LLM Temperature, Top-P, and Top-K Explained, With Python Simulations
Build temperature, top-p, top-k, and min-p sampling from scratch in Python. Interactive code, probability visuals, and a per-task cheat sheet.
OpenAI API Python Tutorial, Chat Completions, Streaming & Error Handling
Learn to use the OpenAI API in Python. Master chat completions, streaming responses, error handling, retries, and async calls with runnable examples.
Build a Python AI Chatbot with Memory Using LangChain
Learn to build a Python AI chatbot with conversation memory using LangChain and LangGraph. Master 5 memory strategies, add a Streamlit UI, and...
Build a BPE Tokenizer from Scratch in Python, Step-by-Step Guide
Build a working BPE tokenizer in Python step by step. Learn how LLMs split text into tokens, implement byte pair encoding, and count tokens with...
101 Polars Exercises for Data Analysis (with Solutions)
Master Polars with 101 hands-on exercises and solutions, covering DataFrames, groupby, joins, window functions, lazy eval, and more.
Zero-Shot vs Few-Shot Prompting: Complete Guide
Build a text classifier that hits 90%+ accuracy using only prompt engineering. Learn zero-shot, one-shot, and few-shot prompting with hands-on Python...
Transformer Attention from Scratch in NumPy (Python)
Build transformer attention from scratch in NumPy with runnable code. Scaled dot-product, multi-head attention, causal masking, and heatmaps step by...
Train a Custom Tokenizer with HuggingFace (Python)
Learn to train custom BPE and WordPiece tokenizers with HuggingFace for medical, legal, and domain-specific NLP. Includes evaluation metrics and code.
tiktoken vs HuggingFace Tokenizers: Benchmark Guide
Benchmark tiktoken vs HuggingFace Tokenizers on speed, vocabulary, and encoding. Runnable Python code, migration guide, and decision framework for...
LLM Structured Output: JSON Mode & Pydantic Guide
Learn LLM structured output in Python with 3 methods: OpenAI JSON schema, Claude tool extraction, and Instructor. Build a type-safe invoice parser...
Mamba & State Space Models: Build a Mini SSM Layer
Build a Mamba SSM layer from scratch in NumPy. Learn selective state spaces, implement selective scan, and benchmark SSM vs attention scaling with...
Speculative Decoding: Faster LLM Inference (Python)
Build a speculative decoding simulator in Python. Learn the draft-verify algorithm, measure acceptance rates, and understand when it speeds up LLM...
RLHF & DPO Explained: Simulate Alignment in Python
Build a reward model, PPO loop, and DPO training from scratch in NumPy. Compare RLHF vs DPO side-by-side with runnable code.
LLM API Retry & Fallback: Build a Resilient Client
Build a resilient LLM client in Python with retry, fallback chains, circuit breakers, and rate limiting, pure Python, runnable code, no SDKs needed.
Prompt Engineering Tutorial for Beginners (2026)
Master prompt engineering basics with Python. Learn the Role-Task-Format framework, zero-shot prompting, and build a testing harness that measures...
Positional Embeddings: RoPE & ALiBi Explained (Python)
Build sinusoidal, RoPE, and ALiBi positional embeddings from scratch in NumPy. Runnable code, heatmaps, and a clear comparison of all three schemes.
OpenAI Function Calling Tutorial in Python (2026)
Learn OpenAI function calling in Python with 3 working tools. Build the tool-use loop, handle parallel calls, and design schemas using raw HTTP...
OpenAI Batch API: Process 10K Prompts at 50% Cost
Master the OpenAI Batch API in Python: build a reusable pipeline for 10,000+ prompts at 50% cost with JSONL formatting, progress polling, and error...
OpenAI Chat Completions API: Complete Python Guide
Master the OpenAI API in Python with raw HTTP requests. Learn chat completions, streaming, parameters, error handling, retries, and cost tracking...
Ollama Tutorial: Run LLMs Locally (Llama, Mistral)
Learn to run LLMs locally with Ollama. Install Llama, Mistral, and DeepSeek, use the OpenAI-compatible Python API, and build a local-to-cloud...
