This course provides a broad introduction to machine learning (ML). It is intended for people with a background in computer science, who have not studied ML and AI before and want to gain skills in this area and understand how ML techniques work under the hood.
Students will learn about standard supervised machine learning techniques (for classification and regression), some unsupervised learning techniques (for clustering and anomaly detection), as well as best practices to achieve a good generalization and avoid underfitting/overfitting. Students will also gain practice implementing these techniques in Python and getting them to work on real data.
The aim of the course is for students to learn about standard supervised Machine Learning (ML) techniques for regression and classification as well as best practices in ML, and gain practice implementing ML in Python to work on real data.
The course covers the following topics:
- Introduction to machine learning, including basics and prerequisites
- Basic aspects of supervised machine learning, including basic regression and classification algorithms
- Overfitting and generalization, the bias/variance trade-off, and methods for avoiding overfitting, including regularization. Explanation of how these problems are addressed in various methods, including Support Vector Machines (SVMs), and ensemble methods
- Introduction to Neural Networks for supervised learning, as well as an overview of deep neural networks and unsupervised feature extraction with autoencoders
- Overview of unsupervised data clustering methods and their applications.
Kozi hii hutoa utangulizi mpana kwa kujifunza mashine (ML). Ni lengo kwa watu wenye historia katika sayansi ya kompyuta, ambao hawajawahi kusoma ML na AI kabla na wanataka kupata ujuzi katika eneo hili na kuelewa jinsi mbinu ML kazi chini ya hood. Wanafunzi watajifunza juu ya mbinu za kawaida za kujifunza mashine (kwa ajili ya uainishaji na kurudi nyuma), baadhi ya mbinu za kujifunza zisizo na usimamizi (kwa ajili ya clustering na kugundua anomaly), pamoja na mazoea bora ya kufikia jumla nzuri na kuepuka underfitting na overfitting. Wanafunzi pia kupata mazoezi ya kutekeleza mbinu hizi katika Python na kupata yao kufanya kazi juu ya data halisi. Lengo la kozi hii ni kwa wanafunzi kujifunza juu ya mbinu za kawaida za kujifunza mashine (ML) kwa ajili ya kurudi nyuma na uainishaji, pamoja na mazoea bora katika ML, na kupata mazoezi ya kutekeleza ML katika Python kufanya kazi kwenye data halisi. Mafunzo ya mafunzo ya mashine ni pamoja na masomo ya msingi ya mafunzo ya mashine, pamoja na masomo ya msingi ya masomo ya mashine, pamoja na algorithms ya msingi ya regression na classification, overfitting na generalization, bias na variance trade-off, na mbinu za kuepuka overfitting. Maelezo ya jinsi matatizo haya ni kushughulikiwa katika mbinu mbalimbali, ikiwa ni pamoja na msaada vector mashine (SVMs) na mbinu ensemble - Utangulizi wa Neural Mitandao kwa ajili ya kujifunza kusimamiwa, kama vile muhtasari wa kina neural mitandao na unsupervised kipengele uchimbaji na autoencoders - muhtasari wa unsupervised data clustering mbinu na maombi yao.