@@ -201,7 +201,7 @@ <h2>Machine learning</h2>
201201 < div class ="tooltip-container ">
202202 < a href ="unsupervised_learning.html ">
203203 < div class ="portfolio-wrap ">
204- < img src ="assets/img/data-engineering/ML-rg-0 .png " class ="img-fluid " alt ="" style ="width: 65%; height: auto; ">
204+ < img src ="assets/img/machine-ln/unsupervised_logo .png " class ="img-fluid " alt ="" style ="width: 65%; height: auto; ">
205205 < p class ="portfolio-title "> 3. Unsupervised Algorithms</ p >
206206 </ div >
207207 < div class ="tooltip-text ">
@@ -226,7 +226,7 @@ <h2>Machine learning</h2>
226226 < a href ="Linear-reg.html ">
227227 < div class ="portfolio-wrap ">
228228 < img src ="assets/img/machine-ln/gradient-discent.png " class ="img-fluid " alt ="" style ="width: 85%; height: auto; ">
229- < p class ="portfolio-title "> 3 . Gradient Descent Method</ p >
229+ < p class ="portfolio-title "> 4 . Gradient Descent Method</ p >
230230 </ div >
231231 < div class ="tooltip-text ">
232232 An optimization technique used to minimize the loss function by iteratively adjusting model parameters in the direction of the steepest descent.
@@ -250,7 +250,7 @@ <h2>Machine learning</h2>
250250 < a href ="mle.html ">
251251 < div class ="portfolio-wrap ">
252252 < img src ="assets/img/machine-ln/mle-logo.png " class ="img-fluid " alt ="" style ="width: 85%; height: auto; ">
253- < p class ="portfolio-title "> 4 . MLE & MAP</ p >
253+ < p class ="portfolio-title "> 5 . MLE & MAP</ p >
254254 </ div >
255255 < div class ="tooltip-text ">
256256 MLE (Maximum Likelihood Estimation) estimates model parameters by maximizing the likelihood function, while MAP (Maximum A Posteriori) incorporates prior distributions into parameter estimation.
@@ -274,7 +274,7 @@ <h2>Machine learning</h2>
274274 < a href ="Linear-Parameter-estimation.html ">
275275 < div class ="portfolio-wrap ">
276276 < img src ="assets/img/data-engineering/Linear-reg1.png " class ="img-fluid " alt ="" style ="width: 85%; height: auto; ">
277- < p class ="portfolio-title "> 5 . Linear Regression</ p >
277+ < p class ="portfolio-title "> 6 . Linear Regression</ p >
278278 </ div >
279279 < div class ="tooltip-text ">
280280 A statistical method for modeling the relationship between a dependent variable and one or more independent variables using a linear equation.
@@ -298,7 +298,7 @@ <h2>Machine learning</h2>
298298 < a href ="Polinomial-regression.html ">
299299 < div class ="portfolio-wrap ">
300300 < img src ="assets/img/machine-ln/polinomial-reg.png " class ="img-fluid " alt ="" style ="width: 75%; height: auto; ">
301- < p class ="portfolio-title "> 6 . Polynomial Regression</ p >
301+ < p class ="portfolio-title "> 7 . Polynomial Regression</ p >
302302 </ div >
303303 < div class ="tooltip-text ">
304304 An extension of linear regression that models the relationship between the dependent variable and the independent variable(s) as an nth-degree polynomial.
@@ -322,7 +322,7 @@ <h2>Machine learning</h2>
322322 < a href ="Ridge-lasso-elasticnet.html ">
323323 < div class ="portfolio-wrap ">
324324 < img src ="assets/img/machine-ln/lasso-jtheta.png " class ="img-fluid " alt ="" style ="max-width: 75%; max-height: 20%; ">
325- < p class ="portfolio-title "> 7 . Ridge-lasso-Elasticnet</ p >
325+ < p class ="portfolio-title "> 8 . Ridge-lasso-Elasticnet</ p >
326326 </ div >
327327 < div class ="tooltip-text ">
328328 Techniques combining regularization methods to prevent overfitting and improve model performance.
@@ -345,7 +345,7 @@ <h2>Machine learning</h2>
345345 < a href ="pca-analysis.html ">
346346 < div class ="portfolio-wrap ">
347347 < img src ="assets/img/machine-ln/pca-logo.png " class ="img-fluid " alt ="" style ="width: 80%; height: 80%; ">
348- < p class ="portfolio-title "> 8 . PC Analysis (PCA)</ p >
348+ < p class ="portfolio-title "> 9 . PC Analysis (PCA)</ p >
349349 </ div >
350350 < div class ="tooltip-text ">
351351 Principal Component Analysis is a technique for dimensionality reduction by transforming data into orthogonal components.