Multimodal AI Tutorial: GPT-4o Vision & Audio API
Learn multimodal AI in Python with GPT-4o, Claude, and Gemini vision APIs. Build image classification, chart analysis, receipt OCR, and audio...
Build a Multi-Provider LLM Toolkit (Python Project)
Build a zero-dep LLM toolkit in Python, unified client for OpenAI, Claude, Gemini, Ollama with cost tracking, retry, streaming, and structured output.
LLM API Router: Groq, Together AI & OpenRouter
Build a multi-provider LLM router in Python with cost-based routing, latency tracking, and automatic fallbacks across Groq, Together AI, and...
Mixture of Experts (MoE): Build a Router in Python
Build a Mixture of Experts (MoE) layer in Python with NumPy. Router, top-k gating, load balancing, and expert networks, with runnable code and...
MHA vs GQA vs MQA: Attention & KV Cache Guide
Build MHA, GQA, and MQA attention from scratch in NumPy. Calculate KV cache VRAM for Llama 3 70B, Mistral 7B, and any model with a reusable...
Lost-in-the-Middle: Fix LLM Position Bias (Python)
Measure and fix LLM position bias with Python. Build a needle-in-haystack test, plot the U-shaped curve, and implement position reordering.
Long-Context LLMs: Sliding Window Summarization Guide
Build a sliding window summarization pipeline in Python that handles documents exceeding LLM context windows. Map-reduce and recursive strategies...
LLM Streaming Tutorial: SSE in Python Step-by-Step
Stream LLM tokens from OpenAI, Claude, and Gemini in Python using SSE and async generators. Includes FastAPI server, backpressure handling, and...
LLM Scaling Laws: Model Comparison Dashboard (2026)
Learn LLM scaling laws and compare GPT, Claude, Gemini, Llama, Mistral, and DeepSeek on benchmarks, pricing, and context windows with interactive...
Temperature, Top-p & Top-k in LLMs Explained (Python)
Master LLM temperature, top-k, and top-p with interactive Python simulations. Runnable code, exercises, and a sampling playground to build real...
GPT vs Claude vs Gemini: Python Benchmark (2026)
Build a Python benchmarking harness to compare GPT-4o, Claude, Gemini, and Llama on quality, latency, and cost with LLM-as-judge and radar charts.
LLM Evaluation: Build an LLM-as-Judge Pipeline
Build an LLM evaluation pipeline in Python with LLM-as-judge scoring, rubric design, A/B testing, and regression alerts. Runnable code examples...
Build an LLM Benchmarking Platform (Python Project)
Build an LLM benchmarking platform in Python from scratch. Define test suites, compare providers with raw HTTP, score with LLM-as-judge, and generate...
LLM API Pricing Guide: Compare & Optimize Costs
Learn to count LLM tokens with tiktoken, compare API pricing across OpenAI, Claude, and Gemini (March 2026), and cut costs by 90% with 10 proven...
OpenAI, Claude & Gemini API Tutorial in Python (2026)
Learn to call OpenAI, Claude, and Gemini APIs from Python in 15 minutes. Includes code examples, error handling, streaming, and a unified wrapper.
KV Cache Explained: Build a Cache Manager in Python
Learn how KV caching works in LLMs, calculate VRAM usage for real models, and build a PagedAttention-style cache manager with token eviction in pure...
Groq vs Fireworks vs Together AI: Speed Benchmark
Build a Python benchmark harness comparing Groq, Fireworks, Together AI, and Replicate on latency, throughput, and cost with runnable code.
Hugging Face Inference API Tutorial in Python
Master Hugging Face inference in 20 minutes. Run LLMs locally with Pipeline API or serverless via HTTP, with Python examples you can copy and run.
Gemini API Tutorial: Multimodal AI in Python
Build a multimodal document analyzer with the Google Gemini API in Python. Analyze images, PDFs, and text with structured JSON output, using raw HTTP...
