These are some general ideas to get one started with "Machine Learning".Machine learning is a vast subject in the field of computer science & needs intense research to master.

2. MACHINE
LEARNING

3. CONTENTS

4. WHAT IS IT?
 Machine learning is the subfield of computer science that,
according to Arthur Samuel, gives "computers the ability
to learn without being explicitly programmed.“
 Optimize a performance criterion using example data or
past experience.

5. HOW EXACTLY DO WE TEACH
MACHINES?
Teaching the machines involve a structural process where every stage builds a
better version of the machine. For simplification purpose, the process of teaching
machines can broken down into 3 parts:

6. SUCCESS OF MACHINE DEPENDS ON
TWO FACTORS:
 How well the generalization of abstraction data take
place.
 How well the machine is able to put it’s learning into
practical usage for predicting the future course of action.

7. WHAT ARE THE STEPS USED IN
MACHINE LEARNING?
Collecting data: Be it the raw data from excel, access, text files
etc., this step (gathering past data) forms the foundation of the future
learning.The better the variety, density and volume of relevant data,
better the learning prospects for the machine becomes.
Preparing the data: Any analytical process thrives on the quality
of the data used. One needs to spend time determining the quality of
data and then taking steps for fixing issues such as missing data and
treatment of outliers.

8. Training a model: This step involves choosing the appropriate algorithm and
representation of data in the form of the model.The cleaned data is split into two parts –
train and test the first part is used for developing the model.The second part (test data), is
used as a reference.
Evaluating the model: To test the accuracy, the second part of the data is used.
This step determines the precision in the choice of the algorithm based on the outcome. A
better test to check accuracy of model is to see its performance on data which was not
used at all during model build.
Improving the performance: This step might involve choosing a different model
altogether or introducing more variables to augment the efficiency.That’s why significant
amount of time needs to be spent in data collection and preparation.

10. Supervised Learning / Predictive models:
o Predictive model as the name suggests is used to predict
the future outcome based on the historical data.
o Predictive models are normally given clear instructions
right from the beginning as in what needs to be learnt and
how it needs to be learnt.
o These class of learning algorithms are termed as
Supervised Learning.

11. FOR EXAMPLE
o Supervised Learning is used when a marketing company is
trying to find out which customers are likely to churn.
o We can also use it to predict the likelihood of occurrence of
perils like earthquakes, tornadoes etc. with an aim to determine
theTotal InsuranceValue. Some examples of algorithms used
are: Nearest neighbour, Naïve Bayes, DecisionTrees, Regression
etc.

12. UNSUPERVISED LEARNING /
DESCRIPTIVE MODELS:
o It is used to train descriptive models where no target is set and no
single feature is important than the other.
o The case of unsupervised learning can be:When a retailer wishes to
find out what are the combination of products, customers tends to buy
more frequently. Furthermore, in pharmaceutical industry,
unsupervised learning may be used to predict which diseases are likely
to occur along with diabetes. Example of algorithm used here is: K-
means Clustering Algorithm

13. REINFORCEMENT LEARNING (RL):
o It is an example of machine learning where the machine is trained to take
specific decisions based on the business requirement with the sole motto to
maximize efficiency (performance).
o The idea involved in reinforcement learning is:The machine/ software agent
trains itself on a continual basis based on the environment it is exposed to,
and applies it’s enriched knowledge to solve business problems.
o This continual learning process ensures less involvement of human
expertise which in turn saves a lot of time!

14. WHAT ARE THE APPLICATIONS OF
MACHINE LEARNING?
 Banking & Financial services: ML can be used to predict
the customers who are likely to default from paying loans or credit
card bills.This is of paramount importance as machine learning would
help the banks to identify the customers who can be granted loans and
credit cards.
 Healthcare: It is used to diagnose deadly diseases (e.g. cancer)
based on the symptoms of patients and tallying them with the past
data of similar kind of patients.

15. Retail: It is used to identify products which sell more
frequently (fast moving) and the slow moving products which
help the retailers to decide what kind of products to introduce
or remove from the shelf.
 Also, machine learning algorithms can be used to find which
two / three or more products sell together.This is done to design
customer loyalty initiatives which in turn helps the retailers to
develop and maintain loyal customers.

16. HOW IS MACHINE LEARNING
DIFFERENT FROM A.I?
It refers to the procedure of programming a computer (machine)
to take rational.
the central motive behind using AI is to achieve the computer
(machine) behave in a dandy fashion in lieu of human guidance
instead of being doltish!
AI may include programs to check whether certain parameters
within a program are behaving normally.

17. HOW IS MACHINE LEARNING
ALREADY PRESENT IN OUR
DAILY LIVES?

18. Amazon.com

20. NETFLIX

21. VERDICT
 There are many subtleties and pitfalls in ML, and many ways to be
lead astray by what appears to be a perfectly well-tuned thinking
machine.
 Machine Learning is an incredibly powerful tool. In the coming years,
it promises to help solve some of our most pressing problems, as well
as open up whole new worlds of opportunity.
 The demand for ML engineers is only going to continue to grow,
offering incredible chances to be a part of something big.

22. THANKS A LOT!
Now may ask your queries.