I first signed up for a Standford University course on artificial intelligence. Took one of the courses and found it was actually a bit difficulty. Not sure what the other 200,000 people who took it, and completed it ( I didn't), thought. Next, I signed up to take a course from
udacity (Standford again) on progamming the robotic car, but that class time conflicted with something called my job. These courses are interesting, but today I found out that my university library, in the multi-media centre, has a subscription and dedicated computer with an IP link to
lynda.com where there are all kinds of software tutorials. As I am still trying to learn enough Python to run an application at that place called my job, this might be very useful. Programming a robotic car might be more fun, but for now, I am going let other people do that. In fact though, the prerequiste for
programming the robotic car is knowledge of Python!
You should either already know Python, or have enough experience with another language to be confident you can pick up what you need on your own. Fortunately, Python was built to be easy to learn, read, and use. If you already know another programming language, you'll be coding in Python in less than an hour. Additionally, knowledge of probability and linear algebra will be helpful.
Python Review
Python for Programmers
Introduction to Programs Data Types and Variables
Python Lists
For Loops in Python
While Loops in Python
Writing a Simple Factorial Program
Fun with Strings
Probability
Basic Probability
Probability (Part 6) [Conditional Probability]
Probability (Part 7) [Bayes' Rule]
Probability (Part 8) [More Bayes' Rule]
Introduction to Random Variables
Probability Density Functions
Expected Value: E(X)
Linear Algebra
Introduction to Matrices
Matrix Multiplication (Part 1)
Matrix Multiplication (Part 2)
Inverse Matrix (Part 1)
Inverting Matrices (Part 2)
Inverting Matrices (Part 3)
Matrices to Solve a System of Equations
Singular Matrices
Introduction to Vectors
Vector Dot Product and Vector Length
Defining the Angle Between Vectors
Cross Product Introduction
Matrix Vector Products
Linear Transformations as Matrix Vector Products
Linear Transformation Examples: Scaling and Reflections
Linear Transformation Examples: Rotations in R2
Introduction to Projections
Exploring the Solution Set of Ax = b
Transpose of a Matrix
3x3 Determinant
Introduction to Eigenvalues and Eigenvectors