Machine Learning (ML): It is a way of teaching machines to solve problem using data, rather than doing explicit coding for each situation.

Deep Learning (DL): It is a part of Machine Learning which tackles any problem (in theory) using interconnected nodes (neurons / weights & biases). It is highly flexible, adaptable and even mimics human brain.

Types of Machine Learning:

1. Supervised Learning - consumes data with proper labels
2. Semi-supervised Learning - requires partial data
3. Unsupervised Learning - works by clustering similar data
4. Reinforcement Learning - based on environments, policies, rewards

History of ML

1. AI winter: It is period of time where AI was has having a lot of attention but no one ever took it seriously (implementation).
2. Big Data: In 80’s and 90’s data was produced but due to limited computational power and low quality of data, AI never took off.
3. Technology: Now we have very fast accessible computational power, good quality big data, and improved algorithms.

Limitations of Machine Learning

1. Model requires some amount of good data
2. Data collected should also contain labels
3. Model learns only patterns which exist in the dataset
4. Model only predicts outcomes of the classes, not recommended actions.
5. Feedback loops: Few models are biased towards a region of the dataset, when the model retrained on the new data, It tends to go in a loop where model gets more and more biased