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 in a production line to classify them (CNN)
• Detecting tumors in brain scans (CNN)
• News articles classification (NLP, RNN, CNN or Transformers)
• Flagging unwanted content (NLP)
• Summarizing long documents (Text Summarization)
• 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)
• AI bot (RL)

Types of ML

• Trained with human supervision (supervised, unsupervised, semisupervised, 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)

References