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1. Written by
Muhammad Abrar
MACHINE LEARNING

2. HISTORY OF MACHINE LEARNING
1957 | First neural network for computers.
Frank Rosenblatt (Frank
Rosenblatt was an American psychologist notable in the field
of artificial intelligence) invented the perceptron (the first
neural network) to simulate the thought processes of the
human brain.
The name machine learning was coined in 1959
by Arthur Samuel.(Arthurs Samuel was an Amercian
pioneer in the field of computer gaming and Artifical
intelligence).

3. INTRODUCTION
What is machine learning?
Machine learning is an
application of artificial intelligence (AI) that provides
systems the ability to automatically learn and
improve from experience without being explicitly
programmed. Machine learning focuses on the
development of computer programs that can
access data and use it learn for themselves.

4. Machine learning (ML) is the scientific study of
algorithms and statistical models that computer
systems use to effectively perform a specific task
without using explicit instructions, relying on
patterns and inference instead. It is seen as a
subset of artificial intelligence. Machine learning
algorithms build a mathematical model of sample
data, known as "training data“.

5. AN ALGORITHM IS A STEP BY STEP METHOD OF SOLVING A
PROBLEM. IT IS COMMONLY USED FOR DATA PROCESSING,
CALCULATION AND OTHER RELATED COMPUTER AND
MATHEMATICAL OPERATIONS.

6. Statistical model:
• Statistical modeling is about applying statistics
on data or mathematical equations to encode
information extracted from the data.
• And statical models have some techniques.

7. SOFTWARE IS USED FOR MACHINE LEARNING
 TensorFlow( is used for dataflow and is also used for
machine learning applications such as neural networks)
written in (python , c++).
 Scikit –learn(is a machine learning library, it features
various classification,regression and clustering)
written in(python ,c,c++).
 XGBoost( written in c++),Pytorch(written in python,c++)
Torch( written in lua,c,c++),Weka( written in java).

8. USES:
• Human language understanding.
• Fault detection for safety and efficiency.
• Use for marketing,advertising.
• use for detection like text,speech,image,sound,human
behaviour and identity detection .
• Self driving car.

11. • What is Supervised Learning?
• Supervised Learning is the one, where you can
consider the learning is guided by a teacher. We
have a dataset which acts as a teacher and its
role is to train the model or the machine. Once
the model gets trained it can start making a
prediction or decision when new data is given to
it.

12. CLASSIFICATION AND REGRESSION
Classification:
The main goal of classification is to predict the target class (Yes/
No). If the trained model is for predicting any of two target classes.
It is known as binary classification. Considering the student profile
to predict whether the student will pass or fail. Considering
the customer, transaction details to predict whether he will buy the
new product or not. These kind problems will be addressed with
binary classification. If we have to predict more the two target
classes it is known as multi-classification.

13. Regression:
The main goal of regression algorithms is the predict
the discrete or a continues value. In some cases, the
predicted value can be used to identify the linear
relationship between the attributes. Suppose the
increase in the product advantage budget will increase
the product sales. Based on the problem difference
regression algorithms can be used.

14. EXAMPLE

15. WHAT IS UNSUPERVISED LEARNING?
• The model learns through observation and finds structures
in the data. Once the model is given a dataset, it
automatically finds patterns and relationships in the dataset
by creating clusters in it. What it cannot do is add labels to
the cluster, like it cannot say this a group of apples or
mangoes, but it will separate all the apples from mangoes.
• Suppose we presented images of apples, bananas and
mangoes to the model, so what it does, based on some
patterns and relationships it creates clusters and
divides the dataset into those clusters. Now if a new
data is fed to the model, it adds it to one of the created
clusters.

16. CLUSTERING:
• Clustering is a Machine Learning technique
that involves the grouping of data points. Given a set of
data points, we can use a clustering algorithm to
classify each data point into a specific group. In theory,
data points that are in the same group should have
similar properties and/or features, while data points in
different groups should have highly dissimilar
properties and/or features. Clustering is a method of
unsupervised learning and is a common technique for
statistical data analysis used in many fields.

17. EXAMPLE:

18. • What is Reinforcement Learning?
• It is the ability of an agent to interact with the
environment and find out what is the best
outcome. It follows the concept of hit and trial
method. The agent is rewarded or penalized with
a point for a correct or a wrong answer, and on
the basis of the positive reward points gained
the model trains itself. And again once trained it
gets ready to predict the new data presented to
it.

19. REINFORCMENT LEARNING METHOD:

20. THANKS