Machine Learning is the unknown friend of ours that has crept in to our daily lives without most of us knowing it. But to enthusiasts of the more technical aspects of smart devices or even to those with a passing interest in the subject the term Machine Learning is no way alien. But if you are interested in knowing what it means and entails to a little detail then this video is a must watch. Read more at: http://www.dexlabanalytics.com/

1. As technology marches on with its conquest of miracles
we find Machine Learning becoming more and more
ubiquitous. From smartphones to chatbots (remember the
recent controversy surround an AI chatbot of Microsoft)
machine learning is fast becoming a part and parcel of our
everyday lives in which technology plays a pivotal role.
Machine Learning

2. What is Machine Learning?
Machine Learning may present itself in the
humblest of fashions like when cameras of
smart phones are able to recognize faces of
people. There are even simpler examples of
our day to day interactions with machine
learning. Suppose you have added the
names and phone numbers of friends and
acquaintances in your. And what happens
when you start to dial a number the
suggested contacts are displayed
automatically. Here you unknowingly are
teaching the phone to detect keywords and
patterns.

3. Machine Learning
Algorithms and Their Types
• There are number of ways in which
Machine learning may take place
and these are known as Machine
Learning Algorithms.
• Three many types of Machine
Learning Algorithms that dominate
this evolving field. They are:
• Supervised Learning
• Semi-supervised Learning
• Unsupervised Learning

4. Important Keywords in
Machine Learning
• However, before we move on
with details of machine learning
algorithms there are a number
of keywords which we should
be well aware of. These are:
• Training Data
• Bayes Theorem and
• K-Means

5. Training Data
Training Data: The data made
available to the machine
through input is known as
Training Data as this data is
used by the machine to further
develop patterns. It consists of
known labels technically called
categorical variables like,
ratings, gender and the like.

6. Bayes Theorem
Bayes Theorem: According to
the Bayes Theorem, the product
of probability of occurrence of
event B and occurrence of event
A when B has already occurred
is equal to product of event A
and occurrence of B when A has
already occurred

7. K-Means
• Through the method of K-
Means clustering based on
Euclidean distance which is
• Here K is the no. of clusters.

8. Supervised Learning
Preparation of models are done
through a process of training where
the machine makes predictions and are
corrected when they err. This process
of training goes on till desirable levels
of accuracy are achieved on the
training data. Labels in training data
are also present.

9. Example of Supervised
Learning
To cite an example, past GRE scores
and GPA of students in indicate that a
score of 720 and a GPA of 4.2 will
help them secure admission to good
colleges. Inputting scores result in
you being given feedback regarding
whether you are rejected or selected.
As abnormalities are present, this
learning process is continuous. They
also make use of Logistic Regression.

10. Unsupervised Learning
In case of unsupervised learning
there is an absence of input data and
the results are known beforehand.
The preparation of the model occurs
through deducing structures that are
present in the input data. One of the
goals may be to chance upon general
rules. This process may occur in a
mathematical manner so that
redundancy is reduced or data is
organized by similarity.

11. Example of Unsupervised
Learning
To explain Unsupervised Learning
we may cite the following example-
You are engaged in cluster analysis in
order to figure out the particular data
points that form part of particular
clusters. When a new data point is
introduced the machines deduces it to
be part of one of the clusters.

12. Semi-Supervised
Learning
Here the input data is a blend of
examples that may or may not
have labels. A desired prediction
problem is present but the model
needs to learn the structures
required for organizing data in
addition to making predictions.

13. Thank You
DexLab Analytics would like
to thank the viewers of this
presentation for going through
the same.
For details visit:
http://www.dexlabanalytics.com