This document provides an introduction to machine learning for data science. It discusses the applications and foundations of data science, including statistics, linear algebra, computer science, and programming. It then describes machine learning, including the three main categories of supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms covered include logistic regression, decision trees, random forests, k-nearest neighbors, and support vector machines. Unsupervised learning methods discussed are principal component analysis and cluster analysis.

1. MACHINE LEARNING FOR DATA SCIENCE INTRODUCTION
By
NIKHIL GR
STUDENT
CSE
SJCIT

2. Contents
• Introduction to Data Science
• Applications of Data Science
• Foundations of Data Science
• Machine Learning
• Supervised Learning
• Classification
• Logistic Regression
• Decision Tree
• Random Forest
• K-Nearest Neighbor
• Support Vector Machine
• Regression
• Unsupervised Learning
• Cluster Analysis
• Principal Component Analysis

3. Introduction to DataScience
• Data science is a multi-disciplinary field which uses scientific
methods, processes, algorithms and systems to extract knowledge
and insights from structured and unstructured data.
• It is a blend of computer Science, Mathematics and
business/domain expertise.

4. Applications of DataScience

5. Foundations of DataScience
• Statistics: Descriptive, Inferential.
• Linear Algebra: Matrices, Planes, Vectors, etc.
• Computer Science: Algorithm, Graph Theory, Data Structure,
DBMS, etc.
• Machine Learning: Supervised, Unsupervised, Reinforcement.
• Business Analytics: Predictive, Prescriptive, Descriptive,
Decision.
• Programming: R/Python, SQL, NoSQL.

7. Machine Learning
• Machine learning is a subfield of computer science which focuses to
develop the computer algorithm to learn from examples and improve
the performance of a task.
• The algorithms in machine learning use training data which is the set
of past observations.
• There are three broad categories of machine learning:
 Supervised Learning: Which learns from labeled examples.
 Unsupervised Learning: Which learns from unlabeled examples.
 Reinforcement Learning: Which learns from environment through feedbacks.
• It develops predictive analytics models which allow researchers, data
scientists to predict about future based on past and current data.

8. SupervisedLearning
• It is a category of machine learning algorithms. As name indicates, it
is supervised by the presence of output in the training data.
• It learns from the labelled data – input for which output is known.
• It builds a mathematical model of a set of data that contains both the
inputs and the desired outputs.
• A supervised learning algorithm analyzes the training data and
produces an inferred function, which can be used for mapping new
examples.
• Generally, all the supervised learning problems are classified into
Classification and Regression problems.

9. Classification
• Classification in machine learning is a supervised learning
problem where the output variable is a category, such as “yes”
or “no” or “disease” and “no disease”.
• In this problem, the dependent variable is categorical whose
category is predicted based on several independent variables.
• A classification model attempts to draw some conclusion from
observed values.
• Given one or more inputs a classification model will try to predict
the value of one or more outcomes.
• There are a number of classification models.

10. Classification through machine learning
algorithms
Following are the popular machine learning algorithms which are
used in classification problems:-
• Logistic Regression
• Decision Tree
• Random Forest
• K-Nearest Neighbor
• Support Vector Machine

11. LogisticRegression
• This regression model is used when the dependent variable is
categorical.
• There are binary outputs of categories in this case.

12. DecisionTree
• A Decision tree is a flowchart like tree structure, where each
internal node denotes a test on an attribute, each branch
represents an outcome of the test, and each leaf node holds a
class label.
Example:-

13. RandomForest
• Randomforests or random decisionforest is an ensemble
learning method that consists a large number of decision trees.
• Each individual tree in the random forest spits out a class
prediction and the class with the most votes becomes our
model’s prediction.
Example:

14. K-NearestNeighbor
• In k-NN classification, the output is a class membership of a
new observation.
• An object is classified by a plurality vote of its neighbors, with
the object being assigned to the class most common among its
k nearest neighbors.
• Example:

15. Support VectorMachine
• In Support Vector Machine (SVM), we plot each data item as a
point in n-dimensional space (where n is the number of features
you have) with the value of each feature being the value of a
particular coordinate.
• Then, we perform classification by finding the
hyperplane that differentiate the two classes very well.
• To identify the hyperplane, we try to maximize the distance
between boundary elements of separated classes.
• Variety of kernel functions are used to separate observations
based on whether they are linear separable or non-linearly
separable.

17. Regression
• Regression in machine learning is supervised learning problem
where the output variable is a real or continuous value, such as
“salary” or “weight”.
• Many different models can be used, the simplest is the linear
regression.
• It tries to fit data with the best hyper-plane which goes through the
points.
• There are various techniques used for regression analysis such as
Linear Regression, Decision Tree Regression, Random Forest
Regression etc.

18. UnsupervisedLearning
• Unsupervised learning is performed on the unlabeled data –
there are no input output labels (categories) are given in the
data.
• Here the task of machine is to group unsorted information
according to similarities, patterns and differences without any
prior training of data.
• Two of the main methods used in unsupervised learning are:
• Principal component Analysis, and
• Cluster analysis.

19. Principal ComponentAnalysis
• Principal component analysisis a method of extracting
important variables from a large set of variables available in a
data set.
• It extractslow dimensional set of features from a high
dimensional data set with a motive to capture as much
information as possible.

20. ClusterAnalysis
Vaibhav Kumar@DIT
University
• Cluster analysis or clustering is the task of grouping a set of
objects in such a way that objects in the same group (called a
cluster) are more similar (in some sense) to each other than to
those in other groups (clusters).
• Cluster analysis can be achieved by various algorithms that
differ significantly in their understanding of what constitutes a
cluster and how to efficiently find them.
Example:

21. ThankingYou