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Abhishek Thakur

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Course 65 Developers 60 Beginner 56 Intermediate 48 Build an App 42 Students 36 Analyse Business Data 30 AI Coding 29 Complete Beginners 20 Data Scientist 20 AI skills 20 Train a Team 18 Python 12 Fine-Tuning 11

Segment Anything + ControlNet + Stable Diffusion = 💥

Abhishek Thakur Save to stack

Stable Diffusion Inpainting with Segment Anything Model (SAM)

Abhishek Thakur Save to stack

How to create GPT-powered conversational bot for any website

Abhishek Thakur Save to stack

How to become a data scientist in 30 days?

Abhishek Thakur Save to stack

Data representations for neural networks

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My First Neural Network using Keras

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Kaggle's 30 Days Of ML (Competition Part-7): What are public and private leaderboard?

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Kaggle's 30 Days Of ML (Competition Part-6): Model Stacking

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Kaggle's 30 Days Of ML (Competition Part-5): Model Blending 101

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Kaggle's 30 Days Of ML (Competition Part-4): Hyperparameter tuning using Optuna

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Kaggle's 30 Days Of ML (Competition Part-3): What is Target Encoding and how does it work?

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Kaggle's 30 Days Of ML (Competition Part-2): Feature Engineering (Categorical & Numerical Variables)

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Kaggle's 30 Days Of ML (Competition Part-1): Cross Validation & First Submission on Kaggle

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Kaggle's 30 Days Of ML (Day-14 Part-2): What is Data Leakage?

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Kaggle's 30 Days Of ML (Day-14 Part-1): Intro to XGBoost

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Kaggle's 30 Days Of ML (Day-13 Part-2): Cross-validation

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Kaggle's 30 Days Of ML (Day-13 Part-1): Scikit-Learn Pipelines

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Kaggle's 30 Days Of ML (Day-12 Part-2): Handling Categorical Variables

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Kaggle's 30 Days Of ML (Day-12 Part-1): Handling Missing Values in Datasets (imputing missing value)

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Kaggle's 30 Days Of ML (Day-11): Machine Learning Model to Predict House Prices (Intro to ML Ends)

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Kaggle's 30 Days Of ML (Day-10): Underfitting, Overfitting & Random Forests

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Kaggle's 30 Days Of ML (Day-9): First Machine Learning Model and Validation

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Makers residency 4th edition

Your idea can be anything. At our last workshops, people built plant-based nutrition businesses, a way to measure planetary resilience, crochet headwear and a way to connect lonely people living in the same building. An asset manager went on to raise an additional £25M on their fund within nine months. The fourth workshop is a full day at KOKO with Nick Sarafa, learning to build with Claude Code. Bring the thing you keep meaning to start. We’ll bring the pizza.

◆ Fri 13 Nov 2026 ◆ KOKO, London

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Makers residency 4th edition

Fri 13 Nov 2026

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Let us build it for you. Design and engineering from the people who shipped platforms to billions of users. AI-native, live in weeks, and yours outright at the end.

Let's build it See the work

Segment Anything + ControlNet + Stable Diffusion = 💥

Stable Diffusion Inpainting with Segment Anything Model (SAM)

How to create GPT-powered conversational bot for any website

How to become a data scientist in 30 days?

Data representations for neural networks

My First Neural Network using Keras

What is deep learning?

Kaggle's 30 Days Of ML (Competition Part-7): What are public and private leaderboard?

Kaggle's 30 Days Of ML (Competition Part-6): Model Stacking

Kaggle's 30 Days Of ML (Competition Part-5): Model Blending 101

Kaggle's 30 Days Of ML (Competition Part-4): Hyperparameter tuning using Optuna

Kaggle's 30 Days Of ML (Competition Part-3): What is Target Encoding and how does it work?

Kaggle's 30 Days Of ML (Competition Part-2): Feature Engineering (Categorical & Numerical Variables)

Kaggle's 30 Days Of ML (Competition Part-1): Cross Validation & First Submission on Kaggle

Kaggle's 30 Days Of ML (Day-14 Part-2): What is Data Leakage?

Kaggle's 30 Days Of ML (Day-14 Part-1): Intro to XGBoost

Kaggle's 30 Days Of ML (Day-13 Part-2): Cross-validation

Kaggle's 30 Days Of ML (Day-13 Part-1): Scikit-Learn Pipelines

Kaggle's 30 Days Of ML (Day-12 Part-2): Handling Categorical Variables

Kaggle's 30 Days Of ML (Day-12 Part-1): Handling Missing Values in Datasets (imputing missing value)

Kaggle's 30 Days Of ML (Day-11): Machine Learning Model to Predict House Prices (Intro to ML Ends)

Kaggle's 30 Days Of ML (Day-10): Underfitting, Overfitting & Random Forests

How do random forests work?

Kaggle's 30 Days Of ML (Day-9): First Machine Learning Model and Validation

Makers residency 4th edition

Jobs in AI.Apply now.

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