Machine Learning is a new age technology, which is considered as a subset of AI. And due to this newness or since the technology is in its nascent stage, there is no clarity among many members around the world. And that’s the number one reason why this new-age technology is under-utilized and is not extracted the way it is meant to be. There are several reasons as to why Machine Learning Projects fail around the globe and they are:
- Lack of clear leadership, clarity in technology, Data Strategy, etc.
- Incorrect perceptions of the solution, losing the ground due to poorly constructed strategies, not so clear observation of the problem, leading to incorrect development approach of building Machine Learning models.
- Lacks mutual understanding, and collaboration between the teams, the incorrect depiction of the problem, and absence of true strategy in the Data Science stream for unifying the data for training the models to improve accuracy.
If you are interested in learning Machine Learning, check out the Machine Learning course from Intellipaat. This course offers you dedicated instructor-led training and guided projects that will help in building practical skills. Also, watch the following video on the Machine Learning project made by subject-matter experts.
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