LLM Context Windows Explained: Token Budget Guide
Learn how LLM context windows work, count tokens with tiktoken, estimate API costs, and build a Python token budget manager that allocates context...
Constitutional AI: Build a Self-Critique Loop (Python)
Build a Constitutional AI safety loop in Python. Generate, critique, and revise LLM responses with raw API calls, no fine-tuning needed.
How LLM Tokenization Works: Build a BPE Tokenizer
Build a working BPE tokenizer in Python, learn how text becomes tokens, and use tiktoken to count tokens and estimate LLM API costs across providers.
Claude API Tutorial: Messages, Tools & Streaming
Master the Claude API with raw HTTP, messages, streaming, tool use, extended thinking, and prompt caching with runnable Python code examples.
101 Pandas Exercises for Data Analysis (Interactive)
101 interactive pandas exercises with solutions. Edit and run every code block directly in your browser, no installation needed.
What Is LangGraph? How It Works and When to Use It
Discover what is LangGraph, how it compares to LangChain, and when graph-based orchestration is the right choice for building reliable AI agents.
Prompt Engineering Fundamentals, Reliable LLM Outputs
Apply prompt engineering fundamentals, zero-shot, few-shot, chain-of-thought, and structured output, to get consistent, reliable results from any LLM.
LangGraph Tool Calling: Build Agents That Take Action
Build LangGraph agents that call tools with @tool, bind_tools, and ToolNode. Step-by-step examples for web search, database queries, and API calls in...
LangGraph Subgraphs: Compose Reusable Workflows
Learn how to build modular LangGraph apps with subgraphs you can develop, test, and reuse across projects, with full code examples and patterns.
LangGraph Structured Output & Self-Correcting Agents
Build LangGraph agents that return validated, typed data using Pydantic models, with automatic retry loops that self-correct on validation failures.
LangGraph Streaming: Real-Time Agent Output Guide
Add real-time streaming to your LangGraph agents, pick the right stream mode, show tokens as they arrive, and build chat UIs that feel instant.
LangGraph State Management: TypedDict & Reducers
Master LangGraph state management with TypedDict schemas, reducer functions, and add_messages. Learn how nodes share data, merge updates, and track...
LangGraph ReAct Agent: Build from Scratch
Learn to build a ReAct agent from scratch in LangGraph with this hands-on guide, wire the think-act-observe loop, add tools, and debug agent cycles.
LangGraph RAG Agent: Self-Correcting Retrieval Pipeline
Build a RAG agent in LangGraph that routes questions, grades documents, detects hallucinations, and rewrites queries when results are poor. Full code...
LangGraph SQL Agent: Answer Business Questions
Step-by-step guide to building a LangGraph SQL agent that turns plain English into SQL, runs queries, retries on errors, and explains results clearly.
LangGraph Multi-Agent Research Assistant Project
Build a multi-agent research pipeline in LangGraph with planner, researcher, writer, and reviewer agents that turn any topic into a polished report.
LangGraph + FastAPI: Build a Full-Stack AI App
Build a full-stack AI app from scratch with FastAPI and LangGraph, streaming responses, saved chats, API key auth, and a live chat frontend in one...
LangGraph Document Processing Agent: Multi-Modal
Build a LangGraph agent that reads PDFs, images, and text, cross-checks facts across sources, and writes a clean JSON report, with full code...
LangGraph Customer Support Agent with Escalation
Learn to build a LangGraph customer support agent that classifies queries, searches a knowledge base, escalates to humans, and logs every step.
LangGraph Web Scraping Agent: Autonomous Pipeline
Learn to build a LangGraph pipeline that scrapes websites on its own, pulls structured data, handles pagination and errors, and writes analytical...
LangGraph Platform: Deploy Agents as APIs
Deploy LangGraph agents as scalable APIs with LangGraph Server, step-by-step guide covering SDK setup, streaming, background runs, and cloud hosting.
