How to Build a Custom Instruction Dataset for LLM Fine-Tuning
Learn how to build a custom instruction dataset for LLM fine-tuning, covering Alpaca, ShareGPT, and DPO formats, quality filtering, synthetic data...
Machine Learning
Every Machine Learning tutorial on Machine Learning Plus in one place: Machine Learning, Predictive Modeling, Deployment and Deep Learning. 56 guides with code you can read, copy and run.
Machine Learning 42
Build a Custom Scikit-Learn Regression Model: Step-by-Step Guide
Creating custom regressors in scikit-learn means building your own machine learning models that follow scikit-learn's API conventions, allowing them...
Complete Data Science and AI Roadmap by ML+
A structured roadmap to learn Data Science, AI, and ML from scratch. Covers foundations, Python, statistics, machine learning, deep learning, and...
Mutual information vs Cross Entropy
Understand the key differences between mutual information and cross-entropy. Learn how each measures relationships between probability distributions...
Bayesian Optimization for Hyperparameter Tuning – Clearly explained.
Bayesian Optimization is a method used for optimizing 'expensive-to-evaluate' functions, particularly useful in hyperparameter tuning for machine...
KL Divergence – What is it and mathematical details explained
At its core, KL (Kullback-Leibler) Divergence is a statistical measure that quantifies the dissimilarity between two probability distributions.
Probe Method – How to select features for ML models
The Probe method is a highly intuitive approach to feature selection. The idea is to introduce a random feature to the dataset and train a machine...
Cook’s Distance for Detecting Influential Observations
Cook’s distance is a measure computed to measure the influence exerted by each observation on the trained model. It is measured by building a...
How to detect outliers with z-score
Z score, also called as standard score, is used to scale the features in a dataset. It can also be used to detect outliers.
How to detect outliers using Z score?
Z score is one of the most important concepts in statistics. It is also called __standard score__. Typically it is used to scale the features for...
How to detect outliers using IQR and Boxplots?
Outliers are those specific data points that differ significantly from others. Let's understand how to identify them using IQR and Boxplots.
MICE imputation – How to predict missing values using machine learning in Python
MICE Imputation, short for 'Multiple Imputation by Chained Equation' is an advanced missing data imputation technique that uses multiple iterations...
Spline Interpolation – How to find the polynomial curve to interpolate missing values
Spline interpolation is a special type of interpolation where a piecewise lower order polynomial called spline is fitted to the datapoints.
Interpolation in Python – How to interpolate missing data, formula and approaches
Interpolation can be used to impute missing data. Let's see the formula and how to implement in Python.
Missing Data Imputation Approaches | How to handle missing values in Python
The quality of ML model results depend on the data provided. Missing values in data degrade the quality. Let's see how to use missing data imputation...
Exploratory Data Analysis (EDA) – How to do EDA for Machine Learning Problems using Python
Exploratory Data Analysis (EDA) is the step where you understand the data in detail: each variable through frequency counts and distribution plots...
ML Modeling – Problem statement and Data description
ML modeling is the step where machine learning is used to find patterns in data and use that learned knowledge to predict an outcome
AdaBoost – An Introduction to AdaBoost
Adaboost is one of the earliest implementations of the boosting algorithm. It forms the base of other boosting algorithms.
How to formulate machine learning problem
Let's understand how to define and formulate the machine learning problem (for predictive modeling) from a business problem.
Build your first ML project
Build your first machine learning project in Python from scratch. A beginner-friendly, step-by-step guide, no prior ML experience needed to get...
Setup Python environment for ML
Python is the most popular programming language used for AI and machine learning. Let’s see how to setup python environment for ML using anaconda.
Train Test Split – How to split data into train and test for validating machine learning models?
The train-test split technique is a way of evaluating the performance of machine learning models
Task Checklist for Almost Any Machine Learning Project
A cheat sheet of tasks and things to take care of for every end-to-end ML projects
Data Science Roadmap – How to become a Data Scientist? (6 month self study plan)
The Data Science Roadmap: a self-study guide to machine learning in about 6 months, with what to learn for data science, ML, AI and statistics, the...
Why learn the math behind Machine Learning and AI?
Why understanding the math behind ML algorithms matters even when libraries exist. Learn how mathematical intuition helps with debugging, tuning, and...
Mistakes programmers make when starting machine learning
I want to discuss some of the common mistakes that programmers make when starting to learn machine learning.
Machine Learning Use Cases – The Big List of Real World Applications by Vertical and Industry
The use cases of machine learning to real world problems keeps growing as ML/AI sees increased adoption across industries. Let's take of tour of the...
Main Pitfalls in Machine Learning Projects
Not all Data Science projects that get kicked-off see the light of day. Let's understand the common reasons why machine learning projects may fail.
Regression Coefficient Formula
Understand the formula behind linear regression coefficients (alpha and beta). Step-by-step derivation with examples for simple linear regression.
