see, Technogies like BLOCKCHAIN and MACHINE LEARNING in some slides is not enough to make you master of these technogies but if you visit our slides starting from first to last,you will have some basic concept of MACHINE LEARNING

1. Machine Learning
Human & Machine! Not versus

2. Overview over Machine Learning
 Thename Machine Learningwascoined in 1959
by ArthurSamuel.
 It is one of the newest and most trending technology
in presentComputerScience world.
 Machine learning(ML) means Learning from Data.
 Machine =Yourmachine/computer &
Learning =Finding patterns from data
 In ML, certain computer algorithms are usedto
autonomously learn from data and information.

3. What is Machine Learning?
(Technically)
Machine learning(ML) is abranch of sciencethat deals with programming
the systemsin suchaway that they automatically learn and improve with
experience
It is abranch of artificial intelligence based on the idea that systems can
learn from data, identify patterns and makedecisions with minimal human
intervention.
Here, learning meansrecognizing and understanding the input data and
making wise decisions based on the supplied data.

4. Typesof Machine Learning
 Following arethe type of MachineLearning:-
1. Supervisedlearning- Supervised learning deals with learning afunction from
available training data. A supervised learning algorithm analyzes the training
data and produces an inferred function, which canbe used for mappingnew
examples e.g. Email spamming, voice recognition etc.
2. Unsupervised Learning - Unsupervised learning makes senseof unlabeled data
without havingany predefined dataset for its training. It is most commonly
usedfor clustering similar input into logical groups.E.g. self-OrganizingMaps,
HierarchicalClustering, k-means.
3. Reinforcement learning- It is an area of machine learning
concerned with a software agents ought to take actions in an
environment so as to maximize some notion of cumulative
rewards.

5. Components of a learning problem
 Task : The behaviour or task that is being improved.
 For example: classification, acting in an environment
 Data : The experiences that are being used to improve
performance in the task.
 Measure of improvement :
 For example: increasing accuracy in prediction, acquiring
new improved speed and efficiency.

6. Applications
 Virtual PersonalAssistants- Siri, Alexa, Google Now are some of the popular examples of
virtual personalassistants.Asthe namesuggests,they assistin finding information, when askedover
voice.All you needto do is activate them and askanything you want to ask.
 Social MediaServices- Somecommonusesin socialmedia services –
1)PeopleYouMay Know
2)FaceRecognition
Search Engine Result Refining-Youshoppedfor aproduct online few daysback and then you
keep receiving emails for shoppingsuggestions.
 Healthcare-MLis becoming afast-growing trend in healthcare.Sensorsin wearable provide real-
time patient information, suchasoverall health condition, heartbeat, blood pressure and other vital
parameters. Doctorsand medical experts canusethis information to analyzethe health condition of
anindividual, draw apattern from the patient history, and predict the occurrenceof anyailments in
the future.