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    Master statistics & machine learning: intuition, math, code

    BY
    Udemy

    Develop an understanding of the deep concepts associated with python and MATLAB for operations involving statistics and machine learning.

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    Mode

    Online

    Fees

    ₹ 3499

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course overview

    Master statistics & machine learning: intuition, math, code certification course is created by Mike X Cohen - Neuroscientist, Writer & Instructor, and is made available through Udemy which is developed for students who want to learn the core principles of machine learning and statistics. Master statistics & machine learning: intuition, math, code online course by Udemy focuses on explaining how to utilize tools such as Python, Octave, and MATLAB for machine learning and mathematical operations, including statistics.

    Master statistics & machine learning: intuition, math, and code online classes aim to teach the concepts of statistics and machine learning which act as the foundational principles of A.I. and business intelligence. This course involves more than 38.5 hours of prerecorded lectures, as well as four downloadable resources and three articles on topics like correlation, regression, clustering, classification, ANOVA, black box statistics, descriptive statistics, predictive analysis, signal detection theory, probability theory, data cleaning, data visualization, and data normalization.

    The highlights

    • Certificate of completion
    • Self-paced course
    • 38.5 hours of pre-recorded video content
    • 3 articles 
    • 4 downloadable resources

    Program offerings

    • Online course
    • Learning resources. 30-day money-back guarantee
    • Unlimited access
    • Accessible on mobile devices and tv

    Course and certificate fees

    Fees information
    ₹ 3,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    What you will learn

    Mathematical skill Machine learning Knowledge of python Knowledge of matlab Knowledge of data visualization

    After completing the Master statistics & machine learning: intuition, math, code online certification, students will develop an understanding of the functionalities of Python, MATLAB, and Octave for activities like math, statistics, and machine learning. Students will learn about predictive analysis, clustering, data cleaning, classification, data normalization, and data visualization, among other machine learning techniques. Students will learn about ANOVA, correlation, T-tests, regression, and hypothesis testing procedures. Students will learn about statistical principles such as black box statistics, descriptive statistics, and inferential statistics.

    The syllabus

    Introductions

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

    Math prerequisites

    • Should you memorize statistical formulas?
    • Arithmetic and exponents
    • Scientific notation
    • Summation notation
    • Absolute value
    • Natural exponent and logarithm
    • The logistic function
    • Rank and tied-rank

    Important: Download course materials

    • Download materials for the entire course!

    What are (is?) data?

    • Is "data" singular or plural?!?!!?!
    • Where do data come from and what do they mean?
    • Types of data: categorical, numerical, etc
    • Code: representing types of data on computers
    • Sample vs. population data
    • Samples, case reports, and anecdotes
    • The ethics of making up data

    Visualizing data

    • Bar plots
    • Code: bar plots
    • Box-and-whisker plots
    • Code: box plots
    • "Unsupervised learning": Boxplots of normal and uniform noise
    • Histograms
    • Code: histograms
    • "Unsupervised learning": Histogram proportion
    • Pie charts
    • Code: pie charts
    • When to use lines instead of bars
    • Linear vs. logarithmic axis scaling
    • Code: line plots
    • "Unsupervised learning": log-scaled plots

    Descriptive statistics

    • Descriptive vs. inferential statistics
    • Accuracy, precision, resolution
    • Data distributions
    • Code: data from different distributions
    • "Unsupervised learning": histograms of distributions
    • The beauty and simplicity of Normal
    • Measures of central tendency (mean)
    • Measures of central tendency (median, mode)
    • Code: computing central tendency
    • "Unsupervised learning": central tendencies with outliers
    • Measures of dispersion (variance, standard deviation)
    • Code: Computing dispersion
    • Interquartile range (IQR)
    • Code: IQR
    • QQ plots
    • Code: QQ plots
    • Statistical "moments"
    • Histograms part 2: Number of bins
    • Code: Histogram bins
    • Violin plots
    • Code: violin plots
    • "Unsupervised learning": asymmetric violin plots
    • Shannon entropy
    • Code: entropy
    • "Unsupervised learning": entropy and number of bins

    Data normalizations and outliers

    • Garbage in, garbage out (GIGO)
    • Z-score standardization
    • Code: z-score
    • Min-max scaling
    • Code: min-max scaling
    • "Unsupervised learning": Invert the min-max scaling
    • What are outliers and why are they dangerous?
    • Removing outliers: z-score method
    • The modified z-score method
    • Code: z-score for outlier removal
    • "Unsupervised learning": z vs. modified-z
    • Multivariate outlier detection
    • Code: Euclidean distance for outlier removal
    • Removing outliers by data trimming
    • Code: Data trimming to remove outliers
    • Non-parametric solutions to outliers
    • Nonlinear data transformations
    • An outlier lecture on personal accountability

    Probability theory

    • What is probability?
    • Probability vs. proportion
    • Computing probabilities
    • Code: compute probabilities
    • Probability and odds
    • "Unsupervised learning": probabilities of odds-space
    • Probability mass vs. density
    • Code: compute probability mass functions
    • Cumulative distribution functions
    • Code: cdfs and pdfs
    • "Unsupervised learning": cdf's for various distributions
    • Creating sample estimate distributions
    • Monte Carlo sampling
    • Sampling variability, noise, and other annoyances
    • Code: sampling variability
    • Expected value
    • Conditional probability
    • Code: conditional probabilities
    • Tree diagrams for conditional probabilities
    • The Law of Large Numbers
    • Code: Law of Large Numbers in action
    • The Central Limit Theorem
    • Code: the CLT in action
    • "Unsupervised learning": Averaging pairs of numbers

