Machine Learning in Python
Advanced Statistical Methods builds upon the statistical knowledge you will already have gained by focusing on predictive modelling and entering multidimensional spaces which require an understanding of mathematical methods, transformations, and distributions. The course introduces these concepts as well as complex means of analysis such as clustering, factoring, Bayesian inference, and decision theory while also allowing you to exercise your Python programming skills.
Your Instructor
We create online on-demand video courses in data science. The 365 Data Science Program is comprised of different modules starting from the fundamentals (Mathematics, Probability, and Statistics), going through programming languages (Python, R, SQL) and finishing off with state-of-the-art machine and deep learning.
We have a team of experts that create the curriculum and the course content, who work closely with our talented designers to bring the concepts to life in the most engaging and understandable way using specialized animation software. We strive to make the learning experience not only all-inclusive, detailed, and thorough, but also interactive, practical, and fun.
Course Curriculum
Linear regression
Available in
days
days
after you enroll
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StartWelcome to Advanced Statistics! (0:28)
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StartWelcome to the Course
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StartIntroduction to Regression Analysis (1:27)
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StartThe Linear Regression Model (5:50)
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StartCorrelation vs Regression (1:43)
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StartGeometrical Representation of the Linear Regression Model (1:25)
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StartPython Packages Installation (4:39)
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StartFirst Regression in Python (7:11)
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StartFirst Regression in Python Exercise
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StartUsing Seaborn for Graphs (1:21)
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StartHow to Interpret the Regression Table (5:47)
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StartDecomposition of Variability (3:37)
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StartWhat is the OLS? (3:13)
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StartR-Squared (5:30)
Multiple Linear Regression
Available in
days
days
after you enroll
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StartMultiple Linear Regression (2:55)
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StartAdjusted R-Squared (6:00)
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StartMultiple Linear Regression Exercise
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StartTest for Significance of the Model (F-Test) (2:01)
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StartOLS Assumptions (2:21)
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StartA1: Linearity (1:50)
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StartA2: No Endogeneity (4:09)
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StartA3: Normality and Homoscedasticity (5:47)
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StartA4: No Autocorrelation (3:31)
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StartA5: No Multicollinearity (3:26)
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StartDealing with Categorical Data - Dummy Variables (6:43)
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StartDealing with Categorical Data - Dummy Variables Exercise
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StartMaking Predictions with the Linear Regression (3:29)
Linear Regression with sklearn
Available in
days
days
after you enroll
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StartWhat is sklearn? (2:14)
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StartGame Plan for sklearn (1:55)
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StartSimple Linear Regression with sklearn (5:38)
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StartSimple Linear Regression with sklearn - Summary Table (4:48)
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StartA Note on Normalization
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StartMultiple Linear Regression with sklearn (3:10)
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StartAdjusted R-Squared (4:45)
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StartAdjusted R-Squared Exercise
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StartFeature Selection through p-values (F-regression) (4:41)
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StartA Note on Calculation of P-Values with sklearn
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StartCreating a Summary Table with the p-values (2:10)
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StartMultiple Linear Regression - Exercise
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StartFeature Scaling (5:38)
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StartFeature Selection through Standardization (5:22)
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StartMaking Predictions with Standardized Coefficients (3:52)
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StartFeature Scaling - Exercise
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StartUnderfitting and Overfitting (2:42)
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StartTraining and Testing (6:54)