Machine Learning Algorithms (Part 1)

DIFFERENTIATE
ARTIFICIAL
INTELLIGENCE,
MACHINE
LEARNING AND
DEEP LEARNING
01
UNDERSTAND THE
NEED FOR
MACHINE
LEARNING
02
ILLUSTRATE
MACHINE
LEARNING
PROCESS
03

UNDERSTAND THE
TYPES OF MACHINE
LEARNING
04
DETERMINE THE
TYPE OF PROBLEMS
SOLVED USING
MACHINE
LEARNING
05
PRACTICAL
IMPLEMENTATION
OF MACHINING
LEARNING
06

• It covers anything which
enables the computers to
behave like humans.
• It deals with the
extraction of patterns
from a large data sets.
• It deals to train Deep
Neural Networks so as to
achieve better accuracy in
those cases where former
was not performing up to
the mark.

•2.5 quintillion bytes of data every single day
•Estimated by 2020, 1.7MB of data will be
created every second for every person on
earth.
•predictive models that can study and
analyze complex data
Source: Google Images
•Netflix and Amazon

Netflix’s Recommendation Engine
Facebook’s Auto-tagging feature
Amazon’s Alexa
Google’s Spam Filter

Task: How will you choose the best mangoes?

Experience 1:
You were informed that bright and yellow
mangoes are sweeter than pale and yellow ones.
So you make a simple rule: pick only from the
bright yellow mangoes. You check the colour of
the mangoes, pick the bright yellow ones, pay up,
and return home. Right?
Learning 1:
Bright yellow mangoes are sweeter than pale
yellow ones

Experience 2:
Now when you went home and tasted the
mangoes, some of them were not sweet as you
thought. You are worried as your wisdom was
insufficient. You concluded that when it comes
shopping mangoes, you have to look for more
than just the colors.
After a lot of pondering and tasting different
types of mangoes,

Experience 2:
you concluded that the bigger and bright yellow
mangoes are guaranteed to be sweet, while the
smaller, bright yellow mangoes are sweet only
half the time (i.e. if you bought 100 bright
yellow mangoes (50 will be big in size and rest
50 will be small), then the 50 big mangoes will
all be sweet, while out of the 50 small ones,
only 25 mangoes will turn out to be sweet). You
will then update your rule about the mango
shopping and from next time you will keep this
in mind.

Learning 2:
The smaller and bright yellow mangoes are
sweet only half the time.

Experience 3:
Tragedy: Next time at the market, you see that
your favorite vendor has gone out of town. You
decide to buy from a different vendor, who
supplies mangoes grown from a different part of
the country. Now, you realize that the rule which
you had learnt (that big, bright yellow mangoes
are the sweetest) is no longer applicable. You have
to learn from scratch. You taste a mango of each
kind from this vendor and realize that the small,
pale yellow ones are in fact the sweetest of all.
Learning 3:
Small, pale yellow ones are the sweetest of all.

Experience 4:
One day your cousin visits you from another city.
You decide to treat her with mangoes. But she is
like “I don’t care about the sweetness of a mango,
I only want the juiciest ones”. Now once again,
you run your experiments, tasting all kinds of
mangoes, and realizing that the softer ones are
juicier.
Learning 4:
Soft mangoes are juicier

Experience 5:
Later on, you move to a different part of the
world and you found that the mangoes here taste
surprisingly different from your home country.
You realized that for this country the green
mangoes are tastier than the yellow ones.
Learning 5:
Green mangoes are tastier than yellow one

Experience 6:
You marry someone who hates mangoes but loves
oranges instead. Now you go for shopping
oranges instead of mangoes. Now, all your
accumulated knowledge about mangoes is
worthless. Now you have to learn everything
about the correlation between the physical
characteristics and the taste of apples, by the same
method of experimentation.
Learning 6:
You don’t need mangoes anymore

What if you have to
write a code for it?

if is bright yellow and
size is big and sold by:
mango is sweet.
if (soft): mango is juicy

But every time you
make a new
observation from your
experiments, you have
to modify the list of
rules manually.