@@ -369,7 +369,7 @@ <h2>Machine learning</h2>
369369 < a href ="classification.html ">
370370 < div class ="portfolio-wrap ">
371371 < img src ="assets/img/data-engineering/classification.png " class ="img-fluid " alt ="" style ="width: 75%; height: auto; ">
372- < p class ="portfolio-title "> 9 . Classification Regression</ p >
372+ < p class ="portfolio-title "> 10 . Classification Regression</ p >
373373 </ div >
374374 < div class ="tooltip-text ">
375375 Methods for classifying data into categories and predicting continuous values using regression techniques.
@@ -393,7 +393,7 @@ <h2>Machine learning</h2>
393393 < a href ="logistic-regression.html ">
394394 < div class ="portfolio-wrap ">
395395 < img src ="assets/img/machine-ln/deep-smf.png " class ="img-fluid " alt ="" style ="max-width: 95%; max-height: 80%; ">
396- < p class ="portfolio-title "> 10 . Logistic Regression</ p >
396+ < p class ="portfolio-title "> 11 . Logistic Regression</ p >
397397 </ div >
398398 < div class ="tooltip-text ">
399399 A classification algorithm used for binary outcomes, predicting probabilities based on the logistic function.
@@ -418,7 +418,7 @@ <h2>Machine learning</h2>
418418 < a href ="naive-byes.html ">
419419 < div class ="portfolio-wrap ">
420420 < img src ="assets/img/machine-ln/classification-naive-modified1.png " class ="img-fluid " alt ="" style ="max-width: 55%; max-height: 55%; ">
421- < p class ="portfolio-title "> 11 . Naive Bayes ML</ p >
421+ < p class ="portfolio-title "> 12 . Naive Bayes ML</ p >
422422 </ div >
423423 < div class ="tooltip-text ">
424424 A probabilistic classifier based on Bayes' theorem with an assumption of feature independence.
@@ -442,7 +442,7 @@ <h2>Machine learning</h2>
442442 < a href ="knn.html ">
443443 < div class ="portfolio-wrap ">
444444 < img src ="assets/img/machine-ln/classification-knn1.png " class ="img-fluid " alt ="" style ="width: 55%; height: auto; ">
445- < p class ="portfolio-title "> 12 . KNN ML</ p >
445+ < p class ="portfolio-title "> 13 . KNN ML</ p >
446446 </ div >
447447 < div class ="tooltip-text ">
448448 K-Nearest Neighbors is a simple, non-parametric algorithm used for classification and regression based on proximity.
@@ -466,7 +466,7 @@ <h2>Machine learning</h2>
466466 < a href ="decision-tree.html ">
467467 < div class ="portfolio-wrap ">
468468 < img src ="assets/img/machine-ln/classification-decision-tree.png " class ="img-fluid " alt ="" style ="max-width: 80%; max-height: 70%; ">
469- < p class ="portfolio-title "> 13 . Decision Tree</ p >
469+ < p class ="portfolio-title "> 14 . Decision Tree</ p >
470470 </ div >
471471 < div class ="tooltip-text ">
472472 A model that uses a tree-like graph of decisions and their possible consequences for classification and regression.
@@ -491,7 +491,7 @@ <h2>Machine learning</h2>
491491 < a href ="support-vector.html ">
492492 < div class ="portfolio-wrap ">
493493 < img src ="assets/img/machine-ln/classification-svm.png " class ="img-fluid " alt ="" style ="width: 80%; height: auto; ">
494- < p class ="portfolio-title "> 14 . Support Vector</ p >
494+ < p class ="portfolio-title "> 15 . Support Vector</ p >
495495 </ div >
496496 < div class ="tooltip-text ">
497497 Support Vector Machines are used for classification and regression by finding the optimal hyperplane that maximizes the margin between classes.
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