@@ -211,6 +211,20 @@ <h2>Machine learning</h2>
211211 </ div >
212212 </ div >
213213
214+ < div class ="col-lg-4 col-md-6 portfolio-item filter-python ">
215+ < div class ="tooltip-container ">
216+ < a href ="reinforcement_learning.html ">
217+ < div class ="portfolio-wrap ">
218+ < img src ="assets/img/machine-ln/reinforcment_logo.png " class ="img-fluid " alt ="" style ="width: 65%; height: auto; ">
219+ < p class ="portfolio-title "> 4. Reinforcement Learning</ p >
220+ </ div >
221+ < div class ="tooltip-text ">
222+ Agent learns to make decisions by interacting with an environment.
223+ </ div >
224+ </ a >
225+ </ div >
226+ </ div >
227+
214228 <!-- Portfolio items for 'ML' category -->
215229 <!-- <div class="col-lg-4 col-md-6 portfolio-item filter-ML">
216230 <a href="Linear-reg.html" title="An optimization technique used to minimize the loss function by iteratively adjusting model parameters in the direction of the steepest descent.">
@@ -226,7 +240,7 @@ <h2>Machine learning</h2>
226240 < a href ="Linear-reg.html ">
227241 < div class ="portfolio-wrap ">
228242 < img src ="assets/img/machine-ln/gradient-discent.png " class ="img-fluid " alt ="" style ="width: 85%; height: auto; ">
229- < p class ="portfolio-title "> 4 . Gradient Descent Method</ p >
243+ < p class ="portfolio-title "> 5 . Gradient Descent Method</ p >
230244 </ div >
231245 < div class ="tooltip-text ">
232246 An optimization technique used to minimize the loss function by iteratively adjusting model parameters in the direction of the steepest descent.
@@ -250,7 +264,7 @@ <h2>Machine learning</h2>
250264 < a href ="mle.html ">
251265 < div class ="portfolio-wrap ">
252266 < img src ="assets/img/machine-ln/mle-logo.png " class ="img-fluid " alt ="" style ="width: 85%; height: auto; ">
253- < p class ="portfolio-title "> 5 . MLE & MAP</ p >
267+ < p class ="portfolio-title "> 6 . MLE & MAP</ p >
254268 </ div >
255269 < div class ="tooltip-text ">
256270 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 +288,7 @@ <h2>Machine learning</h2>
274288 < a href ="Linear-Parameter-estimation.html ">
275289 < div class ="portfolio-wrap ">
276290 < img src ="assets/img/data-engineering/Linear-reg1.png " class ="img-fluid " alt ="" style ="width: 85%; height: auto; ">
277- < p class ="portfolio-title "> 6 . Linear Regression</ p >
291+ < p class ="portfolio-title "> 7 . Linear Regression</ p >
278292 </ div >
279293 < div class ="tooltip-text ">
280294 A statistical method for modeling the relationship between a dependent variable and one or more independent variables using a linear equation.
@@ -298,7 +312,7 @@ <h2>Machine learning</h2>
298312 < a href ="Polinomial-regression.html ">
299313 < div class ="portfolio-wrap ">
300314 < img src ="assets/img/machine-ln/polinomial-reg.png " class ="img-fluid " alt ="" style ="width: 75%; height: auto; ">
301- < p class ="portfolio-title "> 7 . Polynomial Regression</ p >
315+ < p class ="portfolio-title "> 8 . Polynomial Regression</ p >
302316 </ div >
303317 < div class ="tooltip-text ">
304318 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 +336,7 @@ <h2>Machine learning</h2>
322336 < a href ="Ridge-lasso-elasticnet.html ">
323337 < div class ="portfolio-wrap ">
324338 < 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 "> 8 . Ridge-lasso-Elasticnet</ p >
