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AI Enthusiasts with writings covering AI, Data Science, and Freelancing

Study Notes: Python Data Structures and Algorithms

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Overview

Stacks are popular, linear data structures — or more abstractly a sequential collection. There are two principle operations involved with stack; 1) the addition of elements, also known as push 2) the removal of elements, also known as pop. Each operation can only be performed at the end of the sequence, thus the operations are performed in a Last In First Out (LIFO) manner. This is to illustrate that the last element added to the stack will be the first one to be removed.


Either ML Can’t Solve The Problem or it’s Simply A Bad Decision

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Machine Learning can solve many problems we face. Inspiration may be derived from the several instances in which ML has thrived, for example, fraud detection, smart assistants, navigation prediction, and many more. On the other side of the coin exists several instances where Machine Learning should be left alone, completely. These instances typically fall into two categories; 1) ML can’t solve the problem 2) It’s a bad decision to use ML. In these situations, other solutions which may be rule-based or use heuristics can be much better alternatives.

Here are 5 instances in which ML should absolutely be left alone:

#1 It’s Unethical


Although, It’s Not Always The Optimal Solution

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Machine Learning projects fail often. In 2018, Gartner reported that 85% of Machine Learning (ML) projects fail. Since then, practitioners have been constantly recycling this stat to highlight to various stakeholders [including practitioners] that ML is not a magic tool that can be used to solve all of mankind's problems. But even for problems Machine Learning can solve, it’s not always the optimal way to solve a problem. To understand the type of problems we may want to use ML to solve, we must take a step back and understand what ML is:

Machine learning is an approach to learn…


Study Notes: Python Data Structures and Algorithms

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Overview

The Array Sequence data structure is the most commonly used across all languages. It’s also the simplest data structure that may be defined as a collection of elements, each stored at contiguous memory locations and each data element can be accessed at random using its index number.

Why Does Arrays Exist?

Arrays are homogeneous data structures, meaning it allows us to hold several data items of the same type (i.e. numbers, strings, booleans, characters, objects, etc.). Thus, once the type of value that our array is going to hold is defined, all the elements must be of that type. …


Study Notes: Creating Value With Machine Learning

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Many Machine Learning Engineers make the mistake of prolonging the modeling and evaluation learning stage. Several courses exist to teach about various models and modeling techniques, but fail to deliver on what is most important to businesses. How will the model be used in production? That’s it. Machine Learning is supposed to add value so knowing how your model will do such is important. Unfortunately, learning how to use Machine Learning to build valuable products is probably the most boring part of the process, but it is what will make you indispensable once you’ve grasped it.

What is Machine Learning system Design?

Moving past the research…


Overfitting, Regularization, and Complex Models

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When a machine learning model performs poorly, it is either because it has underfitted or overfitted the training data. Underfitting is when the learned hypothesis is unable to accurately capture the relationship between input and output features — this results in bad performance on the training data and test data. If we only consider the model, a good solution to underfitting models would be to use a different model that can learn complex relationships.

In contrast, overfitting is when the learned hypothesis fits the training data so well that it performs poorly on unseen instances — we say the model…


The Relative Time Taken For An Algorithm To Run

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Data structures & Algorithms (DSA) play an important role in the hiring process of the world’s largest technology firms. Companies such as Google and Facebook like to test candidates on their knowledge of various algorithms and their efficiency, which is where Big O notation comes into play. Good knowledge of Big O notation is not only beneficial to software developers, but also to Machine Learning engineers.

When dealing with small datasets, how fast an algorithm runs may not be much of an issue. However, when the dataset is large, the number of operations an algorithm takes could be the difference…


Building Stronger Performing Machine Learning Models

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“Teamwork makes the dream work”. This popular quote is the strange saying I use to recall why we may prefer an ensemble over an individual predictor. Informally, an ensemble is a name used to describe a combination of multiple predictors being used as an individual predictor. This combined quality results in an extremely powerful individual predictor, hence why these models regularly appear in the top solutions of machine learning competitions.

The predictive performance of an ensemble is usually better than that of any one of the constituent algorithms individually. However, some tweaks ought to be made in order to attain…


Your identity seeks congruence

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My uncle has been a smoker for most of his life. He’d smoke when he’s hot, when he’s cold, when he’s tired, and when he’s energized. I felt bad for him at times because he tried so hard to stop on a number of occasions, but he just could never shake the habit.

One day, he woke up with severe pains in the side of his tummy. At first, it was dismissed as “just one of those things”, however after a week of constant kicking in his stomach and feeling poorly, he knew something was up. …


3 Traits I’ve Noticed

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Whenever I want to do something, I look for the best in the field and seek out the price to pay to reach their level. For instance, when I wanted to improve my dribbling skills in Football, I went to the GOAT (Greatest of all Time), Lionel Messi. My study didn’t only consist of drooling over his God-like abilities with a Football, but also a deep dive into understanding: 1) his thought process so I understand when he chooses his moments to dribble or pass and 2) how he got that good at dribbling the first place. …

Kurtis Pykes

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