Master statistics & machine learning: intuition, math, code
Master statistics & machine learning: intuition, math, code, available at $99.99, has an average rating of 4.67, with 224 lectures, based on 2456 reviews, and has 27586 subscribers.
You will learn about Descriptive statistics (mean, variance, etc) Inferential statistics T-tests, correlation, ANOVA, regression, clustering The math behind the "black box" statistical methods How to implement statistical methods in code How to interpret statistics correctly and avoid common misunderstandings Coding techniques in Python and MATLAB/Octave Machine learning methods like clustering, predictive analysis, classification, and data cleaning This course is ideal for individuals who are Students taking statistics or machine learning courses or Professionals who need to learn statistics and machine learning or Scientists who want to understand their data analyses or Anyone who wants to see "under the hood" of machine learning or Artificial intelligence (AI) students or Business intelligence students It is particularly useful for Students taking statistics or machine learning courses or Professionals who need to learn statistics and machine learning or Scientists who want to understand their data analyses or Anyone who wants to see "under the hood" of machine learning or Artificial intelligence (AI) students or Business intelligence students.
Enroll now: Master statistics & machine learning: intuition, math, code
Summary
Title: Master statistics & machine learning: intuition, math, code
Price: $99.99
Average Rating: 4.67
Number of Lectures: 224
Number of Published Lectures: 224
Number of Curriculum Items: 224
Number of Published Curriculum Objects: 224
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Descriptive statistics (mean, variance, etc)
- Inferential statistics
- T-tests, correlation, ANOVA, regression, clustering
- The math behind the "black box" statistical methods
- How to implement statistical methods in code
- How to interpret statistics correctly and avoid common misunderstandings
- Coding techniques in Python and MATLAB/Octave
- Machine learning methods like clustering, predictive analysis, classification, and data cleaning
Who Should Attend
- Students taking statistics or machine learning courses
- Professionals who need to learn statistics and machine learning
- Scientists who want to understand their data analyses
- Anyone who wants to see "under the hood" of machine learning
- Artificial intelligence (AI) students
- Business intelligence students
Target Audiences
- Students taking statistics or machine learning courses
- Professionals who need to learn statistics and machine learning
- Scientists who want to understand their data analyses
- Anyone who wants to see "under the hood" of machine learning
- Artificial intelligence (AI) students
- Business intelligence students
Statistics and probability control your life. I don’t just mean What YouTube’s algorithm recommends you to watch next, and I don’t just mean the chance of meeting your future significant other in class or at a bar. Human behavior, single-cell organisms, Earthquakes, the stock market, whether it will snow in the first week of December, and countless other phenomena are probabilistic and statistical. Even the very nature of the most fundamental deep structure of the universe is governed by probability and statistics.
You need to understand statistics.
Nearly all areas of human civilization are incorporating code and numerical computations. This means that many jobs and areas of study are based on applications of statistical and machine-learning techniques in programming languages like Python and MATLAB. This is often called ‘data science’ and is an increasingly important topic. Statistics and machine learning are also fundamental to artificial intelligence (AI) and business intelligence.
If you want to make yourself a future-proof employee, employer, data scientist, or researcher in any technical field — ranging from data scientist to engineering to research scientist to deep learning modeler — you’ll need to know statistics and machine-learning. And you’ll need to know how to implement concepts like probability theory and confidence intervals, k-means clustering and PCA, Spearman correlation and logistic regression, in computer languages like Python or MATLAB.
There are six reasons why you should take this course:
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This course covers everything you need to understand the fundamentals of statistics, machine learning, and data science, from bar plots to ANOVAs, regression to k-means, t-test to non-parametric permutation testing.
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After completing this course, you will be able to understand a wide range of statistical and machine-learning analyses, even specific advanced methods that aren’t taught here. That’s because you will learn the foundations upon which advanced methods are build.
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This course balances mathematical rigor with intuitive explanations, and hands-on explorations in code.
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Enrolling in the course gives you access to the Q&A, in which I actively participate every day.
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I’ve been studying, developing, and teaching statistics for over 20 years, and I think math is, like, really cool.
What you need to know before taking this course:
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High-school level maths. This is an applications-oriented course, so I don’t go into a lot of detail about proofs, derivations, or calculus.
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Basic coding skills in Python or MATLAB. This is necessary only if you want to follow along with the code. You can successfully complete this course without writing a single line of code! But participating in the coding exercises will help you learn the material. The MATLAB code relies on the Statistics and Machine Learning toolbox (you can use Octave if you don’t have MATLAB or the statistics toolbox). Python code is written in Jupyter notebooks.
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I recommend taking my free course called “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!
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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 sometimes re-film existing lectures) to explain maths concepts better if students find a topic confusing or if I made a mistake in the lecture (rare, but it happens!).
You can check the “Last updated” text at the top of this page to see when I last worked on improving this course!
What if you have questions about the material?
This course has a Q&A (question and answer) section where you can post your questions about the course material (about the maths, statistics, coding, or machine learning aspects). I try to answer all questions within a day. You can also see all other questions and answers, which really improves how much you can learn! And you can contribute to the Q&A by posting to ongoing discussions.
And, you can also post your code for feedback or just to show off — I love it when students actually write better code than me! (Ahem, doesn’t happen so often.)
