Scikit Learn

Skills for certificate:

Python

Machine Learning

Data Science

Continuous Integration

Hyperparameters

Boosting

Neural Networks

Scikit Learn

This is the page displaying all the material related to Scikit Learn. This can include projects, blogs, certificates, university modules and work experience along with sub-skills.

Material

Adult Income Prediction

A project comparing various classification algorithms to predict whether an adult earns more than $50,000 a year. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.

House Price Prediction

A project comparing various regression algorithms to predict house prices in relation to the distance from the coast. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.

Machine Learning Algorithms & Techniques Lab

Practicing various Machine Learning algorithms and techniques. This includes supervised, unsupervised, and reinforcement learning algorithms, as well as feature engineering, data preprocessing, and hyperparameter tuning. Some additional content in the field of Deep Learning and Neural Networks are also covered.

Reinforcement Learning Lab

Practicing various Reinforcement Learning algorithms and techniques. This includes Q-Learning, Deep Q-Learning, and Asynchronous Advantage Actor-Critic (A3C) algorithms.

Machine Learning Assignment 1

Be able to implement machine-learning algorithms, using the Nearest Neighbours algorithm as an example. Have an understanding of ways to apply the ideas and algorithms of machine learning in science and technology.

Machine Learning Assignment 2

Be able to use and implement machine-learning algorithms, with the Lasso and inductive conformal prediction algorithms as examples. Have an understanding of ways to apply the ideas and algorithms of machine learning in industry and medicine.

Machine Learning Assignment 3

Be able to use and implement machine-learning algorithms, with the SVM, neural networks, and cross-conformal prediction algorithms as examples. Have an understanding of ways to apply the ideas and algorithms of machine learning in industry.

Machine Learning Lab Questions

Implemented various machine learning algorithms and techniques learned during the course, such as Nearest Neighbours, conformal prediction, linear regression, Ridge Regression, Lasso, data preprocessing, parameter selection, kernels, neural networks, support vector machines, scikit-learn pipelines, and cross-conformal predictors.

Machine Learning & Data Science Lab

This lab mainly focuses on learning generative models, using third-party models and using advanced techniques. This includes techniques such as transfer learning, LLM Agents, and Generative Models.

Related Skills