A rigorous and fascinating deep-dive into statistics and machine-studying, with fingers-on functions in Python and MATLAB.
What you’ll study
- Descriptive statistics (imply, variance, and many others)
- Inferential statistics
- T-assessments, correlation, ANOVA, regression, clustering
- The mathematics behind the “black box” statistical strategies
- Learn how to implement statistical strategies in code
- Learn how to interpret statistics accurately and keep away from widespread misunderstandings
- Coding strategies in Python and MATLAB/Octave
- Machine studying strategies like clustering, predictive evaluation, classification, and information cleansing
- Good work ethic and motivation to study.
- Earlier background in statistics or machine studying shouldn’t be essential.
- Python -OR- MATLAB with the Statistics toolbox (or Octave).
- Some coding familiarity for the elective code workout routines.
- No textbooks essential! All supplies are offered contained in the course.
Statistics and likelihood management your life. I don’t simply imply What YouTube’s algorithm recommends you to observe subsequent, and I don’t simply imply the prospect of assembly your future vital different at school or at a bar. Human conduct, single-cell organisms, Earthquakes, the inventory market, whether or not it is going to snow within the first week of December, and numerous different phenomena are probabilistic and statistical. Even the very nature of probably the most basic deep construction of the universe is ruled by likelihood and statistics.
It is advisable to perceive statistics.
Practically all areas of human civilization are incorporating code and numerical computations. Because of this many roles and areas of research are based mostly on functions of statistical and machine-studying strategies in programming languages like Python and MATLAB. That is typically known as ‘data science’ and is an more and more vital subject. Statistics and machine studying are additionally basic to synthetic intelligence (AI) and enterprise intelligence.
If you wish to make your self a future-proof worker, employer, information scientist, or researcher in any technical area — starting from information scientist to engineering to analysis scientist to deep studying modeler — you’ll must know statistics and machine-studying. And also you’ll must know the best way to implement ideas like likelihood principle and confidence intervals, okay-means clustering and PCA, Spearman correlation and logistic regression, in pc languages like Python or MATLAB.
There are six the reason why you need to take this course:
- This course covers every little thing it’s essential to perceive the basics of statistics, machine studying, and information science, from bar plots to ANOVAs, regression to okay-means, t-take a look at to non-parametric permutation testing.
- After finishing this course, it is possible for you to to grasp a variety of statistical and machine-studying analyses, even particular superior strategies that aren’t taught right here. That’s as a result of you’ll study the foundations upon which superior strategies are construct.
- This course balances mathematical rigor with intuitive explanations, and fingers-on explorations in code.
- Enrolling within the course provides you entry to the Q&A, wherein I actively take part day by day.
- I’ve been finding out, growing, and educating statistics for 20 years, and I’m, like, actually nice at math.
What it’s essential to know earlier than taking this course:
- Excessive-college degree maths. That is an functions-oriented course, so I don’t go into lots of element about proofs, derivations, or calculus.
- Primary coding abilities in Python or MATLAB. That is essential solely if you wish to comply with together with the code. You may efficiently full this course with out writing a single line of code! However taking part within the coding workout routines will assist you study the fabric. The MATLAB code depends on the Statistics and Machine Studying toolbox (you need to use Octave for those who don’t have MATLAB or the statistics toolbox). Python code is written in Jupyter notebooks.
- I advocate taking my free course known as “Statistics literacy for non-statisticians“. It’s 90 minutes long and will give you a bird’s-eye-view of the main topics in statistics that I go into much much much more detail about here in this course. Note that the free short course is not required for this course, but complements this course nicely. And you can get through the whole thing in less than an hour if you watch if on 1.5x speed!
- You do not need any previous experience with statistics, machine learning, deep learning, or data science. That’s why you’re here!
Is this course up to date?
Yes, I maintain all of my courses regularly. I add new lectures to keep the course “alive,” and I add new lectures (or typically re-movie current lectures) to elucidate maths ideas higher if college students discover a subject complicated or if I made a mistake within the lecture (uncommon, but it surely occurs!).
You may examine the “Last updated” textual content on the prime of this web page to see once I final labored on bettering this course!
What when you have questions concerning the materials?
This course has a Q&A (query and reply) part the place you possibly can put up your questions concerning the course materials (concerning the maths, statistics, coding, or machine studying features). I attempt to reply all questions inside a day. You too can see all different questions and solutions, which actually improves how a lot you possibly can study! And you may contribute to the Q&A by posting to ongoing discussions.
And, you can too put up your code for suggestions or simply to indicate off — I adore it when college students truly write higher code than mine! (Ahem, doesn’t occur so typically.)
What do you have to do now?
To start with, congrats on studying this far; meaning you might be severely considering studying statistics and machine studying. Watch the preview movies, take a look at the critiques, and, if you’re prepared, spend money on your mind by studying from this course!
Who this course is for:
- College students taking statistics or machine studying programs
- Professionals who must study statistics and machine studying
- Scientists who wish to perceive their information analyses
- Anybody who needs to see “under the hood” of machine studying
- Synthetic intelligence (AI) college students
- Enterprise intelligence college students
Created by Mike X Cohen
Final up to date 3/2021
Measurement: 11.72 GB
DISCLAIMER: No Copyright Infringement Meant, All Rights Reserved to the Precise Proprietor. This content material has been shared beneath Academic Functions Solely. For Copyright Content material Removing Please Contact the Administrator or E-mail at Getintocourse@gmail.com