LangGraph Checkpointing: Save & Resume Graph State
Save and resume LangGraph agent state with checkpointers so your conversations survive crashes, restarts, and long breaks between sessions.
LangGraph Debugging with LangSmith Tracing Guide
Master LangGraph debugging with LangSmith tracing, follow step-by-step examples to inspect agent runs, tag traces, and monitor production performance.
LangGraph Multi-Agent: Supervisor, Swarm & Network
Build multi-agent AI systems in LangGraph using supervisor, swarm, and network patterns, with full code for each and a guide to choosing the right...
LangGraph Memory: Short-Term, Long-Term & Conversation
Build LangGraph agents with lasting memory using sliding windows, summaries, and cross-session stores to manage tokens and recall user context.
LangGraph Map-Reduce: Parallel Execution with Send API
Learn how to run LangGraph branches in parallel using the Send API and map-reduce pattern to process multiple items at once and merge results cleanly.
LangGraph Installation & Setup: Build Your First Graph
Complete your LangGraph installation setup in minutes, then build and run your first StateGraph that calls an LLM and returns a response.
LangGraph Human-in-the-Loop: Add Approval Steps
Learn to pause LangGraph agents for human review, accept edits, and resume safely with working interrupt and Command patterns.
LangGraph Nodes, Edges & State: Core Concepts Explained
Understand LangGraph nodes edges state and conditional routing through visual diagrams and runnable Python examples you can copy and extend.
LangGraph Error Handling: Retries & Fallback Strategies
Make your LangGraph agents production-ready with retry logic, fallback paths, and error tracking that keeps pipelines alive when things go wrong.
LangGraph Cycles & Recursion Limits: Control Agent Loops
Master LangGraph cycles and recursion limits, stop runaway agents, handle GraphRecursionError, and build clean exit strategies for your loops.
LangGraph Conditional Edges: Dynamic Routing Guide
Build LangGraph workflows that branch at runtime using conditional edges and routing functions. Route by LLM output, user input, or custom logic with...
LangGraph Code Generation Agent: Build & Run Python
Step-by-step guide to building a LangGraph agent that writes Python code, runs it safely, checks the output, and self-corrects until it works.
LangChain Crash Course, Chains, Models, and Output Parsers
Follow this hands-on LangChain tutorial to master chat models, prompt templates, output parsers, and LCEL chains with runnable Python examples.
Introduction to LLMs and the OpenAI API in Python
Start using the OpenAI API Python SDK today. Build your first LLM-powered app with runnable code for chat completions, streaming, and token...
Unsloth Fine-Tuning, Train LLMs 2x Faster with 70% Less Memory
Fine-tune LLMs with Unsloth, 2x faster training, 70% less GPU memory. Step-by-step guide with LoRA, QLoRA, code examples, and deployment tips.
How to Evaluate LLMs, Metrics, Benchmarks & Python Code
Learn LLM evaluation from scratch -- benchmarks, metrics (BLEU, ROUGE, perplexity), LLM-as-judge, and custom pipelines with runnable Python code.
How to Evaluate an LLM: Benchmarks, Metrics, and Practical Workflows
Evaluate your LLM using MMLU, MT-Bench, LLM-as-judge, and ROUGE. Covers lm-evaluation-harness, fine-tuned model comparison, and evaluation pitfalls...
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...
DPO (Direct Preference Optimization), A Simpler Alternative to RLHF
Align LLMs with human preferences using one loss function -- no reward model, no RL. Complete guide with derivation, PyTorch code, and DPO variants.
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...
How LLMs Work: Transformers Explained Step-by-Step
Learn how LLMs work step by step. Build an inference simulator in Python, tokenize, embed, compute attention, sample, and decode with runnable code...
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...
Build an AI Chatbot with Memory in Python (2026)
Build a Python AI chatbot with conversation memory that actually remembers. Raw HTTP tutorial with streaming, 3 hands-on exercises, and complete code...
Build Your First AI App with Python, Step-by-Step Tutorial
Build your first AI app with Python and the OpenAI API. Step-by-step tutorial covering chat completions, streaming, error handling, and cost control...
