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Lec 35 Logistic regression

Data Analysis for Biologists Intro Video

Lec 1 Rules of probability

Lec 2 Discrete probability distribution

Lec 3 Continuous probability distribution

Lec 4 Moments mean and variance

Lec 5 Moments variance and covariance

Lec 6 Bayes theorem and likelihood

Lec 7 Concept of statistical tests

Lec 8 Vector and vector operations

Lec 9 Matrix and matrix operations

Lec 10 Determinant and Inverse of a matrix

Lec 11 Eigenvalue and eigenvector

Lec 12 Linear system of equations

Lec 13 Singular value decomposition

Lec 14 Getting ready with R

Lec 15 Algebraic and logical operations in R

Lec 16 Reading and writing data

Lec 17 Statistics using R – descriptive statistics

Lec 18 Statistics using R – t test and ANOVA

Lec 19 Linear algebra using R

Lec 20 Scatter plot Line plot Bar plot

Lec 21 Histogram Box plot

Lec 22 Heatmap and Volcano plot

Lec 23 Network visualization

Lec 24 Data visualization using ggplot2 I

Lec 25 Data visualization using ggplo2 II

Lec 26 Correlations

Lec 27 Linear regression I

Lec 28 Linear regression II

Lec 29 Linear regression using R

Lec 30 Multiple linear regression

Lec 31 Multiple linear regression using R

Lec 32 Nonlinear regression

Lec 33 Nonlinear regression using R

Lec 34 Clustering and classification

Lec 35 Logistic regression

Lec 36 Logistic regression using R

Lec 37 Distance mesaures for clustering

Lec 38 k means clustering

Lec 39 k means clustering using R

Linear Algebra for Computer Scientists

Insertion Sort

Bubble Sort

GCSE Computer Science

Random Access Memory

Binary Trees

Bitwise Operators

Introduction to Stacks Data Structures

Organization Theory Structure and Design

Handling Large Scale Unit Level Data

Introduction to Machine Learning

Applied Linear Algebra Vectors Matrices

Artificial Intelligence beginners

Machine Learning Autumn 2018

Robotics and Autonomous Systems

Natural Language Understanding