339+ < p class ="portfolio-title "> 9 . Ridge-lasso-Elasticnet</ p >
326340 </ div >
327341 < div class ="tooltip-text ">
328342 Techniques combining regularization methods to prevent overfitting and improve model performance.
@@ -345,7 +359,7 @@ <h2>Machine learning</h2>
345359 < a href ="pca-analysis.html ">
346360 < div class ="portfolio-wrap ">
347361 < img src ="assets/img/machine-ln/pca-logo.png " class ="img-fluid " alt ="" style ="width: 80%; height: 80%; ">
348- < p class ="portfolio-title "> 9 . PC Analysis (PCA)</ p >
362+ < p class ="portfolio-title "> 10 . PC Analysis (PCA)</ p >
349363 </ div >
350364 < div class ="tooltip-text ">
351365 Principal Component Analysis is a technique for dimensionality reduction by transforming data into orthogonal components.
@@ -369,7 +383,7 @@ <h2>Machine learning</h2>
369383 < a href ="classification.html ">
370384 < div class ="portfolio-wrap ">
371385 < img src ="assets/img/data-engineering/classification.png " class ="img-fluid " alt ="" style ="width: 75%; height: auto; ">
372- < p class ="portfolio-title "> 10 . Classification Regression</ p >
386+ < p class ="portfolio-title "> 11 . Classification Regression</ p >
373387 </ div >
374388 < div class ="tooltip-text ">
375389 Methods for classifying data into categories and predicting continuous values using regression techniques.
@@ -393,7 +407,7 @@ <h2>Machine learning</h2>
393407 < a href ="logistic-regression.html ">
394408 < div class ="portfolio-wrap ">
395409 < 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 "> 11 . Logistic Regression</ p >
410+ < p class ="portfolio-title "> 12 . Logistic Regression</ p >
397411 </ div >
398412 < div class ="tooltip-text ">
399413 A classification algorithm used for binary outcomes, predicting probabilities based on the logistic function.
@@ -418,7 +432,7 @@ <h2>Machine learning</h2>
418432 < a href ="naive-byes.html ">
419433 < div class ="portfolio-wrap ">
420434 < 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 "> 12 . Naive Bayes ML</ p >
435+ < p class ="portfolio-title "> 13 . Naive Bayes ML</ p >
422436 </ div >
423437 < div class ="tooltip-text ">
424438 A probabilistic classifier based on Bayes' theorem with an assumption of feature independence.
@@ -442,7 +456,7 @@ <h2>Machine learning</h2>
442456 < a href ="knn.html ">
443457 < div class ="portfolio-wrap ">
444458 < img src ="assets/img/machine-ln/classification-knn1.png " class ="img-fluid " alt ="" style ="width: 55%; height: auto; ">
445- < p class ="portfolio-title "> 13 . KNN ML</ p >
459+ < p class ="portfolio-title "> 14 . KNN ML</ p >
446460 </ div >
447461 < div class ="tooltip-text ">
448462 K-Nearest Neighbors is a simple, non-parametric algorithm used for classification and regression based on proximity.
@@ -466,7 +480,7 @@ <h2>Machine learning</h2>
466480 < a href ="decision-tree.html ">
467481 < div class ="portfolio-wrap ">
468482 < 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 "> 14 . Decision Tree</ p >
483+ < p class ="portfolio-title "> 15 . Decision Tree</ p >
470484 </ div >
471485 < div class ="tooltip-text ">
472486 A model that uses a tree-like graph of decisions and their possible consequences for classification and regression.
@@ -491,7 +505,7 @@ <h2>Machine learning</h2>
491505 < a href ="support-vector.html ">
492506 < div class ="portfolio-wrap ">
493507 < img src ="assets/img/machine-ln/classification-svm.png " class ="img-fluid " alt ="" style ="width: 80%; height: auto; ">
494- < p class ="portfolio-title "> 15 . Support Vector</ p >
508+ < p class ="portfolio-title "> 16 . Support Vector</ p >
495509 </ div >
496510 < div class ="tooltip-text ">
497511 Support Vector Machines are used for classification and regression by finding the optimal hyperplane that maximizes the margin between classes.
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