This will give you a brief introduction of machine learning.

1. SEMINAR PRESENTATION
ON
INTRODUCTION OF MACHINE LEARNING
Km Pooja
Section: D
Roll No: 1918425
B.tech(CSE) V sem.

2. Table of content
1. Introduction
2. Machine Learning vs. Traditional Programming
3. Types of machine learning
 Supervised machine learning
 Unsupervised machine learning
 Reinforcement learning
4. Concept of learning in ML System
5. Applications of machine learning
6. Conclusion

3. Introduction To Machine Learning
 Machine Learning is an application of artificial intelligence (AI)
that provides systems the ability to automatically learn and improve
from past experience without being explicitly programmed.
 Machine Learning focuses on the development of computer
programs that can access data and use it.

4. Machine Learning vs. Traditional Programming
 In traditional programming language we have to provide
input and on this input a predefined instruction going to
perform and result will be output.
 Whereas in machine learning algorithms, we simply
provide input as well as data associated with it this is done
during learning phase and rest of the things program going
to do itself.
 Next time onwards we don’t require this kind of facility to
be provided, we just need a value or parameter for which
we want the prediction.

5. AI vs MLvs Deep Learning

6. Types of machine learning

7. Supervise Machine Learning
 Supervised learning uses classification algorithms and regression
techniques to develop predictive models.
 Classification separates the data, regression fits the data.
 Supervised learning as the name indicates the presence of a
supervisor as a teacher. Basically supervised learning is a learning
in which we teach or train the machine using data which is well
labeled that means some data is already tagged with the correct
answer. After that, the machine is provided with a new data set of
examples (data) so that supervised learning algorithm analyses the
training data (set of training examples) and produces a correct
outcome from labeled data.

8. Disadvantages of Supervised Learning.
 Computation time is vast for supervised learning.
 Unwanted data downs efficiency.
 Pre-processing of data is no less than a big challenge.
 Always in need of updates.
 Anyone can overfit supervised algorithms easily.

9. Unsupervised Learning
 Unsupervised learning is a machine learning technique,
where you do not need to supervise the model. Instead, you
need to allow the model to work on its own to discover
information. It mainly deals with the unlabeled data.
 Unsupervised learning algorithms allows you to perform
more complex processing tasks compared to supervised
learning. Although, unsupervised can be more
unpredictable compare with the natural learning methods.

10. Advantages of Unsupervised Learning
 Labeling of data demands a lot of manual work and expenses.
Unsupervised learning solves the problem by learning the
data and classifying it without any labels.
 It is very helpful in finding patterns in data, which are not
possible to find using normal methods.
 This is the perfect tool for data scientists, as unsupervised
learning can help to understand raw data.
 We can also find up to what degree the data are similar.This
can be accomplished with probabilistic methods.
 This type of learning is similar to human intelligence in some
way as the model learns slowly and then calculates the result.

11. Disadvantages of Unsupervised Learning
 The result might be less accurate as we do not have any input data
to train from.
 The model is learning from raw data without any prior knowledge.
 It is also a time-consuming process.The learning phase of the
algorithm might take a lot of time, as it analyses and calculates all
possibilities.
 For some projects involving live data, it might require continuous
feeding of data to the model, which will result in both inaccurate
and time-consuming results.
 The more the features, the more the complexity increases.

12. Reinforcement Learning :
 Reinforcement learning is an area of machine learning . It is
about taking suitable action to maximize reward in a particular
situation. It is employed by various software and machines to
find the best possible behavior or path it should take in
specific situation.
 Reinforcement learning differs from the supervised learning in
a way that in supervised learning the training data has the
answer key with it so the model is trained with correct answer
itself whereas in reinforcement learning, there is no answer but
the reinforcement agent decides what to do to perform the
given task. In the absence of training dataset, it is bound to
learn from its experience.

13. Continue…
 Reinforcement learning is a feedback-based machine learning
technique in which an agent learns to behave in an environment by
performing the action and seeing the result of actions.
 For each good action the agent get positive feedback and for each
bad action the agent get negative feedback or penalty.
 In reinforcement learning, the agent learn, automatically using
feedback without any labeled data, unlike supervised learning.
 Since there is no labeled data, so the agent is bound to learn by it’s
experience only.
 RL solve specific type of problem where decision making is
sequential and the goal is long term, such as game-playing ,
robotics, etc.

14. Applications Of Machine Learning

19. Conclusion
 As we move foreword into the digital age, our technology
continues to leaps and strides forward. This incredible form
of Artificial Intelligence is already being used in various
industries and professions. From making, to medicine and
web security. This technology can improve our lives in
several numerous ways.