I had opted for the base spec version — literally the cheapest M1 MacBook you can buy. That meant it arrived with an 8-c

Kevin Horton is a photographer, student, modest book-worm, and wanna-be web developer with a new-found love for writing. He writes helpful words about creativity, productivity, and the enjoyably simple life.

One of the much better solutions is to use Dynamic Planning when possible, which is probably the best way to solve a problem that can be divided into sub-problems. One of my previous articles has illustrated the power of Dynamic Planning.

Linear Regression: where the goal will be to find the function that will map the input to a corresponding target value, which will typically be a real number. Topics covered are model fitting by parameter estimation (linear regression) and by parameter integration (Bayesian regression).

I personally believe that the best value that brings this book is that it links greatly the mathematical concepts explained in the first part of the book to the Machine Learning algorithms detailed in the second part. If you ever struggled understanding concepts like Gradient Descent, you won’t need to worry anymore about it after studying Mathematics for Machine Learning.

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ample, describes two studies of people with chronic Crohn’s. Half were given the drug and half got a placebo. That study showed a decrease in symptoms in 10 of 11 subjects using cannabis, compared with just four of 10 on the placebo. But when the researchers did a follow-up study using low-dose CBD, they saw no effect in the patients.

After reading/studying it for the first time, it is a great tool to have at the side and I encourage you to come back to it and refresh the related concepts every time that you face a Machine Learning challenge. It will give you a great perspective on how to tackle blocking points and definitely ease up your path in the mid-long term.

I am not going to lie, this is a dense and detailed book. You will have to invest time and effort to go through it and deeply understand its topics. But I assure you that it will be worth it. Do not give up, take your time, and make sure that you internalize its lessons. It will definitely pay up greatly in your understanding and application of Machine Learning.

However, in this article, I’m going to introduce another technique in Python that can be utilised as an alternative to the recursive function. It won’t outperform Dynamic Planning, but much easier in term of thinking. In other words, we may sometimes be struggling to make Dynamic Planning works because of the abstraction of the ideas, but it will be much easier to use closure.

Classification: Similarly to regression, classification also belongs to Supervised Learning, and it is studied through the lens of Support Vector Machines. Unlike regression, target values are typically integers, instead of real values.

The second part will focus on the four pillars of Machine Learning after defining the three components of machine learning (data, models, and parameter estimation) mathematically. These four pillars are:

Density Estimation: The objective will be to find a probability distribution that describes the input data. The focus will be on Gaussian Mixture models to do this and it also belongs to Unsupervised Learning.

I was such a programmer who likes recursive functions very much before, simply because it is very cool and can be used to show off my programming skills and intelligence. However, in most of the circumstances, recursive functions have very high complexity that we should avoid using.

Kevin Horton is a photographer, student, modest book-worm, and wanna-be web developer with a new-found love for writing. He writes helpful words about creativity, productivity, and the enjoyably simple life.

Dimensionality Reduction: using principal component analysis, the goal will be to find a lower-dimensional representation of the input data. This will allow an easier analysis. It is important to notice that in these methods there arent target values. Dimensionality reduction belongs to the set of techniques of the so-called Unsupervised Learning.

After reading/studying it for the first time, it is a great tool to have at the side and I encourage you to come back to it and refresh the related concepts every time that you face a Machine Learning challenge. It will give you a great perspective on how to tackle blocking points and definitely ease up your path in the mid-long term.