Data Science & Machine Learning with Python

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• Data Science & Machine Learning with Python 10:18:50

  • 1: 1. Short Course Overview Table of Contents 09:08
  • 2: 1_introduction to machine learning - concepts, defintions and types - part 2 05:54
  • 3: 1_introduction to machine learning - concepts, defintions and types 04:37
  • 4: 2_preparing_system - part 2 05:44
  • 5: 2_preparing_system_part1 08:20
  • 6: 3_quick_tutorial_assignment 09:41
  • 7: 4_quick_tutorial_flow_control 09:25
  • 8: 5_quick_tutorial_datastructures 12:12
  • 9: 6_quick_tutorial_functions 03:58
  • 10: 7_quick_tutorial_numpy_array 05:57
  • 11: 8_quick_tutorial_numpy_data 08:08
  • 12: 9_quick_tutorial_numpy_arithmetic 04:12
  • 13: 10_quick_tutorial_matplotlib 07:06
  • 14: 11_quick_tutorial_pandas_part1 05:36
  • 15: 11_quick_tutorial_pandas_part2 07:11
  • 16: 12_understand_csv_file 08:55
  • 17: 13_read_csv_python 08:59
  • 18: 14_read_csv_numpy 03:49
  • 19: 15_read_csv_pandas 05:20
  • 20: 16_data_summary_peek_shape_datatypes 09:27
  • 21: 17_data_summary_distribution_description 08:51
  • 22: 18_data_summary_correlation 10:51
  • 23: 19_data_summary_skew 06:35
  • 24: 20_data_summary_histogram 06:42
  • 25: 21_data_summary_density 05:36
  • 26: 22_data_summary_box 05:00
  • 27: 23_data_summary_correlation_plot 08:08
  • 28: 24_data_summary_scatter_plot 05:15
  • 29: 25_data_preprocess_intro 08:47
  • 30: 26_data_preprocess_rescale_part1 08:30
  • 31: 26_data_preprocess_rescale_part2 09:15
  • 32: 27_data_preprocess_standardize 07:16
  • 33: 27_data_preprocess_standardize_part2 03:49
  • 34: 28_data_prepreocess_normalization 08:15
  • 35: 29_data_preprocess_binarization 05:35
  • 36: 30_feature_selection_intro 07:12
  • 37: 31_feature_selection_univariate 08:35
  • 38: 31_feature_selection_univariate_part1_2 10:11
  • 39: 32_feature_selection_rfe 10:44
  • 40: 33_feature_selection_pca 08:55
  • 41: 34_feature_selection_important 06:30
  • 42: 35_algorithm_evaluation_intro 07:07
  • 43: 35_algorithm_evaluation_refresher_session 12:04
  • 44: 36_algorithm_evaluation_test_train_split 11:25
  • 45: 37_algorithm_evaluation_k_fold_cross_validation 08:34
  • 46: 38_algorithm_evaluation_leave_one_out 04:32
  • 47: 39_algorithm_evaluation_repeated_random 06:47
  • 48: 40_algorithm_matrics_intro 08:57
  • 49: 41_algorithm_matrics_classification_Accuracy 08:02
  • 50: 42_algorithm_matrics_log_loss 03:25
  • 51: 43_algorithm_matrics_auc 06:09
  • 52: 44_algorithm_metrics_confusion_matrix 10:20
  • 53: 45_algorithm_metrics_classification_report 04:10
  • 54: 46_algorithm_metrics_regression_mean_absolute_error_part1 06:09
  • 55: 46_algorithm_metrics_regression_mean_absolute_error_part2 06:40
  • 56: 47_algorithm_metrics_regressioin_mean_square_error 02:50
  • 57: 48_algorithm_metrics_regressioin_r2 03:51
  • 58: 49_algorithm_spotcheck_logistic_regression 11:31
  • 59: 50_algorithm_spotcheck_linear_discriminant_analysis 03:48
  • 60: 51_algorithm_spotcheck_knn 04:49
  • 61: 52_algorithm_spotcheck_naive_bayes 04:00
  • 62: 53_algorithm_spotcheck_cart 03:48
  • 63: 54_algorithm_spotcheck_svm 04:36
  • 64: 55_reg_algorithm_spotcheck_linear_regression 07:38
  • 65: 56_reg_algorithm_spotcheck_ridge 03:13
  • 66: 57_reg_algorithm_spotcheck_elasticnet 02:09
  • 67: 57_reg_algorithm_spotcheck_lasso 02:54
  • 68: 58_reg_algorithm_spotcheck_knn 05:56
  • 69: 59_reg_algorithm_spotcheck_cart 04:03
  • 70: 60_reg_algorithm_spotcheck_svm 04:03
  • 71: 61_compare_algorithms_part1 08:56
  • 72: 61_compare_algorithms_part2 05:01
  • 73: 62_pipeline_prepration_modelling 10:56
  • 74: 63_pipeline_selection_modelling 09:35
  • 75: 64_ensemble_voting 06:57
  • 76: 65_ensemble_bagging 08:21
  • 77: 66_ensemble_boosting 04:35
  • 78: 67_param_tuning_grid_search 07:36
  • 79: 68_param_tuning_random_search 05:59
  • 80: 69_load_save_pickle 09:41
  • 81: 70_load_save_joblib 05:52
  • 82: 71_finalization_intro 06:39
  • 83: 72_finalization_pima 06:45
  • 84: 72_unbalanced_data 08:35
  • 85: 73_finalization_boston 08:16
  • 86: 74_prediction_pima 06:39
  • 87: 75_prediction_boston 08:06
  • 88: multi_class_finalization 09:15
  • 89: multi_class_prediction 03:25

• Leave a Review 01:00

  • 90: Leave a Review 01:00

This Data Science & Machine Learning with Python course is designed for beginners and intermediate learners who want to dive into the world of data science. Through this course, you will gain a solid understanding of Python programming, data manipulation techniques, and the basics of machine learning. Learn how to use libraries like NumPy, Pandas, and Matplotlib, and apply machine learning algorithms to solve real-world problems.

With a hands-on approach, you will work with actual datasets, build predictive models, and understand the intricacies of data processing and evaluation. By the end of the course, you’ll be prepared to take on data science projects with confidence, setting you up for a career in one of the UK’s fastest-growing sectors.

Learning Objectives:

• Master the basics of Python programming and key data manipulation libraries (NumPy, Pandas, Matplotlib).
• Understand the core concepts of machine learning and classification algorithms.
• Learn how to prepare, clean, and preprocess data for machine learning models.
• Gain experience in model evaluation using various metrics and techniques.
• Develop hands-on expertise in building, training, and optimizing machine learning models.

Why Should You Take This Course?

Data Science and Machine Learning are among the most sought-after skills today. By taking this course, you’ll gain practical knowledge that is directly applicable to real-world challenges, making you highly employable in the tech and business sectors. This is a great opportunity for those looking to enter or advance in the field, with significant earning potential and job opportunities across industries such as finance, healthcare, and technology.

• Aspiring data scientists
• Python programmers wanting to specialise in data science
• Professionals looking to upskill in machine learning
• Anyone passionate about solving problems with data

• No experience required
• A computer with an internet connection
• Willingness to learn and apply new data science techniques

• Data Scientist: £40k to £70k
• Machine Learning Engineer: £45k to £80k
• Data Analyst: £30k to £50k
• Business Intelligence Developer: £35k to £60k

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