Machine Learning From Scratch

“Machine Learning From Scratch” delves into the fundamental concepts of building machine learning models without relying on pre-built libraries or frameworks. The content covers topics such as data augmentation, deterministic bridge engineering, and the challenges of generative AI in enterprise settings. It explores the importance of understanding and processing big data dynamically, emphasizing the need for responsive and intelligent systems. The document also discusses the significance of context, precision, and data pipelines in enhancing the performance and reliability of machine learning applications. Overall, it provides insights into the core principles and technologies essential for developing robust machine learning solutions.

Decision Tree Explained (Classification)

We explore Decision Trees for classification in an intuitive way. No math will be hidden, and it will be simplified for an easy understanding.

Deploy Your Machine Learning Model For $5/Month

Machine Learning From Scratch

Is your Machine Learning project on a budget, and does it only need CPU power? Luckily, we have got you covered in this article, where we show you the necessary steps to deploy a model in a simple and...

Multiple Linear Regression: Explained, Coded & Special Cases

Machine Learning From Scratch

We explore the math and code for multiple linear regression, along with the two special cases: simple linear regression and polynomial regression.

Inverting A Matrix: Gaussian Elimination & Row Echelon Form

Machine Learning From Scratch

Machine Learning algorithms make use of matrix inverses, but understanding how to invert a matrix is not easy. We walk through the theory and show you how to invert a matrix by example.

Using Deep Learning To Take On COVID-19

Machine Learning From Scratch

Attempting and experimenting with identifying COVID-19 from X-Ray images, by using VGG19 with augmentation practices.

Neural Network From Scratch with NumPy and MNIST

Machine Learning From Scratch

Learn the fundamentals of how you can build neural networks without the help of the deep learning frameworks, and instead by using NumPy.

Introduction To Machine Learning Deployment Using Docker and Kubernetes

Machine Learning From Scratch

Walkthrough of deploying a Random Forest Model on a Toy Dataset. A Dockerfile, along with Deployment and Service YAML files are provided and explained.

Stack Machine Learning Models - Get Better Results

Machine Learning From Scratch

Model stacking will improve your results; but first, discover how it works and learn how to apply model stacking in Python.

<h1 class="post-full-title">Random Forest Explained</h1>

Machine Learning From Scratch

How to do Random Forest in Python? A complete code example is provided. I explain the algorithm, while explaining what lies behind the algorithm.

<h1 class="post-full-title">How to do Linear Regression and Logistic Regression in Machine Learning?</h1>

Machine Learning From Scratch

Wondering how Linear Regression or Logistic Regression works in Machine Learning? Python code and a walkthrough of both concepts are available here.

<h1 class="post-full-title">Statistics Basics - Variance and Standard Deviation</h1>

Machine Learning From Scratch

This part is all about statistics, which is undoubtedly using all over Machine Learning. I explain the terms and give you a function to fill missing data.

<h1 class="post-full-title">Probability Distributions and Maximum Likelihood Estimation (MLE)</h1>

Machine Learning From Scratch

What is probability distributions? How to calculate, visualize and the intuition behind them. What is Maximum Likelihood Estimation (MLE)? Formula for MLE.