

How AI works

Step 1: Wrangle data

Step 2: Organize into rows and columns

Step 3: Data goes through weights then hidden layers then weights to outputs

Step 4: Backpropagation and gradient descent. Weights update back to hidden layers back to weights back to inputs.

Step 5: Repeat until error is less than 1%.

INTERMEDIATE

Programming

Pick a language: Python, R, C++, C, C#, Java, JavaScript. Then, learn how to use libraries. Now learn TensorFlow, Torch, or Keras. Cool.

EXPERT

Statistics

You need to know probability and the error methods: Sum of Squares Errors, Mean Squared Errors.

Calculus

You need to understand partial derivatives. To understand partial derivatives, you need to know derivatives.

Linear Algebra

To understand Linear Algebra, you need to know what a tensor is.

To understand that, you need to know what a matrix is.

To understand that, you need to know what a vector is.

To understand that, you need to know what a scalar is.

So, a scalar is a single number: 5. We’ll throw it in brackets. .

A vector is a list of scalars: [1,2,3,4,5].

A matrix is a vector of vectors: [[1,2,3],[4,5,6],[7,8,9]].

A tensor is…

Go on.

Yep, a matrix of matrices and it’s better to see visually.