This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.

1. Introduction to
Machine
Learning
Presented By: Rahul Khanna
Software Consultant
Knoldus Inc.

2. 01 What is Machine Learning
02 Stages of Machine Learning
03 Who is using Machine Learning ?
04 Types of Machine Learning Algorithms
05 Project: Analyze FIFA 19 Dataset
Our Agenda

3. c
What is
Machine
Learning
1. Machine learning is a method
of data analysis that
automates analytical model
building
2. It is a branch of artificial
intelligence based on the idea
that systems can learn from
data, identify patterns and
make decisions with minimal
human intervention.
A computer program is said to learn from experience E
with respect to some class of tasks T and performance
measure P, if its performance at tasks in T, as
measured by P, improves with experience E.
Machine Learning is training a model from a data that
generalizes a decision against a performance
measure
- Jason Brownlee
The key to artificial intelligence has always been the
representation.
- Jeff Hawkins
- Tom Mitchell

4. Collect Data
1. Identify Data Sets
2. Retrieve Data
3. Query Data
Analyze
1. Select Analytical
Techniques
2. Build Models
Act
Report
1. Create Visualizations
2. Communicate results
Prepare Data
1. Explore Data
2. Preprocess Data
Stages of Machine Learning

5. 1 2
3
4
5Transportation
Analyzing data to identify
patterns and trends is key to the
transportation industry, which
relies on making routes more
efficient and predicting
potential problems to increase
profitability.
Financial services
Banks and other businesses in the financial
industry use machine learning technology for
two key purposes: to identify important
insights in data, and prevent fraud
Health care
Machine learning is a fast-growing trend
in the healthcare industry, thanks to the
advent of wearable devices and sensors
that can use data to assess a patient's
health in real time. The technology can
also help medical experts analyze data to
identify trends or red flags that may lead
to improved diagnoses and treatment.
Government
Government agencies such as public safety and
utilities have a particular need for machine
learning since they have multiple sources of data
that can be mined for insights. Analyzing sensor
data, for example, identifies ways to increase
efficiency and save money.
Retail
Websites recommending items you might like based on previous
purchases are using machine learning to analyze your buying
history. Retailers rely on machine learning to capture data, analyze
it and use it to personalize a shopping experience
Who is using Machine Learning ?

6. Supervised learning
01
Semisupervised
learning03
Unsupervised
learning
02
Reinforcement
learning04
Types of Machine Learning Algorithms

7. c
Supervised
Learning
These algorithms are trained using labeled
examples, such as an input where the desired
output is known. For example, a piece of
equipment could have data points labeled
either “F” (failed) or “R” (runs). The learning
algorithm receives a set of inputs along with
the corresponding correct outputs, and the
algorithm learns by comparing its actual
output with correct outputs to find errors. It
then modifies the model accordingly. Through
methods like classification, regression,
prediction and gradient boosting, supervised
learning uses patterns to predict the values of
the label on additional unlabeled data.

8. c
Unsupervised
Learning
Unsupervised learning is where you only have
input data (X) and no corresponding output
variables.
The goal for unsupervised learning is to model
the underlying structure or distribution in the
data in order to learn more about the data.
These are called unsupervised learning
because unlike supervised learning above
there is no correct answers and there is no
teacher. Algorithms are left to their own
devises to discover and present the interesting
structure in the data.

9. c
Semi Supervised
Learning
It is used for the same applications as
supervised learning. But it uses both labeled
and unlabeled data for training – typically a
small amount of labeled data with a large
amount of unlabeled data (because
unlabeled data is less expensive and takes less
effort to acquire). This type of learning can be
used with methods such as classification,
regression and prediction. Semisupervised
learning is useful when the cost associated
with labeling is too high to allow for a fully
labeled training process. Early examples of this
include identifying a person's face on a
webcam.

10. c
Reinforcement
Learning
It is often used for robotics, gaming and
navigation. With reinforcement learning, the
algorithm discovers through trial and error
which actions yield the greatest rewards. This
type of learning has three primary
components: the agent (the learner or
decision maker), the environment (everything
the agent interacts with) and actions (what
the agent can do). The objective is for the
agent to choose actions that maximize the
expected reward over a given amount of time.
The agent will reach the goal much faster by
following a good policy. So the goal in
reinforcement learning is to learn the best

14. Linear Regression

15. Logistic Regression

16. Naïve Bayes

17. Decision Trees

18. K - Means

19. Apriori

20. Thank You!
@Knolspeak
/KnoldusSoftware