Challenges in Machine Learning | Problems in Machine Learning

Machine Learning or ML is one of the most successful applications of Artificial intelligence which provides systems with automated learning without being constantly programmed. It has acquired a ton of noticeable quality lately due to its capacity to be applied across scores of ventures to tackle complex issues quickly and effectively. From Digital assistants that play your music to the products being recommended based on prior search, Machine Learning has taken over many aspects of our life. It is a skill in high demand as companies require software that can grasp data and provide accurate results. The core objective is to obtain optimal functions with less confusion. But in this field, there are also many challenges and difficulties from development to production stages.

The Cost of AI: https://research.google/pubs/pub43146/

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⌚Time Stamps⌚

00:00 - Intro
00:50 - Data Collection
02:22 - Insufficient Data
06:10 - Non Representative Data
09:19 - Poor Quality Data
10:32 - Irrelevant Features
13:05 - Overfitting
15:39 - Underfitting
16:33 - Software Integration
19:20 - Offline Learning/Deployment
20:45 - Cost Involved
23:20 - Outro