General Introduction to Machine learning

1. GOKUL K S
RESEARCH SCHOLAR
CENTRAL UNIVERSITY OF TAMILNADU
P. KATHIRAVAN
RESEARCH SCHOLAR
CENTRAL UNIVERSITY OF TAMILNADU

3. Learning
Learning is the act of acquiring new or reinforcing existing
knowledge, behaviours, skills or values. Humans have the
ability to learn, however with the progress in artificial
intelligence, machine learning has become a resource which
can augment or even replace human learning.

4. Types of Learning
Learning under expert guidance
Learning guided by knowledge gained from experts.
Learning by self

6. Machine learning is a form of Artificial Intelligence that
enables a system to learn from data rather than through
explicit programming.
Or
Machine learning is the Scientific study of algorithms and
statistical models that computer systems use to perform a
specific task without using explicit instructions.
Machine Learning

9. Types of Learning
Learning can be supervised, semi-supervised,
unsupervised and reinforcement.
Supervised Learning
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.

11. Supervised Cont...
Supervised learning classified into two categories of
algorithms:
oClassification: A classification problem is when the output
variable is a category. Such as "Red" or "Green" or
"disease" and "no disease".
oRegression: A regression problem is when the output
variable is a real value. Such as "dollars" or "weight".

12. Supervised Cont...
Supervised learning algorithms:
Classification:
1. KNN(K-Nearest Neighbors)
2. Naïve-Bayes
3. SVM(Support Vector Machines)
4. Logistic Regression

13. Supervised Cont...
Regression:
1. Linear Regression
2. Decision Trees
3. Neural Networks

14. Unsupervised Learning
Unsupervised learning is the training of machine using
information that is neither classified nor labeled and
allowing the algorithm to act on that information without
guidance. The task of machine is to group unsorted
information according to the similarities, patterns and
differences without any prior training of data.

15. Unsupervised Cont...
Unsupervised learning classified into two categories of
algorithms:
o Clustering: A clustering problem is where you want to discover
the inherent groupings in the data, such as grouping customers
by purchasing behaviour.
o Association: An association rule learning problem is where you
want to discover rules that describe large portions of your data,
such as people that buy X also tend buy Y.

17. Unsupervised Cont...
Unsupervised learning Algorithms:
1. Clustering:
o K-means
o Hierarchical
o Hidden Markov Model

19. Reinforcement Learning
A reinforcement learning algorithm, or agent, learns by
interacting with its environment. The agent receives
rewards by performing correctly and penalites for
incorrectly.

21. Applications
 Image & Speech Recognition
 Medical Diagnosis
 Classification ( E-mail spam filtering)
 Prediction
 Learning Associations
 Online fraud Detection

22. Issues in Machine learning
Focusing too much on algorithms and theories
Using changing or premade tools
Having algorithms become obsolete as soon as data grows
Getting bad predictions to come together with biases
Making the wrong assumptions
Receiving bad recommendations
Having bad data convert to bad results

23. Machine learning Tools
oPyTorch
oTensorFlow
oWeka
oApache Mahout
oShogun
oPython
oR Programming

24. Thank You