This document provides an introduction to machine learning, including the life cycle of machine learning/data science projects and various algorithms. It discusses the main types of machine learning (supervised vs. unsupervised), describes some common classification and regression algorithms, and gives examples of use cases like healthcare, security, recommendation systems, and more. The life cycle includes steps like data selection, description, transformation, model training, evaluation, and deployment.

1. Machine Learning - A
walkthrough
By:
Akshada Bhandari
Data Scientist Intern
-Vasundharaa Geo Technologies
ML Lead at GDSC

2. Contents :
● Introduction to Machine
Learning
● Life cycle about Machine
Learning/Data Science
Projects
● Algorithms
● Use Cases

3. Introduction
Machine Learning is the study of
computer algorithms that can
improve automatically through
experience and by the use of
data.
AI
ML
DS
DL

4. Machine Learning
& Types:

5. Life Cycle of ML/DS Projects :
● Data Selection
● Data Description
● Performing Statistical and Graphical Data Analysis
● Data Transformation (if necessary)
● Selection of ML algorithms based on patterns
observed in EDA
● Data Standardization and Normalization
● Train test split
● Model training
● Evaluation metrics
● Deployment

6. The Classification algorithm is a Supervised Learning technique that is
used to identify the category of new observations on the basis of
training data. In Classification, a program learns from the given
dataset or observations and then classifies new observation into a
number of classes or groups. Such as, Yes or No, 0 or 1, Spam or
Not Spam, cat or dog, etc. Classes can be called as targets/labels or
categories.
Classification

7. Regression in statistics is the process of predicting a Label(or Dependent
Variable) based on the features(Independent Variables) at hand. Regression
is used for time series modelling and finding the causal effect relationship
between the variables and forecasting. For example, the relationship
between the stock prices of the company and various factors like customer
reputation and company annual performance etc. can be studied using
regression.
Regression

8. Supervised ML :
● Linear Regression
● Logistic Regression
● SVM
● Naive Bayes
● Decision Trees
● Random Forest
● Ada Boost
● Gradient Boosting
● XG Boost
● KNN
● Time Series (Arimas, Sarinas)
Algorithms

9. Unsupervised ML :
● K means clustering
● DBScan
● K - Neigbours
● PCA (Principal Component Analysis)
Algorithms

10. Use Cases :
Linear Regression
In statistics, linear regression is a
linear approach for modelling the
relationship between a scalar
response and one or more
explanatory variables (also known as
dependent and independent
variables). The case of one
explanatory variable is called simple
linear regression; for more than one,
the process is called multiple linear
regression

11. Logistic Regression
Logistic regression is a
statistical model that in its basic
form uses a logistic function to
model a binary dependent
variable, although many more
complex extensions exist. In
regression analysis, logistic
regression[1] (or logit regression)
is estimating the parameters of
a logistic model (a form of
binary regression).

13. ● Healthcare
● Security
● NLP
● Robotics
● Recommendation Systems
● Finance Sectors
● E-commerce
● Automation
Applications :