    Hypothesis testing

    • IVs, DVs, models, and other stats lingo
    • What is an hypothesis and how do you specify one?
    • Sample distributions under null and alternative hypotheses
    • P-values: definition, tails, and misinterpretations
    • P-z combinations that you should memorize
    • Degrees of freedom
    • Type 1 and Type 2 errors
    • Parametric vs. non-parametric tests
    • Multiple comparisons and Bonferroni correction
    • Statistical vs. theoretical vs. clinical significance
    • Cross-validation
    • Statistical significance vs. classification accuracy

    The t-test family

    • Purpose and interpretation of the t-test
    • One-sample t-test
    • Code: One-sample t-test
    • "Unsupervised learning": The role of variance
    • Two-samples t-test
    • Code: Two-samples t-test
    • "Unsupervised learning": Importance of N for t-test
    • Wilcoxon signed-rank (nonparametric t-test)
    • Code: Signed-rank test
    • Mann-Whitney U test (nonparametric t-test)
    • Code: Mann-Whitney U test
    • Permutation testing for t-test significance
    • Code: permutation testing
    • "Unsupervised learning": How many permutations?

    Confidence intervals on parameters

    • What are confidence intervals and why do we need them?
    • Computing confidence intervals via formula
    • Code: compute confidence intervals by formula
    • Confidence intervals via bootstrapping (resampling)
    • Code: bootstrapping confidence intervals
    • "Unsupervised learning:" Confidence intervals for variance
    • Misconceptions about confidence intervals

    Correlation

    • Motivation and description of correlation
    • Covariance and correlation: formulas
    • Code: correlation coefficient
    • Code: Simulate data with specified correlation
    • Correlation matrix
    • Code: correlation matrix
    • "Unsupervised learning": average correlation matrices
    • "Unsupervised learning": correlation to covariance matrix
    • Partial correlation
    • Code: partial correlation
    • The problem with Pearson
    • Nonparametric correlation: Spearman rank
    • Fisher-Z transformation for correlations
    • Code: Spearman correlation and Fisher-Z
    • "Unsupervised learning": Spearman correlation
    • "Unsupervised learning": confidence interval on correlation
    • Kendall's correlation for ordinal data
    • Code: Kendall correlation
    • "Unsupervised learning": Does Kendall vs. Pearson matter?
    • The subgroups correlation paradox
    • Cosine similarity
    • Code: Cosine similarity vs. Pearson correlation

    Analysis of Variance (ANOVA)

    • ANOVA intro, part1
    • ANOVA intro, part 2
    • Sum of squares
    • The F-test and the ANOVA table
    • The omnibus F-test and post-hoc comparisons
    • The two-way ANOVA
    • One-way ANOVA example
    • Code: One-way ANOVA (independent samples)
    • Code: One-way repeated-measures ANOVA
    • Two-way ANOVA example
    • Code: Two-way mixed ANOVA

    Regression

    • Introduction to GLM / regression
    • Least-squares solution to the GLM
    • Evaluating regression models: R2 and F
    • Simple regression
    • Code: simple regression
    • "Unsupervised learning": Compute R2 and F
    • Multiple regression
    • Standardizing regression coefficients
    • Code: Multiple regression
    • Polynomial regression models
    • Code: polynomial modeling
    • "Unsupervised learning": Polynomial design matrix
    • Logistic regression
    • Code: Logistic regression
    • Under- and over-fitting
    • "Unsupervised learning": Overfit data
    • Comparing "nested" models
    • What to do about missing data

    Statistical power and sample sizes

    • What is statistical power and why is it important?
    • Estimating statistical power and sample size
    • Compute power and sample size using G*Power

    Clustering and dimension-reduction

    • K-means clustering
    • Code: k-means clustering
    • "Unsupervised learning:" K-means and normalization
    • "Unsupervised learning:" K-means on a Gauss blur
    • Clustering via dbscan
    • Code: dbscan
    • "Unsupervised learning": dbscan vs. k-means
    • K-nearest neighbor classification
    • Code: KNN
    • Principal components analysis (PCA)
    • Code: PCA
    • "Unsupervised learning:" K-means on PC data
    • Independent components analysis (ICA)
    • Code: ICA

    Signal detection theory

    • The two perspectives of the world
    • d-prime
    • Code: d-prime
    • Response bias
    • Code: Response bias
    • F-score
    • Receiver operating characteristics (ROC)
    • Code: ROC curves
    • "Unsupervised learning": Make this plot look nicer!

    A real-world data journey

    • Note about the code for this section
    • Introduction
    • MATLAB: Import and clean the marriage data
    • MATLAB: Import the divorce data
    • MATLAB: More data visualizations
    • MATLAB: Inferential statistics
    • Python: Import and clean the marriage data
    • Python: Import the divorce data
    • Python: Inferential statistics
    • Take-home messages

    Bonus section

    • About deep learning
    • Bonus content

    Instructors

    Mr Mike X Cohen

    Mr Mike X Cohen
    Associate Professor
    Freelancer

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