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Machine Learning - Introduction

Note - This is handmade notes, for best learning please refer-

1] CampusX - https://www.youtube.com/@campusx-official

2] Tutorial Point

3] SimpliLearn

Machine Learning

Machine Learning is a field of computer science that uses statistical techniques to give computer system the ability to “learn” with data, without being explicitly programmed.

 

Data Analysis-

Data Analysis is to extract useful information or finding hidden pattern from data by plotting graph and all , and taking the decision based upon the data analysis.

A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it.

Data Mining-

Some hidden pattern cannot be extracted from data analysis. For example finding whether the mail is spam or not. In that case, we will use data mining by applying machine learning model on large dataset to identify patterns or trends in the data that is being mined.

Question Time
Machine Learning

Machine Learning

  • Introduction
  • Overview
    • Type Of Machine Learning
    • Batch Vs Online Machine Learning
    • Instance Vs Model Based Learning
    • Challenges in Machine Learning
    • Machine Learning Development Life Cycle
  • Machine Learning Development Life Cycle
    • Framing the Problem
    • Data Gathering
    • Understanding your Data
    • Exploratory Data Analysis (EDA)
    • Feature Engineering
    • Principal Component Analysis
    • Column Transformer
    • Machine Learning Pipelines
    • Mathematical Transformation
    • Binning and Binarization | Discretization | Quantile Binning | KMeans Binning
  • Supervised Learning
    • Overview
    • Linear Regression [Regression]
    • Multiple Linear Regression
    • Polynomial Linear Regression [Regression]
    • Bias Variance Trade Off
    • Regularization
    • LOGISTIC REGRESSION [Regression & Classification]
    • Polynomial Logistic Regression
    • Support Vector Machines / Support Vector Regressor
    • Naïve Bayes Classifier [classification]
    • Decision Tree
    • Entropy
    • Information Gain
    • K Nearest Neighbor (KNN)
    • Neural Network (MultiLayer Perceptron)
  • Ensemble Learning
    • Introduction to Ensemble Learning
    • Basic Ensemble Techniques
    • Advanced Ensemble Techniques
    • Random Forest Classifier
    • Boosting
  • UnSupervised Learning
    • Overview
    • K Mean Clustering

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