Machine Learning is great for:
- Problems for which existing solutions require a lot of fine-tuning or long lists of rules: One Machine Learning algorithm can often simplify code and perform better than the traditional approach.
- Complex problems for which using a traditional approach yields no good solution: The best Machine Learning techniques can perhaps find a solution.
- Fluctuating environments: A Machine Learning system can adapt to new data, getting insights about complex problems and large amounts of data.
Examples of applications:
- Analyzing images of products on a production line to classify them (CNN).
- Detecting tumors in brain scans (CNN).
- Classifying news articles (NLP, RNN, CNN, or Transformers).
- Flagging unwanted content (NLP).
- Summarizing long documents (Text Summarization).
- Creating a chatbot (NLP, NLU).
- Forecasting revenue based on many performance metrics (Linear Regression, SVM, NN).
- Segmenting clients based on their purchases (Clustering).
- Recommending a product based on past purchases (Artificial NN).
- Building an AI bot (RL).
Types of ML:
- Whether or not they are trained with human supervision (supervised, unsupervised, semi-supervised, and RL).
- Whether or not they can learn incrementally on the fly (online vs. batch learning).
- Whether they work by comparing new data points to known data points or by detecting patterns in the training data and building a model (instance-based vs. model-based learning).
