| 01-Introduction_To_Machine_Learning | |||
| 01-Machine_Learning_Essentials | |||
| 01-welcome_to_applied_machine_learning_with_python_instructions.html | 7.03 KB | ||
| 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 | ||
| 05-how_companies_are_crafting_the_future_instructions.html | 4.6 KB | ||
| 02-Overview_Of_Machine_Learning | |||
| 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 | ||
| 04-machine_learning_101_instructions.html | 280.02 KB | ||
| 03-Regression | |||
| 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 | ||
| 16-regression_and_its_assumptions_instructions.html | 4.1 KB | ||
| 17-role_of_regularization_instructions.html | 5.71 KB | ||
| 04-Evaluation_Metrics | |||
| 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 | ||
| 09-evaluation_of_all_things_predictive_instructions.html | 17.32 KB | ||
| 05-Module_Wrap_Up_And_Assessment | |||
| 01-summary_for_inception_of_machine_learning.mp4 | 16 MB | ||
| 02-Machine_Learning_Algorithms | |||
| 01-Decision_Tree_And_Random_Forest | |||
| 00.Support - Onehack.Us.txt | 94 B | ||
| 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 | ||
| 11-decision_trees_and_random_forests_instructions.html | 5.82 KB | ||
| 02-Svm_Knn_And_Naive_Bayes_Algorithms | |||
| 01-what_is_svm.mp4 | 37.05 MB | ||
| 02-terminologies_in_svm.mp4 | 86.17 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 | 46.9 MB | ||
| 14-demonstration_of_knn_creating_classifier.mp4 | 28.13 MB | ||
| 15-svm_knn_and_naive_bayes_when_to_use_which_algorithm_instructions.html | 4.97 KB | ||
| 03-Dimensionality_Reduction | |||
| 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 | ||
| 10-best_practices_for_dimensionality_reduction_pca_vs_lda_instructions.html | 5.52 KB | ||
| 04-Module_Wrap_Up_And_Assessment | |||
| 01-summary_for_machine_learning_algorithms.mp4 | 16.4 MB | ||
| 03-Association_Rule_Mining_And_Recommendation_System | |||
| 01-Association_Rules | |||
| 01-what_are_association_rules.mp4 | 45.32 MB | ||
| 02-apriori_algorithm.mp4 | 34.39 MB | ||
| 03-demonstrating_apriori_algorithm.mp4 | 87.65 MB | ||
| 04-fp_growth_in_association_rule_instructions.html | 5.62 KB | ||
| 02-Recommendation_Engines | |||
| 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 | ||
| 05-how_recommendation_engines_personalize_your_world_instructions.html | 4.8 KB | ||
| 03-Reinforcement_Learning_And_Boosting | |||
| 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 | ||
| 06-demonstration_of_boosting_age_previous_year_rating_influencing_promotion.mp4 | 31.02 MB | ||
| 07-demonstration_of_boosting_department_influencing_promotion.mp4 | 49.06 MB | ||
| 08-demonstration_of_boosting_education_affecting_promotion_and_summarization.mp4 | 49.24 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 | ||
| 13-training_models_to_get_better_with_experience_instructions.html | 6.99 KB | ||
| 04-Module_Wrap_Up_And_Assessment | |||
| 01-summary_for_association_rule_mining_and_recommendation_system.mp4 | 22.79 MB | ||
| 04-Course_Wrap_Up_And_Assessment | |||
| 01-course_summary_applied__with_python.mp4 | 19.28 MB | ||
| 02-final_project_cab_booking_demand_analysis_instructions.html | 4.82 KB | ||
| Resources.zip | 1.68 MB | ||
| Support - Onehack.Us.txt | 94 B |
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Edureka - Applied Machine Learning With Python 2025
Course details
This course provides an in-depth, hands-on introduction to machine learning using Python. You'll explore core concepts and methods, diving into supervised, unsupervised, and semi-supervised learning. Through practical exercises and examples, you'll master key algorithms including decision trees and random forests for classification, regression for predictive modeling, and K-means clustering for uncovering hidden patterns in unlabeled data. Additionally, you’ll gain insights into using model-boosting techniques to enhance model accuracy and apply strategies for leveraging unlabeled data effectively. By the end of this course, you’ll be able to: - Explain and implement decision trees and random forests as classification algorithms. - Define and differentiate various types of machine learning algorithms. - Analyze the working of regression for predictive tasks. - Apply K-means clustering to explore and discover patterns in unlabeled data. - Strategically use unlabeled data to improve model training. - Manipulate boosting algorithms to achieve higher model accuracy. This course is ideal for learners with foundational knowledge in Python programming and some familiarity with basic statistical concepts. Prior experience in data analysis or working with data libraries (such as Pandas or NumPy) is beneficial. This course is designed for aspiring data scientists, machine learning enthusiasts, and Python programmers who want to deepen their understanding of machine learning and enhance their data-driven decision-making skills. Equip yourself with practical machine learning skills and advance your journey in AI. Enroll in "Applied Machine Learning with Python" today and bring predictive power to your projects.
What you'll learn
- Explore machine learning algorithms, including supervised, unsupervised, and semi-supervised methods.
- Apply decision trees, random forests, and K-means clustering for classification and clustering.
- Develop machine learning models to gain insights and make predictions from real-world data.
- Enhance model accuracy by applying model-boosting techniques and evaluating their effectiveness.
There are 4 modules in this course
This course provides an in-depth, hands-on introduction to machine learning using Python. You'll explore core concepts and methods, diving into supervised, unsupervised, and semi-supervised learning. Through practical exercises and examples, you'll master key algorithms including decision trees and random forests for classification, regression for predictive modeling, and K-means clustering for uncovering hidden patterns in unlabeled data. Additionally, you’ll gain insights into using model-boosting techniques to enhance model accuracy and apply strategies for leveraging unlabeled data effectively.
By the end of this course, you’ll be able to:
- Explain and implement decision trees and random forests as classification algorithms.
- Define and differentiate various types of machine learning algorithms.
- Analyze the working of regression for predictive tasks.
- Apply K-means clustering to explore and discover patterns in unlabeled data.
- Strategically use unlabeled data to improve model training.
- Manipulate boosting algorithms to achieve higher model accuracy.
This course is ideal for learners with foundational knowledge in Python programming and some familiarity with basic statistical concepts. Prior experience in data analysis or working with data libraries (such as Pandas or NumPy) is beneficial.
This course is designed for aspiring data scientists, machine learning enthusiasts, and Python programmers who want to deepen their understanding of machine learning and enhance their data-driven decision-making skills.
Equip yourself with practical machine learning skills and advance your journey in AI. Enroll in "Applied Machine Learning with Python" today and bring predictive power to your projects.
General Details:
Duration: 6h 46m 42s
Updated: 03/2025
Language: English
Source: https://www.coursera.org/learn/applied-machine-learning-with-python
Instructor: https://www.edureka.co/
MP4 | Video: AVC, 1920x1080p | Audio: AAC, 44.100 KHz, 2 Ch
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