Introduction to machine learning

Bayes theorem is a mathematical formula for determining conditional probability of events

Training for supervised learning needs a lot of computation time

Autonomous vehicles can monitor their environment & maneuver themselves with minimal or no human interaction

ML uses probabilistic models to solve the problem

Classifying big data is one of the disadvantages of supervised learning

New wave of AI brings us a critical component of intelligence called prediction

Credit card approval is considered to be a classification problem

Clustering is one of the types of Supervised Learning Algorithm

Supervised learning is commonly used in situations where historical data predicts likely future events

Email spam detection is one good example of unsupervised learning application

Credit card fraud detection is one of the examples of supervised machine learning algorithm

One of the founders of computing:

Data preparation & pre-processing is one of the challenges in supervised machine learning

Supervised machine learning deals with unlabeled data

Algorithm is a set of rules to be followed in calculations or problem-solving operations by a computer

Unsupervised learning works on unlabeled and uncategorized data