Understanding Confidence Intervals: A spelled out guide to clarify misconceptions
A clear guide to confidence intervals, what they really mean, common misconceptions, how to calculate them, and why they matter in statistical...
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...
Ridge Regression as MAP Estimation – Supporting notes
Explore the elegant connection between Ridge Regression and MAP estimation. Step-by-step derivation showing why the L2 penalty equals a Gaussian...
Optimizing RAG Chunk Size: Your Definitive Guide to Better Retrieval Accuracy
The optimal chunk size for RAG systems is usually 128 to 512 tokens: smaller chunks suit precise fact lookups, larger chunks keep more context for...
Maximum A Posteriori (MAP) Estimation – Clearly Explained
Maximum A Posteriori (MAP) estimation is a Bayesian method for finding the most likely parameter values given observed data and prior knowledge...
Relevant Segment Extraction (RSE) – Building better Context by assembling contiguous chunks for better RAG Performance
Relevant Segment Extraction (RSE) is a query-time post-processing technique that intelligently combines related text chunks into longer, coherent...
Ollama Tutorial: Your Guide to running LLMs Locally
A complete guide to Ollama, run LLMs like Llama 3, Mistral, and Gemma locally. Covers installation, model management, prompting, API usage, and...
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.
Mutual information vs Cross Entropy
Understand the key differences between mutual information and cross-entropy. Learn how each measures relationships between probability distributions...
F Statistic Formula – Explained
The F statistic is used in statistical hypothesis testing to determine if there are significant differences between group means. It is most commonly...
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...
Difference between Joint probability and Conditional probability
Joint probability and conditional probability are two concepts in probability theory that deal with the likelihood of events, but they are used in...
How to convert Python code to Cython (and speed up 100x)?
Using Cython, you can speed up existing Python code by an order of 100x or more. This is possible because Cython converts some of the Python code to...
How to convert Python to Cython inside Jupyter Notebooks?
Lets see how to convert Python to Cython inside Jupyter Notebooks?
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...
What is the difference between CROSS JOIN and INNER JOIN in SQL?
Let's understand the difference between CROSS JOIN and INNER JOIN with example
How to get a list of dates between two dates in SQL?
Let's see how to get a list of dates between two dates in SQL using an example. We will explore alternate solutions as well.
How to get records from one table that does not exist in another?
How to get records from one table that does not exist in another?
How to exclude a column using select * except column?
How to exclude a column using select * except column in SQL? Let's also see some work arounds.
How to access the “previous row” value in a SELECT statement in SQL?
Let's see how to access the 'previous row' value in a SELECT statement in SQL?
What is the difference between UNION and UNION ALL in SQL?
Let's understand the difference between UNION and UNION ALL with an example
How to use GROUP BY to concatenate strings in MySQL and SQL Server?
How to use GROUP BY to concatenate strings in MySQL and SQL Server?
How to find duplicate values in SQL Table?
Let's see how to find duplicate values in an SQL table.
How to return results only if value exists in SQL?
You have a table with a column where some rows contain NULL values or empty strings. You want to retrieve rows where this column has a value (i.e...
How to get top n results in each ‘group by’ group in SQL?
Let's see how to get top n results in each 'group by' group in SQL. You have a table with multiple records. For each distinct value in each 'group...
How to concatenate multiple rows into one field in MySQL?
Let's see how to concatenate multiple rows into one field in MySQL?
What is the difference between INNER JOIN, OUTER JOIN and FULL OUTER JOIN?
Understand the difference between INNER JOIN, OUTER JOIN and FULL OUTER JOIN with examples and compare the results.
How to select a random row using SQL?
Let's see how to select a random row from large tables efficiently using SQL
How to randomly select rows from a large table in MySQL super fast?
Let's see how to randomly select rows from a large table quick and efficiently in SQL
How to escape a single quote in SQL Server?
Let's look at how to escape a single quote in SQL Server, with a solution that is also reproducible in MySQL
How to efficiently convert rows to columns in SQL?
