Master statistics & machine learning: intuition, math, code

Master statistics & machine learning: intuition, math, code

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 36 Hours | 11.7 GB

A rigorous and engaging deep-dive into statistics and machine-learning, with hands-on applications in Python and MATLAB.

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.

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:

  • 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.
  • 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.
  • This course balances mathematical rigor with intuitive explanations, and hands-on explorations in code.
  • Enrolling in the course gives you access to the Q&A, in which I actively participate every day.
  • I’ve been studying, developing, and teaching statistics for 20 years, and I’m, like, really great at math.

What you’ll 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

+ Table of Contents

1 [Important] Getting the most out of this course
2 About using MATLAB or Python
3 Statistics guessing game!
4 Using the Q&A forum
5 (optional) Entering time-stamped notes in the Udemy video player

Math prerequisites
6 Should you memorize statistical formulas
7 Arithmetic and exponents
8 Scientific notation
9 Summation notation
10 Absolute value
11 Natural exponent and logarithm
12 The logistic function
13 Rank and tied-rank

IMPORTANT Download course materials
14 Download materials for the entire course!

What are (is ) data
15 Is data singular or plural ! !! !
16 Where do data come from and what do they mean
17 Types of data categorical, numerical, etc
18 Code representing types of data on computers
19 Sample vs. population data
20 Samples, case reports, and anecdotes
21 The ethics of making up data

Visualizing data
22 Bar plots
23 Code bar plots
24 Box-and-whisker plots
25 Code box plots
26 Unsupervised learning Boxplots of normal and uniform noise
27 Histograms
28 Code histograms
29 Unsupervised learning Histogram proportion
30 Pie charts
31 Code pie charts
32 When to use lines instead of bars
33 Linear vs. logarithmic axis scaling
34 Code line plots
35 Unsupervised learning log-scaled plots

Descriptive statistics
36 Descriptive vs. inferential statistics
37 Accuracy, precision, resolution
38 Data distributions
39 Code data from different distributions
40 Unsupervised learning histograms of distributions
41 The beauty and simplicity of Normal
42 Measures of central tendency (mean)
43 Measures of central tendency (median, mode)
44 Code computing central tendency
45 Unsupervised learning central tendencies with outliers
46 Measures of dispersion (variance, standard deviation)
47 Code Computing dispersion
48 Interquartile range (IQR)
49 Code IQR
50 QQ plots
51 Code QQ plots
52 Statistical moments
53 Histograms part 2 Number of bins
54 Code Histogram bins
55 Violin plots
56 Code violin plots
57 Unsupervised learning asymmetric violin plots
58 Shannon entropy
59 Code entropy
60 Unsupervised learning entropy and number of bins

Data normalizations and outliers
61 Garbage in, garbage out (GIGO)
62 Z-score standardization
63 Code z-score
64 Min-max scaling
65 Code min-max scaling
66 Unsupervised learning Invert the min-max scaling
67 What are outliers and why are they dangerous
68 Removing outliers z-score method
69 The modified z-score method
70 Code z-score for outlier removal
71 Unsupervised learning z vs. modified-z
72 Multivariate outlier detection
73 Code Euclidean distance for outlier removal
74 Removing outliers by data trimming
75 Code Data trimming to remove outliers
76 Non-parametric solutions to outliers
77 An outlier lecture on personal accountability

Probability theory
78 What is probability
79 Probability vs. proportion
80 Computing probabilities
81 Code compute probabilities
82 Probability and odds
83 Unsupervised learning probabilities of odds-space
84 Probability mass vs. density
85 Code compute probability mass functions
86 Cumulative probability distributions
87 Code cdfs and pdfs
88 Unsupervised learning cdf’s for various distributions
89 Creating sample estimate distributions
90 Monte Carlo sampling
91 Sampling variability, noise, and other annoyances
92 Code sampling variability
93 Expected value
94 Conditional probability
95 Code conditional probabilities
96 Tree diagrams for conditional probabilities
97 The Law of Large Numbers
98 Code Law of Large Numbers in action
99 The Central Limit Theorem
100 Code the CLT in action
101 Unsupervised learning Averaging pairs of numbers

