What are the major classification techniques in machine learning?

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.

Course Content

Introduction to Machine Learning

1m 5s

Environment Setup part 1

3m 26s

Environment Setup part 2

4m 43s

Environment Setup part 3

2m 33s

Quiz : Introduction to Machine Learning

Data Wrangling

12m 26s

Importing Libraries and Dataset

11m 19s

Handling Missing Data

11m 51s

Handling Missing Data - Practical

17m 3s

Encoding Categorical Data

6m 34s

Encoding Catergorical Data - Practical

8m 3s

Splitting Dataset

3m 38s

Splitting Dataset - Practical

9m 36s

Normalizing the Data - Part 1

6m 18s

Normalizing the Data - Part2

6m 58s

Finding Machine Learning Datasets

9m 17s

Exploratory Data Analysis

Plotting Graphs - Part 1

19m 12s

Plotting Graphs - Part 2

23m 44s

Distribution Models - Part 1

33m 54s

Distribution Models - Part 2

13m 56s

Assignment of Data Preprocessing for Machine Learning

1m 39s

Quiz : Data Preprocessing for Machine Learning

10m

Machine Learning Paradigms

9m 53s

Sampling Methods

19m 43s

Underfitting and Overfitting in Models

6m 7s

Variance and Bias

9m 49s

Assignment of Machine Learning Paradigms

3m 24s

Quiz : Machine Learning Paradigms

10m

Overview of Classification

5m 22s

Logistic Regression

4m 54s

Logistic Regression - Practical

32m 40s

KNN

7m 21s

KNN - Practical

35m

Decision Trees for Classification

6m 17s

Decision Trees Practical - 1

22m 41s

Decision Trees Practical - 2

20m 42s

Random Forest

8m 37s

Support Vector Machine Classifier

5m 44s

Support Vector Machine Classifier - Practical 1

16m 59s

Support Vector Machine Classifier - Practical 2

11m 18s

Naive Bayes Classifier

5m 25s

Naive Bayes Classifier - Practical 1

15m 21s

Naive Bayes Classifier - Practical 2

11m 7s

Evaluating Classification Models Performance

7m 31s

Evaluating Classification Models Performance - Practical

33m 2s

Assignment of Supervised Learning Algorithms

1m 12s

Quiz : Supervised Learning Algorithms

10m

Simple Linear Regression

8m 57s

Simple Linear Regression - Practical

12m 34s

Multi-Linear Regression

Support Vector Regressor

10m 47s

Support Vector Regressor - Practical

6m 47s

Decision Tree Regressor

4m 52s

Decision Tree Regressor - Practical

5m 2s

Regressor Model Selection

9m 7s

Evaluating Regression Model Performance

8m 45s

Evaluating Regression Model Performance - Practical

11m 34s

Assignment of Regression Algoritms

1m 12s

Quiz : Regression Algorithms

10m

Advance Trends in Machine Learning

10m 13s

Course Summary

3m 51s

Interview Questions Part 1

19m 22s

Interview Questions Part 2

12m 23s

Interview Questions Part 3

15m 20s

Career Guidelines

1m 33s

Overview of Classification

Supervised Learning in Machine Learning Supervised Learning Algorithms

Enroll Now For Free

Discussion

Course Content

Introduction to Machine Learning

1m 5s

Environment Setup part 1

3m 26s

Environment Setup part 2

4m 43s

Environment Setup part 3

2m 33s

Data Wrangling

12m 26s

Importing Libraries and Dataset

11m 19s

Handling Missing Data

11m 51s

Handling Missing Data - Practical

17m 3s

Encoding Categorical Data

6m 34s

Encoding Catergorical Data - Practical

8m 3s

Splitting Dataset

3m 38s

Splitting Dataset - Practical

9m 36s

Normalizing the Data - Part 1

6m 18s

Normalizing the Data - Part2

6m 58s

Finding Machine Learning Datasets

9m 17s

Exploratory Data Analysis

Plotting Graphs - Part 1

19m 12s

Plotting Graphs - Part 2

23m 44s

Distribution Models - Part 1

33m 54s

Distribution Models - Part 2

13m 56s

Assignment of Data Preprocessing for Machine Learning

1m 39s

Machine Learning Paradigms

9m 53s

Sampling Methods

19m 43s

Underfitting and Overfitting in Models

6m 7s

Variance and Bias

9m 49s

Assignment of Machine Learning Paradigms

3m 24s

Overview of Classification

5m 22s

Logistic Regression

4m 54s

Logistic Regression - Practical

32m 40s

KNN

7m 21s

KNN - Practical

35m

Decision Trees for Classification

6m 17s

Decision Trees Practical - 1

22m 41s

Decision Trees Practical - 2

20m 42s

Random Forest

8m 37s

Support Vector Machine Classifier

5m 44s

Support Vector Machine Classifier - Practical 1

16m 59s

Support Vector Machine Classifier - Practical 2

11m 18s

Naive Bayes Classifier

5m 25s

Naive Bayes Classifier - Practical 1

15m 21s

Naive Bayes Classifier - Practical 2

11m 7s

Evaluating Classification Models Performance

7m 31s

Evaluating Classification Models Performance - Practical

33m 2s

Assignment of Supervised Learning Algorithms

1m 12s

Simple Linear Regression

8m 57s

Simple Linear Regression - Practical

12m 34s

Multi-Linear Regression

Support Vector Regressor

10m 47s

Support Vector Regressor - Practical

6m 47s

Decision Tree Regressor

4m 52s

Decision Tree Regressor - Practical

5m 2s

Regressor Model Selection

9m 7s

Evaluating Regression Model Performance

8m 45s

Evaluating Regression Model Performance - Practical

11m 34s

Assignment of Regression Algoritms

1m 12s

Advance Trends in Machine Learning

10m 13s

Course Summary

3m 51s

Interview Questions Part 1

19m 22s

Interview Questions Part 2

12m 23s

Interview Questions Part 3

15m 20s

Career Guidelines

1m 33s

FAQs

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.