AI Campus • Prayogika kalike
Data Science mattu Maulyamaapana Holike
Kelasakke takka metric aarisi, ottaare score hindiruva failuregalannu parisheelisi.
Aarambhisuva modalu
- Python, basic fractions, chikka labeled dataset mattu ulisida outputgalu beku.
Ee oduva mattu planning abhyasagalige paid course athava API bekaagilla. Optional inference, training, IDE feature athava hosted sevege hardware, account athava paid access bekaagabahudu. Kharchu maaduva modalu local athava manual aayke balasi.
Official kalikeya maahitiyinda aarambhisi
Odalu free iruva moolagalannu 3 November 2026 randu parisheelisalagide. Documentation oduvudarinda provider athava subscription activate aaguvudilla.
Prayogika abhyasa maadi
1. Decision mattu labelgalannu nirdharisi
Classification-metrics bhaaga odi. Expected mattu predicted categorygala 20-item sarala table kattiri. Ondu classge true positives, false positives mattu false negatives lekkahaaki. Nimma kelasakke yaava tappu hecchu nashta taruttade endu vivarisi.
2. Upayukta metricgalannu holisi
Zero denominator tadeyutta precision mattu recall lekkahaaki. Mukhya class tappisidaroo accuracy hecchaagi kaanuva class-imbalanced case parisheelisi. Text overlap score annu factuality ge taane puraave endu balasabedi.
3. Candidate nalli gunamatta ilike pattehacci
Ulisida baseline mattu candidate outputgala mele sambandhita offline-evaluator starter run maadi. Chikka human-review rubric serisi, prati case failure ulisi. Phrase checks, reviewer scores, latency mattu measure maadada vivaragalannu pratyekavaagi gurutisi.
Kalitaddannu parikshe maadi
- Metric vyaakhyaana mattu denominatorgalu kaanisabeku.
- Prati summary score jothe kanishta ondu failure case irabeku.
- Measure maadada model quality athava cost metric annu nodida result endu torisabaaradu.
Nimma karya, maadida parikshe mattu ulida mitigalannu sanna README nalli bareyiri. Kevala design maadida abhyasavannu execute maadida haage helabedi.