Learn how to efficiently convert rows to columns in SQL using PIVOT and CASE statements. Includes example problem, input table, and working solutions.
How to split values to multiple rows in SQL?
Let's see how to split values to multiple rows in SQL with an example problem and working solution.
How to remove duplicate rows in MySQL?
Given a table in MySQL, how do you remove duplicate rows? Let's see how to solve this with an example and solutions.
How to insert into a table or update if exists in MySQL?
Learn how to insert into a table or update if exists in MySQL and SQL Server. You need to calculate a running total in SQL Server and provide a...
How to get the rows which have the max value for a column for each group in another column in SQL?
Learn how to get the rows which have the max value for a column for each group in another column
How to do a FULL OUTER JOIN in MySQL?
Let's understand how to do a FULL OUTER JOIN in MySQL
How to concatenate text from multiple rows into a single text string in MySQL?
Learn How to concatenate text from multiple rows into a single text string in MySQL
How to calculate running total in SQL Server?
Let's see how to calculate running total in SQL Server
How to switch rows and columns in SQL?
How to switch rows and columns in SQL? Let's transpose so the rows become columns and columns as rows.
How to retrieve the last record in each group in SQL?
How to retrieve the last record in each group in SQL: the problem, the input table, the desired output and three solutions, each with an explanation...
How to get the top 1 row of each group in SQL?
How to get the top 1 row of each group in SQL: the problem, the input table, the desired output and three solutions, each with an explanation of how...
SQL Window Function Exercises and Solutions – Set 2
SQL window function exercises is designed to challenge your SQL muscle and help internalize data wrangling using window functions in SQL.
How to transpose columns to rows in SQL?
Let's see how to transpose columns to rows in SQL. That is, how to reshape the table so that certain columns of a table are moved to rows.
How to select only rows with max value on a column?
How to select only rows with max value on a column for each distinct value in another categorical column in MySQL?
How to select first row in each ‘Group By’ group?
How to select first row in each 'Group By' group? Let's understand with example problem and solutions.
SQL Window Functions Exercises – Practice to perfection
SQL window functions is considered a _'hard'_ concept in SQL. This set of exercises is designed to challenge your SQL muscle and help internalize...
SQL Window Functions – Made simple and intuitive
SQL window functions is one of the advanced concepts, understanding which will give you the ability to do complex data wrangling and transformations...
SQL Subquery – How to write subqueries using simple examples
SQL Subquery, also known as inner queries or nested queries, are used to query data from one or more tables and then used in another SQL statement.
SQL Tutorial – A Simple and Intuitive Guide to the Structured Query Language
SQL, short for Structured Query Language is a programming language used to communicate with databases and do various types of Data wrangling...
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.
Scrapy vs. Beautiful Soup: Which is better for web scraping?
Web scraping is the technique of extracting data from a specific website or web page. Let's understand how to use Scrapy for web scraping of Reddit...
install pip mac – How to install pip in MacOS?: A Comprehensive Guide
Pip is a widely used package manager for Python, allowing you to install and manage Python packages easily. In this blog post, we'll explore various...
add Python to PATH – How to add Python to the PATH environment variable in Windows?
Adding Python to the PATH environment variable in Windows allows you to run Python commands from any directory within the command prompt
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...
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...
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
How to reduce the memory size of Pandas Data frame
Let's see how to reduce the memory size of a pandas dataframe.
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...
Numpy.random.randint() in python
Numpy.random.randint is used to get random integers from low to high values. The low value is included while the high value is excluded while...
How to use numpy.random.uniform() in python.
The numpy.random.uniform() function is used to create an array with random samples from a uniform probability distribution of given low and high...
Numpy.sort() in python
The numpy.sort() function in python is used to sort the array along a specified axis.
numpy.median() – How to compute median in Python
numpy.median function is used to calculate the median of an array along a specific axis or multiple axes
How to Use Numpy Where Function?
The numpy.where() function is used to filter data based on the conditions provided. These conditions can vary.
