Supervised Learning in Machine Learning > Regression Algoritms

Simple Linear Regression

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To model the relationship between two continuous variables, simple linear regression is utilized. The goal is frequently to anticipate the value of an output variable (or responder) based on the value of an input variable (or predictor).

Linear regression is straightforward to execute and the output coefficients are easier to interpret. This algorithm is the best to employ when you know the independent and dependent variables have a linear relationship because it is less difficult than other algorithms.

Linear-regression models have become a proven way to scientifically and reliably predict the future. Because linear regression is a long-established statistical procedure, the properties of linear-regression models are well understood and can be trained very quickly.

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Rohit Khare
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What will be the mandatory requirement of configuration of PC for this ML tool

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Muhammad Fahad Bashir
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Explained the concept easily

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Pradeep Kumar Kaushik
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Please give me iris,csv file.

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Ankit Malik
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where is the finaldata.csv

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Vimal Bhatt
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great learning plateform kushal sir is really too good

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good

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Prabhat Yadav
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Superb course content and easy to understand.

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fahad ameer
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good

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  1. Introduction to Machine Learning
    1m 5s
  2. Environment Setup part 1
    3m 26s
  3. Environment Setup part 2
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  4. Environment Setup part 3
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  1. Data Wrangling
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  2. Importing Libraries and Dataset
    11m 19s
  3. Handling Missing Data
    11m 51s
  4. Handling Missing Data - Practical
    17m 3s
  5. Encoding Categorical Data
    6m 34s
  6. Encoding Catergorical Data - Practical
    8m 3s
  7. Splitting Dataset
    3m 38s
  8. Splitting Dataset - Practical
    9m 36s
  9. Normalizing the Data - Part 1
    6m 18s
  10. Normalizing the Data - Part2
    6m 58s
  11. Finding Machine Learning Datasets
    9m 17s
  12. Exploratory Data Analysis
    9m
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    19m 12s
  14. Plotting Graphs - Part 2
    23m 44s
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    33m 54s
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  17. Assignment of Data Preprocessing for Machine Learning
    1m 39s

  1. Machine Learning Paradigms
    9m 53s
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    6m 7s
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    9m 49s
  5. Assignment of Machine Learning Paradigms
    3m 24s

  1. Overview of Classification
    5m 22s
  2. Logistic Regression
    4m 54s
  3. Logistic Regression - Practical
    32m 40s
  4. KNN
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  5. KNN - Practical
    35m
  6. Decision Trees for Classification
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  7. Decision Trees Practical - 1
    22m 41s
  8. Decision Trees Practical - 2
    20m 42s
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  10. Support Vector Machine Classifier
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  11. Support Vector Machine Classifier - Practical 1
    16m 59s
  12. Support Vector Machine Classifier - Practical 2
    11m 18s
  13. Naive Bayes Classifier
    5m 25s
  14. Naive Bayes Classifier - Practical 1
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  15. Naive Bayes Classifier - Practical 2
    11m 7s
  16. Evaluating Classification Models Performance
    7m 31s
  17. Evaluating Classification Models Performance - Practical
    33m 2s
  18. Assignment of Supervised Learning Algorithms
    1m 12s

  1. Simple Linear Regression
    8m 57s
  2. Simple Linear Regression - Practical
    12m 34s
  3. Multi-Linear Regression
    3m
  4. Support Vector Regressor
    10m 47s
  5. Support Vector Regressor - Practical
    6m 47s
  6. Decision Tree Regressor
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  7. Decision Tree Regressor - Practical
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  8. Regressor Model Selection
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  9. Evaluating Regression Model Performance
    8m 45s
  10. Evaluating Regression Model Performance - Practical
    11m 34s
  11. Assignment of Regression Algoritms
    1m 12s

  1. Advance Trends in Machine Learning
    10m 13s

  1. Course Summary
    3m 51s

  1. Interview Questions Part 1
    19m 22s
  2. Interview Questions Part 2
    12m 23s
  3. Interview Questions Part 3
    15m 20s

  1. Career Guidelines
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