Linear Regression in Machine Learning – Clearly Explained
Let's understand what linear regression is all about from a non-technical perspective, before we get into the details, we will first understand from...
Nebullvm – Tutorials and benchmarks on Nebullvm, the open-source deep learning inference accelerator
Explore Nebullvm, an open-source library that optimizes deep learning models to run 5-20x faster. Includes installation, benchmarks, and supported...
Simulated Annealing Algorithm Explained from Scratch (Python)
Simulated annealing algorithm is a global search optimization algorithm that is inspired by the annealing technique in metallurgy. Understand the...
An Introduction to Gradient Boosting Decision Trees
Learn how Gradient Boosting builds strong predictors by combining many weak learners sequentially. Understand the algorithm, math, and how to prevent...
Bias Variance Tradeoff – Clearly Explained
Bias Variance Tradeoff represents a machine learning model's performance based on how accurate it is and how well it generalizes on new dataset
Gradient Boosting – A Concise Introduction from Scratch
Gradient boosting works by building weak prediction models sequentially where each model tries to predict the error left over by the previous model.
Portfolio Optimization with Python using Efficient Frontier with Practical Examples
Portfolio optimization in finance is the process of creating a portfolio of assets, which maximizes return and minimizes risk.
Principal Component Analysis – How PCA algorithms works, the concept, math and implementation
Understand PCA, the math, concept, and Python implementation. Learn how Principal Component Analysis reduces dimensions while preserving maximum...
Feature Selection – Ten Effective Techniques with Examples
Discover 10 powerful feature selection techniques in R including Boruta, Lasso, stepwise selection, and variable importance to build better...
Caret Package – A Practical Guide to Machine Learning in R
Using caret package, you can build all sorts of machine learning models. In this tutorial, I explain the core features of the caret package and walk...
Evaluation Metrics for Classification Models – How to measure performance of machine learning models?
Computing just the accuracy to evaluate a classification model is not enough. This tutorial shows how to build and interpret the evaluation metrics.
Logistic Regression – A Complete Tutorial With Examples in R
Learn the concepts behind logistic regression, its purpose and how it works. This is a simplified tutorial with example codes in R. Logistic...
Complete Introduction to Linear Regression in R
Learn how to implement linear regression in R, its purpose, when to use and how to interpret the results of linear regression, such as R-Squared, P...
Predictive Modeling 2
K-Means Clustering Algorithm from Scratch
Learn the K-Means clustering algorithm from scratch. Covers the math, step-by-step implementation in Python, the Elbow method, and real-world...
How Naive Bayes Algorithm Works? (with example and full code)
Understand how the Naive Bayes algorithm works with a step-by-step example. Covers Bayes Theorem, Laplace correction, Gaussian Naive Bayes, and full...
Deployment 7
CUDA Programming: Do Large scale parallel computing in GPU from Scratch
Learn CUDA Programming to implement large scale parallel computing in GPU from Scratch. Understand concepts and underlying principles.
conda list environments – How to view all the virtual environments present in conda?
In this one, let's see how to view a list of all virtual environments in conda. Conda is a popular package management system that allows you to...
conda delete environment – How to remove a conda environment and all the associated packages?
Let's see how to delete conda environment. If you no longer need an environment, you can delete it to free up disk space and simplify your...
Population Stability Index (PSI)
Learn what Population Stability Index (PSI) is and how to use it for model monitoring. Understand the formula, interpretation, and Python...
Feature selection using FRUFS and VevestaX
FRUFS is model agnostic and is unsupervised, which means that Y does not have a role to play in identifying the features importance.
Deploy ML model in AWS Ec2 – Complete no-step-missed guide
understand the steps to deploy your ML model app in Amazon EC2 service. This guide is meant to serve as a walk through with full explanation of how...
Conda create environment and everything you need to know to manage conda virtual environment
Learn how to create, manage, and delete conda virtual environments. Covers conda create, activate, deactivate, list, clone, and best practices for...
Deep Learning 5
Fine-Tuning LLMs with LoRA and QLoRA in Python, A Complete Guide
Fine-tune LLMs with LoRA and QLoRA in Python. Complete guide covering memory math, PEFT setup, 4-bit QLoRA, adapter merging, and common mistakes...
How to Fine-Tune LLMs with LoRA in Python, A Complete Guide
Learn how to fine-tune large language models with LoRA in Python using PEFT and TRL, covers LoraConfig, QLoRA, SFTTrainer, model merging, and common...
How to implement Linear Regression in TensorFlow
Learn how to implement a simple linear regression in Tensorflow 2.0 using the Gradient Tape API very clearly.
How to use tf.function to speed up Python code in Tensorflow
tf.function is a decorator function provided by Tensorflow 2.0 that converts regular python code to a callable Tensorflow graph function, which is...
TensorFlow vs PyTorch – A Detailed Comparison
Compare the deep learning frameworks: Tensorflow vs Pytorch. We will go into the details behind how TensorFlow 1.x, TensorFlow 2.0 and PyTorch...