42- Multiple Linear Regression and Machine Learning in Python-(Day-20)

Machine Learning helps you build models that can make predictions and take decisions of their own. This video on Types of Machine Learning and Algorithms will make you understand what Machine Learning is and the various types of Machine Learning.
This videos explains the Multiple Linear Regression for Machine Learning in Python.
The other types are in the next videos in the same playlist:
-Supervised Machine Learning and its algorithms
Logistic Regression.
K-Nearest Neighbors(K-NN)
Support Vector Machine(SVM)
Kernel SVM.
Naive Bayes
Decision Tree Classification.
Random Forest Classification
-unsupervised Machine Learning and its algorithms
K-Means Clustering
Hierarchical Clustering.
Probabilistic Clustering
-semisupervised Machine Learning and its algorithms
-Reinforcement Machine Learning and its algorithms
Model-Free Reinforcement Learning.
Policy Optimization.
Q-Learning
Model-Based Reinforcement Learning.
Learn the Model
Given the Model.

only if you are interested in this 40 days long course (python_ka_chilla with baba_aammar.
More about me: I am Dr Aammar Tufail, your instructor in Python_ka_chilla. My aim is to train people in Data Science, machine learning, artificial intelligence, and deep learning by the end of the year (2022).

If you are keen to learn from this complete course, then here is the playlist for the course:

If you want to learn Data Science with R here is the completed and uploaded course in urdu, link:

If you have any questions, you can always write in the comment section of the video, you have a question about.
#DataScience
#artificailIntelligence
#deeplearning
#machinelearning
#python
#python_ka_chilla
#baba_aammar
#supervised_machine_learning
#unsupervised_machines learning
#semi_supervised_machine_learning
#reinforcement_machine_learning

[Codanics]

Like the video before your start playing... it will help us to remember you

[asadtariq]

8:20
It is expected to have very big and very small intercepts Coefficients. You may see a huge difference in your prediction with a slight change in input value i.e changing Experience from '1.1' to '1.2' may predict Salary from 36000 to -123243124134112.223. Which makes no sense.

The reason behind this is that Linear regression Overfits your data if it is very diverse. It tries to map every input observation to its actual output.
Thats why we use different type of regressions to avoid overfitting.

[abdulnasirafridi]

Outstanding MashaAllah, Baba Ge your one of my favourite trainers in my career I have learnt online or offline, Allah keep you safe and sound from devil eye

[usmanayaz]

Great explanations of both simple linear regression and multiple linear regression. Jazakallah.

[abdurrasheed]

Sir, kindly link description me deya kary.. as we’ve already been filtered out 🥵🥵 Trying very very hard to catch your pace.
Practiced almost all videos and pushed to the github. Thank you very much. Allah AP ko eska ajar dy aameen

[sanashah]

present
Simple linear regression> 2 variables and one dependent and one independent
Multi-Linear regression> More than 2 variables and one dependent nad more than one independent

Independent variables are more than one is called input data, features or independent variables.
Dependent variable are only one and is called prediction, output or dependent variables

[abbashris]

Independent variables are also called “regressors, “ “controlled variable, ” “manipulated variable, ” “explanatory variable, ” “exposure variable, ” and/or “input variable.”

[komalkhan]

2:09 "X" can also be called "covariate", "predictor", "regression input", "explanatory variable".
For "y" can also be called "target variable", "measured variable", and "response variable"

[abdullahmir]

Input variables = type in variables, load variables, featuring
Output variables= target, label, experimental variables

[MuhammadArslan-dzfi]

#2:06 ndependent variables are also called “regressors, “ “controlled variable, ” “manipulated variable, ” “explanatory variable, ” “exposure variable, ” and/or “input variable.” Similarly, dependent variables are also called “response variable, ” “regressand, ” “measured variable, ” “observed variable, ” “responding variable, ” “explained variable, ” “outcome variable, ” “experimental variable, ” and/or “output variable.”

[NasirJumani]

2:00 Independent variables are also called predictor variables or explanatory variables. Dependent variables are also called response variables, outcome variables, target variables or output variables.

[Kidsfunzone]

2:00 independent variable: explanatory variable, predictor variable, righthand side variable
dependent variable: response variable, outcome variable, left side variable

[momna]

2:04
Independent variables are also called “regressors, “ “controlled variable, ” “manipulated variable, ” “explanatory variable, ” “exposure variable”
Dependent variables are also called “response variable, ” “regressand, ” “measured variable, ” “observed variable”

[aajizattari]

1:50
*Input variables:* independent variables, explanatory variables, predictor variables, regressors exogenous variables

*Output variable:* dependent variable, regressand, endogenous variable, response variable, measured variable, criterion variable

[zainulabidin]

2:07
1;- Input variables: regressors, controlled variable, manipulated variable, explanatory variable, exposure variable.
2:- Output variables: response variable, regressand, measured variable, observed variable, responding variable.

[faheemk]

1:40 output : target/label /guesses
input: Instance / predictors

[muhammadrizwan]

@2:07
1- Input variables: independent variables, explanatory variables, predictor variables, regressors

2- Output variable: dependent variable, measured variable, criterion variable

[ehtishamahmad]

1:58
1- Input variables= predictors, factors, treatment variables, explanatory variables, x-variables, and right-hand variable.

2- Output variables= response variable, regressand, measured variable, observed variable, responding variable, explained variable, outcome variable, experimental variable.

[hassanorakzai]

@1:54
Independent variables: regressors, controlled variable, manipulated variable, xplanatory variable, exposure variable, and/or input variable.
Dependent variables: response variable, regressand, measured variable, observed variable, responding variable,

[haseeboffc]

1:50 Input variables: predictor variables
Output variables: response variable