Applied ML / 25 April 2019 Random Forest with GridSearchCV in Python and Decision Trees explained Perhaps one of the most common algorithms in Kaggle competitions, and machine learning in general, is the random forest algorithm. It performs well in almost all scenarios and is mostly impossible to overfit,
Applied ML / 14 April 2019 How to do Linear Regression and Logistic Regression in Machine Learning? Wondering how Linear Regression or Logistic Regression works in Machine Learning? Python code and a walkthrough of both concepts are available here.
ML Theory / 31 March 2019 Machine Learning Introduction 7: Probability Distributions and Maximum Likelihood Estimation (MLE) Probability distributions is all about how we can represent the distributions of probabilities of data.There is fundamentally a formula for each distribution, but I like to visualize the sort of distribution of
ML Theory / 24 March 2019 Machine Learning Introduction 6: Probability Theory This post is where you need to listen and really learn the fundamentals. All modern approaches to Machine Learning uses probability theory. AlphaStar is an example, where DeepMind made many different AIs using
ML Theory / 16 March 2019 Machine Learning Introduction 5: Measures of distance and similarity or dissimilarity Anytime we do machine learning, measures of distance can be important. An example comes to mind, when a machine has to distinguish between two object, say a pig or cow. Well then it
ML Theory / 9 March 2019 Machine Learning Introduction 4: Statistics We want to be able to say something about our data using some statistical measures, because it is important information. There are many ways to use statistics in machine learning, and the mean,
ML Theory / 4 March 2019 Machine Learning Introduction 3: Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) This post will keep us on the level of Machine Learning Introduction, but it will try to give a clearer image of what happens behind, i.e. what PCA and SVD does.Psst.
ML Theory / 24 February 2019 Machine Learning Introduction 2: Data and Data Manipulation What is data, a dataset, and how do we describe it?What we say that data is something, which is held in a dataset, that just contains N observations (rows) and M features
ML Theory / 12 February 2019 Machine Learning Introduction 1: Introductory terms This will be a series of introduction to Machine Learning, starting from the very foundation of machine learning and then moving into topics within the field. I will cover datasets, principal component analysis,
Linear Algebra / 5 February 2019 Cheat Sheet for Linear Algebra This is a continuously updated cheat sheet for the Linear Algebra I covered, as well as for future posts. Currently included are intuition, notation and formulas. Notation like vector, scalar, matrix, m x n, basis vectors, mapping in space, determinant, cross product, dot product and much more.
Linear Algebra / 28 January 2019 Linear Algebra Basics 4: Determinant, Cross Product and Dot Product I visualized the determinant, cross product and dot product can be hard. Come read the intuitive way of understanding these three pieces from Linear Algebra.
Linear Algebra / 6 January 2019 Linear Algebra Basics 3: Linear Transformations and Matrix Multiplication What is Linear Transformations? What is Vector Space? How to do Vector Multiplication (Matrix Multiplication)? Conceptualizing a Linear Transformation is also key to understanding a transformation, so ..
Linear Algebra / 29 December 2018 Linear Algebra Basics 2: Basis Vectors, Span and Linear Combinations What are basis vectors? Why are they so important? What is Span and a Linear Combination? Come read, quick recap at the bottom.
Linear Algebra / 16 December 2018 Linear Algebra Basics 1: Vectors, Vector Addition and Scalars What is a vector? How to think about vectors? What is vector addition? What is a scalar? Come find the answers here.