Task: How will you choose the best apple?

This is where
Machine
Learning comes
into the picture

“Machine Learning is a concept which
allows the machine to learn from
examples and experience, and that too
without being explicitly programmed.
So instead of you writing the code,
what you do is you feed data to the
generic algorithm, and the
algorithm/machine builds the logic
based on the given data.”

• an American pioneer in the field of
computer gaming and artificial
intelligence
Arthur Lee Samuel
• popularized the term "machine
learning" in 1959

Model:
A model is the main component of Machine Learning. A model is
trained by using a Machine Learning Algorithm. An algorithm maps
all the decisions that a model is supposed to take based on the given
input, in order to get the correct output.
Predictor Variable:
It is a feature(s) of the data that can be used to predict the output.
Response Variable:
It is the feature or the output variable that needs to be predicted by
using the predictor variable(s).

Training Data:
The Machine Learning model is built using the training data. The
training data helps the model to identify key trends and patterns
essential to predict the output.
Testing Data:
After the model is trained, it must be tested to evaluate how accurately
it can predict an outcome. This is done by the testing data set.

Step 1: Define the objective of the Problem Statement
• At this step, we must understand what exactly needs to be
predicted.
• In our case, the objective is to predict the possibility of rain
by studying weather conditions.
• At this stage, it is also essential to take mental notes on
what kind of data can be used to solve this problem or the
type of approach you must follow to get to the solution.

Step 2: Data Gathering
• What kind of data is needed to solve this problem?
• Is the data available?
• How can I get the data?

Step 4: Exploratory Data Analysis
• Grab your detective glasses because this stage is all about
diving deep into data and finding all the hidden data
mysteries. EDA or Exploratory Data Analysis is the
brainstorming stage of Machine Learning.
• Data Exploration involves understanding the patterns and
trends in the data.
• At this stage, all the useful insights are drawn and
correlations between the variables are understood.

Step 5: Building a Machine Learning Model
• All the insights and patterns derived during Data
Exploration are used to build the Machine Learning
Model.
• This stage always begins by splitting the data set into two
parts, training data, and testing data.
• The training data will be used to build and analyze the
model.
• The logic of the model is based on the Machine Learning
Algorithm that is being implemented.

Step 6: Model Evaluation & Optimization
• After building a model by using the training data set, it is
finally time to put the model to a test.
• The testing data set is used to check the efficiency of the
model and how accurately it can predict the outcome.
• Once the accuracy is calculated, any further improvements
in the model can be implemented at this stage.
• Methods like parameter tuning and cross-validation can be
used to improve the performance of the model.

Step 7: Predictions
• Once the model is evaluated and improved, it is finally
used to make predictions.
• The final output can be a Categorical variable (eg. True or
False) or it can be a Continuous Quantity (eg. the
predicted value of a stock).

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

• a technique in which we teach or train the machine using data
which is well labeled.

• Y = f(X)
• every instance of
the training
dataset consists of
input attributes
and expected
output

• The training
dataset can take any
kind of data as an
input like values of
a database row, the
pixels of an image,
or even an audio
frequency
histogram.

• involves training by using unlabeled data and allowing the model
to act on that information without guidance

• input data (X) and
no corresponding
output variables
• to model the
underlying structure
or distribution in
the data in order to
learn more about
the data.

• dataset does not
have an expected
• can detect patterns
based on the typical
characteristics of the
input data
• Clustering

• a part of Machine learning where an agent is put in an
environment and he learns to behave in this environment by
performing certain actions and observing the rewards which it gets
from those actions

• hit and trial method of
learning
• all about the interaction
between the environment
and the learning agent
• exploration and
exploitation

Machine Learning Algorithms (Part 1)

Machine Learning Algorithms (Part 1)

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