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.
Linux Commands List with Examples
List of most useful Linux commands with description and examples
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
Why learn from us?
How is machinelearningplus university different from other courses that teach machine learning, Data Science , AI?
What is a Data Scientist? – Roles, Responsibilities, Skillsets, Career Path and Salary
A Data scientist uses Data and AI to solve business problems, is skilled at working with data, extract meaningful insights, using ML to solve...
The story of how Data Scientists came into existence
In this one, I look back and reflect on how the profession of Data Scientists came into being.
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...
How to deal with Big Data in Python for ML Projects (100+ GB)?
The size of the data you can load and train in your local computer is typically limited by the memory in your computer. Let's see how to deal with...
Granger Causality Test in Python
Granger Causality test is a statistical test that is used to determine if a given time series and it's lags is helpful in explaining the value of...
Granger Causality Test
Granger causality test is a statistical test that is used to determine of a particular time series is helpful in forecasting another series
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.
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...
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...
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...
Python Module – What are modules and packages in python?
Any Python files is a Module in Python. A python package is a collection of such modules along with a `__init__.py` file. Let's understand how to...
Iterators in Python – What are Iterators and Iterables?
Iterable in Python is any object that can be looped over. In order for an iterable to be looped over, an iterable needs to be converted to an...
Generators in Python – How to lazily return values only when needed and save memory?
Generators in python provide an efficient way of generating numbers or objects as and when needed, without having to store all the values in memory...
Decorators in Python – How to enhance functions without changing the code?
Decorators in python allow you to dynamically change the functionality of another function, without altering it's code.
Object Oriented Programming (OOPS) in Python
Object oriented programming is an effective way of writing code. Classes represent meaningful entities which defines its own behaviour (via methods)...
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...
ARIMA Model – Complete Guide to Time Series Forecasting in Python
Using ARIMA model, you can forecast a time series using the series past values. In this post, we build an optimal ARIMA model from scratch and extend...
One Sample T Test – Clearly Explained with Examples | ML+
One sample T-Test tests if the mean of a given sample is statistically different from a known value (a hypothesized population mean). If it is found...
Standard Error in Statistics – Understanding the concept, formula and how to calculate
Standard error allows you to build a relationship between a sample statistic (computed from a smaller sample of the population and the population's...
Confidence Interval in Statistics – Formula and Mathematical Calculation
Confidence interval is a measure to quantify the uncertainty in an estimated statisic (like the mean) when the true population parameter is unknown.
T Test (Students T Test) – Understanding the math and how it works
T Test (Students T Test) is one of the foundational statistical tests. It is used to compare the means of two groups and determine if the difference...
data.table in R – The Complete Beginners Guide
data.table package is used for working with tabular data in R. It is super fast and has intuitive and terse syntax. This tutorial guide shows how to...
KPSS Test for Stationarity
The KPSS test checks whether a series is stationary around a deterministic trend. Like the ADF test it is widely used, but it differs in function and...
Augmented Dickey Fuller Test (ADF Test) – Must Read Guide
Augmented Dickey Fuller test (ADF Test) is a common statistical test used to test whether a given Time series is stationary or not. It is one of the...
101 R data.table Exercises
Sharpen your R data.table skills with 101 practical exercises. Cover data manipulation, aggregation, joins, and reshaping with one of R's fastest...
What is P-Value? – Understanding the meaning, math and methods
The p-value is a probability score used in statistical tests to establish the significance of an observed effect. Its meaning is often unclear even...
101 Python datatable Exercises (pydatatable)
Python datatable is the newest package for data manipulation and analysis in Python. It carries the spirit of R's `data.table` with similar syntax...
Vector Autoregression (VAR) – Comprehensive Guide with Examples in Python
Learn Vector Autoregression (VAR) for multivariate time series forecasting. Covers the intuition, math, model training, diagnostics, and forecasting...