What should you do now?
First of all, congrats on reading this far; that means you are seriously interested in learning statistics and machine learning. Watch the preview videos, check out the reviews, and, when you’re ready, invest in your brain by learning from this course!
Course Curriculum
Chapter 1: Introductions
Lecture 1: [Important] Getting the most out of this course
Lecture 2: About using MATLAB or Python
Lecture 3: Statistics guessing game!
Lecture 4: Using the Q&A forum
Lecture 5: (optional) Entering time-stamped notes in the Udemy video player
Chapter 2: Math prerequisites
Lecture 1: Should you memorize statistical formulas?
Lecture 2: Arithmetic and exponents
Lecture 3: Scientific notation
Lecture 4: Summation notation
Lecture 5: Absolute value
Lecture 6: Natural exponent and logarithm
Lecture 7: The logistic function
Lecture 8: Rank and tied-rank
Chapter 3: IMPORTANT: Download course materials
Lecture 1: Download materials for the entire course!
Chapter 4: What are (is?) data?
Lecture 1: Is "data" singular or plural?!?!!?!
Lecture 2: Where do data come from and what do they mean?
Lecture 3: Types of data: categorical, numerical, etc
Lecture 4: Code: representing types of data on computers
Lecture 5: Sample vs. population data
Lecture 6: Samples, case reports, and anecdotes
Lecture 7: The ethics of making up data
Chapter 5: Visualizing data
Lecture 1: Bar plots
Lecture 2: Code: bar plots
Lecture 3: Box-and-whisker plots
Lecture 4: Code: box plots
Lecture 5: "Unsupervised learning": Boxplots of normal and uniform noise
Lecture 6: Histograms
Lecture 7: Code: histograms
Lecture 8: "Unsupervised learning": Histogram proportion
Lecture 9: Pie charts
Lecture 10: Code: pie charts
Lecture 11: When to use lines instead of bars
Lecture 12: Linear vs. logarithmic axis scaling
Lecture 13: Code: line plots
Lecture 14: "Unsupervised learning": log-scaled plots
Chapter 6: Descriptive statistics
Lecture 1: Descriptive vs. inferential statistics
Lecture 2: Accuracy, precision, resolution
Lecture 3: Data distributions
Lecture 4: Code: data from different distributions
Lecture 5: "Unsupervised learning": histograms of distributions
Lecture 6: The beauty and simplicity of Normal
Lecture 7: Measures of central tendency (mean)
Lecture 8: Measures of central tendency (median, mode)
Lecture 9: Code: computing central tendency
Lecture 10: "Unsupervised learning": central tendencies with outliers
Lecture 11: Measures of dispersion (variance, standard deviation)
Lecture 12: Code: Computing dispersion
Lecture 13: Interquartile range (IQR)
Lecture 14: Code: IQR
Lecture 15: QQ plots
Lecture 16: Code: QQ plots
Lecture 17: Statistical "moments"
Lecture 18: Histograms part 2: Number of bins
Lecture 19: Code: Histogram bins
Lecture 20: Violin plots
Lecture 21: Code: violin plots
Lecture 22: "Unsupervised learning": asymmetric violin plots
Lecture 23: Shannon entropy
Lecture 24: Code: entropy
Lecture 25: "Unsupervised learning": entropy and number of bins
Chapter 7: Data normalizations and outliers
Lecture 1: Garbage in, garbage out (GIGO)
Lecture 2: Z-score standardization
Lecture 3: Code: z-score
Lecture 4: Min-max scaling
Lecture 5: Code: min-max scaling
Lecture 6: "Unsupervised learning": Invert the min-max scaling
Lecture 7: What are outliers and why are they dangerous?
Lecture 8: Removing outliers: z-score method
Lecture 9: The modified z-score method
Lecture 10: Code: z-score for outlier removal
Lecture 11: "Unsupervised learning": z vs. modified-z
Lecture 12: Multivariate outlier detection
Lecture 13: Code: Euclidean distance for outlier removal
Lecture 14: Removing outliers by data trimming
Lecture 15: Code: Data trimming to remove outliers
Lecture 16: Non-parametric solutions to outliers
Lecture 17: Nonlinear data transformations
Lecture 18: An outlier lecture on personal accountability
Chapter 8: Probability theory
Lecture 1: What is probability?
Lecture 2: Probability vs. proportion
Lecture 3: Computing probabilities
Lecture 4: Code: compute probabilities
Lecture 5: Probability and odds
Lecture 6: "Unsupervised learning": probabilities of odds-space
Lecture 7: Probability mass vs. density
Lecture 8: Code: compute probability mass functions
Lecture 9: Cumulative distribution functions
Lecture 10: Code: cdfs and pdfs
Lecture 11: "Unsupervised learning": cdf's for various distributions
Lecture 12: Creating sample estimate distributions
Lecture 13: Monte Carlo sampling
Lecture 14: Sampling variability, noise, and other annoyances
Instructors
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Mike X Cohen
Educator and writer
Rating Distribution
- 1 stars: 20 votes
- 2 stars: 16 votes
- 3 stars: 78 votes
- 4 stars: 556 votes
- 5 stars: 1786 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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