learndataaLearn data science, machine learning and the math behind them.
Over 550 free video lessons on YouTube, from your first line of Python to neural networks running on a ₹600 microcontroller. When you want guidance, learn live with Dr. Nilesh Ingle.
Watched over 665,000 times by 5,000+ subscribers since 2020.
Learn free
Every course on YouTube, organised into a path you can follow.
Free coursesLearn live
1-on-1 or small groups of up to 5, with projects and feedback.
Live programsBuild hardware
Real sensor data from STM32 boards into Python, and neural networks on chips.
Hardware seriesGet help with data
Analysis, dashboards and simple models for small, well-defined projects.
ConsultingStart with the free Python path
Three levels, each building on the one before. Every video is free on YouTube.
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Watch playlist
Beginner: data science with Python
Your first Python code, then NumPy, Pandas and Matplotlib with the basics of descriptive statistics.
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Watch playlist
Intermediate: machine learning with scikit-learn
Preprocessing, classification, regression and clustering on real data.
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Watch playlist
Advanced: deep learning with Keras and TensorFlow
Train neural networks for image classification, object detection and more.
Live mentorship programs
For learners who want direct feedback, accountability and a mentor. You code along live, build projects around your own interests, and get help with your portfolio and interviews.
Data Science for Beginners
Python, NumPy, Pandas and Matplotlib, ending in a project with real data and an online portfolio.
What you'll learn
- What data science is, descriptive statistics, matrices and vectors
- Python data types, NumPy, Matplotlib and Pandas in Jupyter
- Data preprocessing and projects with toy and real-world datasets
- A final project shaped around your interests
Who it's for
- Complete beginners to coding who enjoy working with data
- IT and non-IT professionals moving into data science
- Prerequisite: high school maths
Data Science for Intermediate
Machine learning with scikit-learn: supervised and unsupervised learning on real datasets.
What you'll learn
- Data cleaning, standardisation and encoding
- Regression, classification, dimensionality reduction and model selection
- Clustering and manifold learning
- A final project in a domain you choose
Who it's for
- Learners who've finished the Beginner program
- Anyone who wants to build prediction models
- Focus on applying, tuning and evaluating models, not math derivations
Introduction to Statistics
Sampling, probability, regression and inference, understood by reasoning, with no coding required.
What you'll learn
- Populations, samples, variability and graphical summaries
- Probability, independence and the law of large numbers
- The normal curve, correlation and regression
- Confidence intervals, significance and p-values without fear
Who it's for
- Anyone who wants to understand statistics before learning coding tools
- Aspiring analysts who want to interpret results correctly
- Based on Statistics by Freedman, Pisani and Purves
Mathematics for Machine Learning New in 2026
Linear algebra, geometry, matrix calculus, probability and optimisation, each linked to how ML uses it.
What you'll learn
- Vectors, matrices, eigenvalues and transformations
- Projections, distances and orthogonality
- Gradients and Jacobians in learning algorithms
- Distributions, least squares, convexity and gradient descent
Who it's for
- People who've used ML libraries and want to understand the equations underneath
- Prerequisite: high school algebra; no coding needed
- Based on the textbook by Deisenroth, Faisal and Ong
Neural Network Design New in 2026
How neural networks actually learn, built up from first principles with derivations and Python.
What you'll learn
- Perceptron learning, linear neurons and adaptive filters
- Backpropagation, architectures and regularisation
- RBF networks, Hopfield networks, SOMs and LVQ
- Training heuristics, early stopping and error analysis
Who it's for
- Learners who know basic ML and want deeper theory
- Engineers curious about the inner mechanics of neural computation
- Based on the textbook by Hagan, Demuth, Beale and De Jesús
Fees for 2026 are shared on request. Ask about a program.
Hardware: from a ₹600 chip to a neural network
Make real hardware talk to Python, with no electronics experience needed. Start by sending a single number from an STM32 board to your laptop, end by streaming live sensor data into pandas and cleaning a signal with a neural network running on the chip.
Live sessions are also available, including deploying neural networks to STM32 and ESP32 microcontrollers.
What you need
An STM32F051 board, ST-LINK programmer, breadboard, multimeter and a few basic parts: about ₹1,800 in total. All software is free.
Consulting and support
Need data analysed, a dashboard built or a simple prediction model? Small, well-defined projects are a good fit. The first conversation is free.
Data projects
- Data cleaning and preprocessing
- Exploratory and statistical analysis
- Visualisations and dashboards
- Prediction models with classical ML or simple neural networks
Academic support
- Guidance on Master's and Ph.D. thesis projects
- Statistical analysis of research data
- Guidance on writing technical manuscripts
Workshops
- Data science webinars and workshops for companies, universities and schools
- Data science for high school students
- Online or on-site
Hourly rates apply. Describe your project.
Courses on Udemy
Structured, self-paced courses with exercises.
NumPy Fundamentals for Data Analytics in Python
Arrays, operations and the numerical foundations of data analysis.
View on UdemyAbout learndataa
Dr. Nilesh Ingle
Adjunct Assistant Professor at the Wilson College of Textiles, North Carolina State University. Nilesh spent over a decade in research in biomedical engineering, biotechnology and medical textiles, then worked as an analyst and data scientist in medical diagnostics, marketing and gaming. He holds a B.Tech and M.Tech from VJTI, Mumbai, and dual Ph.D.s in Biomedical Engineering and Fiber & Polymer Science from NC State and UNC Chapel Hill, with postdoctoral research in imaging and cancer research at Virginia Tech and the University of Minnesota. He started learndataa on YouTube in 2020.
Amol Ingle
Amol is a database administrator and team leader with over twenty years of experience, including roles at Birlasoft and Zensar Technologies, leading teams in Pune and California. His expertise spans database administration, Python, SQL, statistical analysis and predictive modelling.
Get in touch
Tell us what you want to learn or what your project needs, and your current level. We'll reply with options and fees.