Data Science Projects with Python

Focused View

Barbora stetinova

6:08:34

19 View

01.01-course overview.mp4

02:53

01.02-installation and setup.mp4

04:40

01.03-lesson overview.mp4

02:21

01.04-python and the anaconda package management system.mp4

11:24

01.05-different types of data science problems.mp4

03:35

01.06-loading the case study data with jupyter and pandas.mp4

07:33

01.07-getting familiar with data and performing data cleaning.mp4

09:57

01.08-boolean masks.mp4

13:10

01.09-data quality assurance and exploration.mp4

10:08

01.10-deep dive categorical features.mp4

08:13

01.11-exploring the financial history features in the dataset.mp4

07:45

01.12-lesson summary.mp4

03:45

02.01-lesson overview.mp4

02:15

02.02-exploring the response variable and concluding the initial exploration.mp4

03:33

02.03-introduction to scikit-learn.mp4

10:50

02.04-model performance metrics for binary classification.mp4

08:06

02.05-true positive rate false positive rate and confusion matrix.mp4

08:03

02.06-obtaining predicted probabilities from a trained logistic regression model.mp4

10:28

02.07-lesson summary.mp4

00:21

03.01-lesson overview.mp4

01:48

03.02-examining the relationships between features and the response.mp4

14:42

03.03-finer points of the f-test equivalence to t-test for two classes and cautions.mp4

10:10

03.04-univariate feature selection what it does and doesnt do.mp4

14:16

03.05-generalized linear models (glms).mp4

11:45

03.06-lesson summary.mp4

00:22

04.01-lesson overview.mp4

02:12

04.02-estimating the coefficients and intercepts of logistic regression.mp4

11:44

04.03-assumptions of logistic regression.mp4

08:01

04.04-how many features should you include.mp4

12:05

04.05-lasso (l1) and ridge (l2) regularization.mp4

13:03

04.06-cross validation choosing the regularization parameter and other hyperparameters.mp4

05:07

04.07-reducing overfitting on the synthetic data classification problem.mp4

11:56

04.08-options for logistic regression in scikit-learn.mp4

05:41

04.09-lesson summary.mp4

00:34

05.01-lesson overview.mp4

01:47

05.02-decision trees.mp4

16:05

05.03-training decision trees node impurity.mp4

10:51

05.04-using decision trees advantages and predicted probabilities.mp4

11:12

05.05-random forests ensembles of decision trees.mp4

08:48

05.06-fitting a random forest.mp4

06:57

05.07-lesson summary.mp4

00:24

06.01-lesson overview.mp4

02:20

06.02-review of modeling results.mp4

02:53

06.03-dealing with missing data imputation strategies.mp4

05:57

06.04-cleaning the dataset.mp4

08:02

06.05-mode and random imputation of pay 1.mp4

08:00

06.06-a predictive model for pay 1.mp4

06:56

06.07-using the imputation model and comparing it to other methods.mp4

07:47

06.08-financial analysis.mp4

12:21

06.09-final thoughts on delivering the predictive model to the client.mp4

04:58

06.10-lesson summary.mp4

00:50

9781838986063 Code.zip

Description

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. The codes for this course can be downloaded from https://github.com/TrainingByPackt/Data-Science-Projects-with-Python-eLearning.

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Python

Data Science

Barbora stetinova

Instructor's Courses

Barbora Stetinova works in an Automotive industry earned experience in data science and machine learning, leading small team, leading strategical projects and in controlling topics for 13 years. Since Sept 2018 she is a member of IT department participating on the Data science implementation in an automotive company.

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.