Mahalanobis Distance – Understanding the math with examples (python)
Mahalanobis distance is an effective multivariate distance metric that measures the distance between a point and a distribution. It is an extremely...
datetime in Python – Simplified Guide with Clear Examples
datetime is the standard module for working with dates in python. It provides 4 main objects for date and time operations: datetime, date, time and...
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...
Python Logging – Simplest Guide with Full Code and Examples
The logging module lets you track events while your code runs, so you can find what caused a crash from the logs. This post is a simple, clear guide...
Matplotlib Histogram – How to Visualize Distributions in Python
Matplotlib histogram is used to visualize the frequency distribution of numeric array. In this article, we explore practical techniques like...
Time Series Analysis in Python – A Comprehensive Guide with Examples
Time series is a sequence of observations recorded at regular time intervals. This guide walks you through the process of analysing the...
Matplotlib Tutorial – A Complete Guide to Python Plot with Examples
This tutorial explains matplotlib's way of making python plot, like scatterplots, bar charts and customize th components like figure, subplots...
Topic modeling visualization – How to present the results of LDA models?
In this post, we follow a structured approach to build gensim's topic model and explore multiple strategies to visualize results using matplotlib...
Top 50 matplotlib Visualizations – The Master Plots (with full python code)
A compilation of the Top 50 matplotlib plots most useful in data analysis and visualization. This list helps you to choose what visualization to show...
List Comprehensions in Python – My Simplified Guide
List comprehensions is a pythonic way of expressing a 'For Loop' that appends to a list in a single line of code. It is an intuitive, easy-to-read...
Python @Property Explained – How to Use and When? (Full Examples)
A python @property decorator lets a method to be accessed as an attribute instead of as a method with a '()'. Today, you will gain an understanding...
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...
Parallel Processing in Python – A Practical Guide with Examples
Parallel processing is when the task is executed simultaneously in multiple processors. In this tutorial, you'll understand the procedure to...
Cosine Similarity – Understanding the math and how it works (with python codes)
Cosine similarity is a metric used to measure how similar the documents are irrespective of their size. It is the cosine of the angle between two...
Gensim Tutorial – A Complete Beginners Guide
Gensim is billed as a Natural Language Processing package that does 'Topic Modeling for Humans'. But it is practically much more than that. It is a...
Lemmatization Approaches with Examples in Python
Lemmatization is the process of converting a word to its base form. Python has nice implementations through the NLTK, TextBlob, Pattern, spaCy and...
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...
101 Pandas Exercises for Data Analysis
Master pandas with 101 hands-on exercises across 3 difficulty levels. Practice data manipulation, filtering, grouping, and more to sharpen your...
LDA in Python – How to grid search best topic models?
Python's Scikit Learn provides a convenient interface for topic modeling using algorithms like Latent Dirichlet allocation(LDA), LSI and Non-Negative...
Topic Modeling with Gensim (Python)
Topic Modeling is a technique to understand and extract the hidden topics from large volumes of text. Latent Dirichlet Allocation(LDA) is an...
pdb – How to use Python debugger
pdb, short for python debugger is a standard built-in module used to debug python code interactively. You can set breakpoints, see variable values...
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...
101 NumPy Exercises for Data Analysis (Python)
The goal of the numpy exercises is to serve as a reference as well as to get you to apply numpy beyond the basics. The questions are of 4 levels of...
Numpy Tutorial Part 2 – Vital Functions for Data Analysis
This is part 2 of the mega numpy tutorial. In this part, I go into the details of the advanced features of numpy arrays that are essential for data...
Numpy Tutorial – Your first numpy guide to build python coding foundations
This is part 1 of the numpy tutorial covering all the core aspects of performing data manipulation and analysis with numpy's ndarrays. Numpy is the...
Python Regular Expressions Tutorial and Examples: A Simplified Guide
Regular expressions, also called regex, is a syntax or rather a language to search, extract and manipulate specific string patterns from a larger...
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...
How to implement common statistical significance tests and find the p value?
Learn the purpose, when to use and how to implement statistical significance tests (hypothesis testing) with example codes in R. How to interpret P...
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...