Hypothesis testing
102 IVs, DVs, models, and other stats lingo
103 What is an hypothesis and how do you specify one
104 Sample distributions under null and alternative hypotheses
105 P-values definition, tails, and misinterpretations
106 P-z combinations that you should memorize
107 Degrees of freedom
108 Type 1 and Type 2 errors
109 Parametric vs. non-parametric tests
110 Multiple comparisons and Bonferroni correction
111 Statistical vs. theoretical vs. clinical significance
112 Cross-validation
113 Statistical significance vs. classification accuracy

The t-test family
114 Purpose and interpretation of the t-test
115 One-sample t-test
116 Code One-sample t-test
117 Unsupervised learning The role of variance
118 Two-samples t-test
119 Code Two-samples t-test
120 Unsupervised learning Importance of N for t-test
121 Wilcoxon signed-rank (nonparametric t-test)
122 Code Signed-rank test
123 Mann-Whitney U test (nonparametric t-test)
124 Code Mann-Whitney U test
125 Permutation testing for t-test significance
126 Code permutation testing
127 Unsupervised learning How many permutations

Confidence intervals on parameters
128 What are confidence intervals and why do we need them
129 Computing confidence intervals via formula
130 Code compute confidence intervals by formula
131 Confidence intervals via bootstrapping (resampling)
132 Code bootstrapping confidence intervals
133 Unsupervised learning Confidence intervals for variance
134 Misconceptions about confidence intervals

135 Motivation and description of correlation
136 Covariance and correlation formulas
137 Code correlation coefficient
138 Code Simulate data with specified correlation
139 Correlation matrix
140 Code correlation matrix
141 Unsupervised learning average correlation matrices
142 Unsupervised learning correlation to covariance matrix
143 Partial correlation
144 Code partial correlation
145 The problem with Pearson
146 Nonparametric correlation Spearman rank
147 Fisher-Z transformation for correlations
148 Code Spearman correlation and Fisher-Z
149 Unsupervised learning Spearman correlation
150 Unsupervised learning confidence interval on correlation
151 Kendall’s correlation for ordinal data
152 Code Kendall correlation
153 Unsupervised learning Does Kendall vs. Pearson matter
154 Cosine similarity
155 Code Cosine similarity vs. Pearson correlation

Analysis of Variance (ANOVA)
156 ANOVA intro, part1
157 ANOVA intro, part 2
158 Sum of squares
159 The F-test and the ANOVA table
160 The omnibus F-test and post-hoc comparisons
161 The two-way ANOVA
162 One-way ANOVA example
163 Code One-way ANOVA (independent samples)
164 Code One-way repeated-measures ANOVA
165 Two-way ANOVA example
166 Code Two-way mixed ANOVA

167 Introduction to GLM regression
168 Least-squares solution to the GLM
169 Evaluating regression models R2 and F
170 Simple regression
171 Code simple regression
172 Unsupervised learning Compute R2 and F
173 Multiple regression
174 Standardizing regression coefficients
175 Code Multiple regression
176 Polynomial regression models
177 Code polynomial modeling
178 Unsupervised learning Polynomial design matrix
179 Logistic regression
180 Code Logistic regression
181 Under- and over-fitting
182 Unsupervised learning Overfit data
183 Comparing nested models
184 What to do about missing data

Statistical power and sample sizes
185 What is statistical power and why is it important
186 Estimating statistical power and sample size
187 Compute power and sample size using G Power

Clustering and dimension-reduction
188 K-means clustering
189 Code k-means clustering
190 Unsupervised learning K-means and normalization
191 Unsupervised learning K-means on a Gauss blur
192 Clustering via dbscan
193 Code dbscan
194 Unsupervised learning dbscan vs. k-means
195 K-nearest neighbor classification
196 Code KNN
197 Principal components analysis (PCA)
198 Code PCA
199 Unsupervised learning K-means on PC data
200 Independent components analysis (ICA)
201 Code ICA

Signal detection theory
202 The two perspectives of the world
203 d-prime
204 Code d-prime
205 Response bias
206 Code Response bias
207 Receiver operating characteristics (ROC)
208 Code ROC curves
209 Unsupervised learning Make this plot look nicer!

Bonus section
210 About deep learning
211 Bonus content