SCRiPTMAFiA.ORG > Edureka Applied Machine Learning With Python 2025

Files Included :
02-course introduction.mp4 (24.37 MB)
03-machine learning in industry.mp4 (25.94 MB)
04-how companies use machine learning.mp4 (31.63 MB)
01-machine learning process.mp4 (22.34 MB)
02-steps in machine learning.mp4 (23.02 MB)
03-types of machine learning.mp4 (37.02 MB)
01-introduction to linear regression.mp4 (32.88 MB)
02-real life examples.mp4 (44.56 MB)
03-calculating ols.mp4 (75.45 MB)
04-equation of ols.mp4 (30.86 MB)
05-assumptions in linear regression.mp4 (41.51 MB)
06-demonstration setting up the model.mp4 (40.62 MB)
07-calculating r square and rmse.mp4 (51.03 MB)
08-residual plot and q q plot.mp4 (17.28 MB)
09-cooks distance.mp4 (40.38 MB)
10-real life examples of logistic regression.mp4 (41.2 MB)
11-what is logistic regression.mp4 (49.63 MB)
12-cost function.mp4 (27.61 MB)
13-assumptions in logistic regression.mp4 (35.82 MB)
14-demonstration of logistic regression transforming data.mp4 (57.32 MB)
15-demonstration of logistic regression developing the model.mp4 (32.73 MB)
01-confusion matrix.mp4 (19.33 MB)
02-example for calculating confusion matrix.mp4 (58.54 MB)
03-conditions for over fitting and under fitting.mp4 (22.1 MB)
04-overfitting and underfitting.mp4 (57.48 MB)
05-performance metrics mse rmse mae mape.mp4 (47.02 MB)
06-r square rmsle and adjusted r square.mp4 (33.11 MB)
07-working of r square.mp4 (42.07 MB)
08-significance of r square.mp4 (52.8 MB)
01-summary for inception of machine learning.mp4 (16 MB)
01-classification in machine learning.mp4 (31.06 MB)
02-what is decision tree.mp4 (63.62 MB)
03-decision tree entropy and information gain.mp4 (53.33 MB)
04-step by step building of decision tree.mp4 (77.84 MB)
05-pruning in decision tree.mp4 (53.45 MB)
06-demonstration importing data.mp4 (46.62 MB)
07-demonstration building decision tree and random forest.mp4 (63.65 MB)
08-demonstration importance of features.mp4 (25.33 MB)
09-demonstration production ready random forest.mp4 (18.78 MB)
10-demonstration hyperparameter tuning.mp4 (31.25 MB)
01-what is svm.mp4 (37.05 MB)
02-terminologies in svm.mp4 (86.16 MB)
03-hinge loss function and other parameters.mp4 (82.78 MB)
04-demonstration of svm exploring the data.mp4 (29.44 MB)
05-demonstration of svm setting up the svm classifier.mp4 (67.99 MB)
06-what is naive bayes.mp4 (18.8 MB)
07-working of naive bayes bayes theorem.mp4 (46.3 MB)
08-example of naive bayes algorithm.mp4 (74.88 MB)
09-demonstration of naive bayes code.mp4 (36.34 MB)
10-working of knn.mp4 (32.92 MB)
11-example of knn algorithm.mp4 (45.24 MB)
12-demonstration of knn setting up the model.mp4 (50.31 MB)
13-demonstration of knn transforming and scaling data.mp4 (46.89 MB)
14-demonstration of knn creating classifier.mp4 (28.13 MB)
01-dimensionality reduction.mp4 (57.45 MB)
02-introduction to pca.mp4 (53.44 MB)
03-applying pca.mp4 (45.33 MB)
04-eigen values and eigen vectors.mp4 (59.38 MB)
05-demonstration initializing pca.mp4 (25.12 MB)
06-demonstration determining optimal number of components through pca.mp4 (36.68 MB)
07-demonstration implementing optimal pca.mp4 (45.11 MB)
08-working of lda.mp4 (55.51 MB)
09-demonstration of lda.mp4 (58.53 MB)
01-summary for machine learning algorithms.mp4 (16.4 MB)
01-what are association rules.mp4 (45.32 MB)
02-apriori algorithm.mp4 (34.39 MB)
03-demonstrating apriori algorithm.mp4 (87.65 MB)
01-what are recommendation engine.mp4 (34.48 MB)
02-cbf.mp4 (33.58 MB)
03-demonstration of recommendation engine preparing data.mp4 (56.71 MB)
04-demonstration testing the model.mp4 (49.46 MB)
01-elements for reinforcement learning.mp4 (26.98 MB)
02-demonstration of boosting explaining the dataset.mp4 (55.03 MB)
03-demonstration of boosting cleaning and transforming dataset.mp4 (56.3 MB)
04-demonstration of boosting factors affecting promotion.mp4 (34.52 MB)
05-demonstration of boosting total score and service affecting promotion.mp4 (48.38 MB)
07-demonstration of boosting department influencing promotion.mp4 (49.06 MB)
09-demonstration of boosting modeling the data.mp4 (42.37 MB)
10-demonstration of boosting building a model.mp4 (73.19 MB)
11-working of k means algorithm.mp4 (32.98 MB)
12-demonstration of k means clustering.mp4 (61.1 MB)
01-summary for association rule mining and recommendation system.mp4 (22.79 MB)
01-course summary for applied machine learning with python.mp4 (19.28 MB)]
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