-
Supervise Machine Learning
21 Lessons-
StartWhat is Supervised Machine Learning
-
StartLinear Regression
-
StartSquared Error Function and Gradient Descendent
-
StartIntroduction to Regressoion Performance Metrics and MAE
-
StartMean Squared Error
-
StartRoot Mean Squared Error
-
StartR2 Score
-
StartAdjusted R2 Score
-
StartProblems in Linear Regression
-
StartRidge Regression
-
StartLasso Regression
-
StartLogistic Regression part - 1 & 2
-
StartConfusion Matrix
-
StartNaive Bayes Algorithm
-
StartKNN Algorithm
-
StartDecision Tree Part - 1
-
StartDecision Tree Part - 2
-
StartDecision Tree Part - 3
-
StartDecision Tree Regressor
-
StartEnsemble Techinque Introduction to Bagging Techinique
-
StartBoosting Techinque with Adaboost
-
-
1- Uses of Algorithm (Supervised Machine Learning)
11 Lessons-
Start- Linear Regression Implementaion
-
StartPerformance Metrices & Logistic regression Implementation
-
StartLogistic Regression Implementation
-
StartClassification Report Implementation
-
StartKNN Regression Implementation
-
StartKNN Classifier Implementation
-
StartDecision Tree regression Implementation
-
StartDecision Tree classifier Implementation
-
StartRandom Forest Regression Implementation
-
StartRandom Forest Classifiers Implementation
-
StartNaive Bayes Algorithm Implementation
-
-
2. Stastiics used in Data Science
7 Lessons -
Realtime Project implementation-Youtube Data Analysis
17 Lessons-
StartDownloads
-
StartConnecting Colab with G-Drive
-
StartCleaning the Dataset
-
StartPreprocessing part 1
-
StartPreprocessing part 2
-
StartPreprocessing part 3
-
StartPreprocessing part 4
-
StartAnalysis Part -1
-
StartAnalysis Part -2
-
StartDynamic Analytics Part -1
-
StartDynamic Analytics Part -2
-
StartDynamic Analytics Part -3
-
StartDynamic Analytics Part -4
-
StartMachine Learning - 1
-
StartMachine Learning - 2
-
StartMachine Learning - 3
-
StartMachine Learning - 4
-
-
Deep Learning with Python
9 Lessons
Sign up for our newsletter!
Want to stay up-to-date on industry trends?