This document provides an introduction to machine learning basics for first year students. It discusses different types of data like text, audio, video, structured, and semi-structured data. It also gives examples of machine learning applications using these different data types, such as product recommendations from text data, cancer prediction from health records, and autonomous vehicles from video data. Finally, it outlines concepts for working with structured data, including exploratory data analysis, preprocessing, training and test sets, supervised learning algorithms, unsupervised learning clustering, and model evaluation.

1. Machine Learning
Basics
(For 1st Year)

2. Contents
INTRODUCTION
1. Data Acquisition to KDD, Importance of data mining (overview of the process)
2. Different forms of data – Text (unstructured), Audio, video, Structured data (csv files), Semi structured data (Log
files, xml files)
3. Applications associated – Product Recommendation System (text data), Cancer prediction based on health records
data (text data) , Auto driving cars (video data) etc.
STRUCTURED DATA
1. Knowing your data (Exploratory Data Analysis)
2. Preprocessing
3. Concept of training & test sets
4. Supervised Learning – Classification (Decision Trees, kNN, SVM) & Regression (Linear Regression, MLR)
5. Unsupervised Learning – Clustering (K-means)
6. Model Evaluation

3. Data Acquisition to KDD, Importance of data
mining (overview of the process)

4. Little Background

5. Different forms of data
• Unstructured Data – Text,Audio, video,
• Structured data (csv files),
• Semi structured data (Log files, xml files)

6. Applications which use ML
• Product Recommendation System (text data),
• Cancer prediction based on health records data (text data) ,
• Auto driving cars (video data) etc.

7. STRUCTURED DATA

8. Knowing your data
• Nominal Attributes -
• Binary Attributes,
• Ordinal Attributes,
• Numeric Attributes,
• Discrete (e.g zip codes, profession or set of words) Sometimes, represented as integer variables
• Note: Binary attributes are a special case of discrete attributes
• Continuous Attributes
• Has real numbers as attribute values
• E.g., temperature, height, or weight
• Basic Statistical Descriptions of data

9. Knowing your Data & Preprocessing
• Preprocessing Steps :
• Data Cleaning
• Data Integration
• Data Reduction
• Data Transformation and Data
Discretization
Basic Statistical
Descriptions of
data
Measuring central
tendency Mean, median,mode
Measuring
dispersion of data
Range, Quratiles, Variance,
Standard Deviation,
Interquatile range
Graphic displays Box plot…

10. Concept of training & test sets