Supervised Learning in Machine Learning > Supervised Learning Algorithms

Overview of Classification

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Classification is the process of categorizing a set of data into classes. It can be done on both structured and unstructured data. Predicting the class of provided data points is the first step in the procedure. The classes are often referred to as target, label or categories

  • Supervised Learning: In this technique, the machine learns from labeled examples. The machine will get feedback on its mistakes and adjust itself accordingly.
  • Unsupervised Learning: This technique is used when the machine doesn't have any labels or feedback for its mistakes. It uses unlabeled data to figure out patterns for itself.
  • Reinforcement Learning: This technique is used when the goal of the machine is to maximize a reward signal or minimize a cost signal, but there are no specific labeled examples.

The ability to recognize objects and categorize them is a typical task of machine learning systems. This technique is known as classification, and it allows us to divide large amounts of data into discrete values, such as 0/1, True/False, or a pre-defined output label class.

The data needed to train the algorithm for supervised learning must already be labeled with correct responses. A classification algorithm, for example, will learn to recognize animals after being trained on a dataset of photos that have been appropriately tagged with the animal's species and some identifying traits.

Classifying is an investigative method that involves grouping or categorizing items or events. Classification and identification are crucial because they help us grasp the interactions and connections that exist between things. They also aid scientists in communicating clearly with one another.

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i could not download .pls share link

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

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Explained the concept easily

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Please give me iris,csv file.

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where is the finaldata.csv

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great learning plateform kushal sir is really too good

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Course Content

  1. Introduction to Machine Learning
    1m 5s
  2. Environment Setup part 1
    3m 26s
  3. Environment Setup part 2
    4m 43s
  4. Environment Setup part 3
    2m 33s

  1. Data Wrangling
    12m 26s
  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
  13. Plotting Graphs - Part 1
    19m 12s
  14. Plotting Graphs - Part 2
    23m 44s
  15. Distribution Models - Part 1
    33m 54s
  16. Distribution Models - Part 2
    13m 56s
  17. Assignment of Data Preprocessing for Machine Learning
    1m 39s

  1. Machine Learning Paradigms
    9m 53s
  2. Sampling Methods
    19m 43s
  3. Underfitting and Overfitting in Models
    6m 7s
  4. Variance and Bias
    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
    7m 21s
  5. KNN - Practical
    35m
  6. Decision Trees for Classification
    6m 17s
  7. Decision Trees Practical - 1
    22m 41s
  8. Decision Trees Practical - 2
    20m 42s
  9. Random Forest
    8m 37s
  10. Support Vector Machine Classifier
    5m 44s
  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
    15m 21s
  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
    4m 52s
  7. Decision Tree Regressor - Practical
    5m 2s
  8. Regressor Model Selection
    9m 7s
  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
    1m 33s
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