Is there any connection between ML and AL?
Yes, Machine Learning is an application of AI that allows the system to learn automatically and improve from experience without being explicitly programmed.
What are the 3 basics of Machine Learning?
The three Basic principles of machine learning are 1) Supervised Learning, 2) Unsupervised Learning, 3) Reinforcement Learning.
A brief of 3 basics of Machine Learning is given below...
Supervised Learning:
We train the machine using data that is well labeled with correct output.
As the dataset is labeled meaning that the algorithm identifies the features explicitly and carries out prediction or classification accordingly.
We can assume a dataset is a teacher or supervisor and its role is to train the machine/model.
Types Of Supervised Learning:
Classification
Regression
Unsupervised Learning:
Deals with Unlabeled Data.
No teacher/supervisor (training dataset) is provided. No training will be given to the machine that's why the machine will work on its own to discover info.
Here, the task of the machine is to group unsorted information according to similarities, patterns, and differences with any prior training of data.
Unsupervised learning finds features that can be useful for categorization. It finds all kinds of unknown patterns in data.
Types Of Unsupervised Learning:
Clustering: A clustering problem is where we want to discover inherent grouping/categories in data. It splits the dataset into groups based on similarities.
Categories of Clustering Algo :
K-means Clustering.
Fuzzy/C-means Clustering.
Association: An association rule learning problem is where we want to discover rules that describe large patterns of your data. It identifies a set of items that often occur together in the dataset.
Categories of Association Algo :
Apriori Algorithm.
FP-growth Algorithm.
Eclat Algorithm.
PCAR Algorithm.
Reinforcement Learning:
Type of ML in which a computer learns to perform a task through repeated trial and error interactions with a dynamic environment.
In RL, an agent will have to interact with the environment and find out what is the best outcome. The agent follows the concept of the hit and trial method, agent is rewarded or penalized with a point for a correct or wrong answer.
Based on positive reward points gained, the model trains itself. Once gets trained, it is ready to predict the new data presented to it.
RL algo continuously interactively learns from the environment until it explores the full range of possible states.
e.g. ....-Chess game bots and robotics for industrial automation.
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