For all the self-taught geeks out there, here is our content library with most of the learning materials we have produced throughout the years.
It makes sense to start learning by reading and watching videos about fundamentals and how things work.
Data Science and Machine Learning - 16 wks
Full-Stack Software Developer - 16w
Search from all Lessons
Curated list of small interactive and incremental exercises you can take to get better at any coding skill.
Curated section of projects to build while learning with simple instructions, videos, solutions, and more.
Guides on different topics related to the technologies that we teach in our courses
Social & live learning
The most efficient way to learn: Join a cohort with classmates just like you, live streams, impromptu coding sessions, live tutorials with real experts, and stay motivated.
The fourth row of our sennet table contained the values:
We can draw a plot of this using bars to represent the counts of each outcome.
bars = ['no white', 'one white', 'two white', 'three white', 'four white'] counts = [1, 4, 6, 4, 1]
plt.bar(bars, counts) plt.title('Counts for each outcome with four two sided sticks');
Using this plot, we come back to the problem of determining the probability of a given outcome or outcomes. Here, we can interpret this probability as the relative area of a given bar to the overall count. For example, we consider each bar having width of one unit, and height of the count. Thus, we have a total area of:
This is the total number of possible outcomes. Thus, determining the probability of a specific outcome is as simple as dividing the total area of our bars by the area under the event of interest.