Machine Learning in Python
Sharpening your predictive modelling skills to differentiate you as a data scientist instead of a data analyst; covering regressions, classifications, and clustering.
With Iliya ValchanovStart Course
Machine Learning in Python builds upon the statistical knowledge you have gained earlier in the program. This course focuses on predictive modelling and enters multidimensional spaces which require an understanding of mathematical methods, transformations, and distributions. Moreover, we will introduce 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.
Skills you will gain
What You'll Learn
This course is focused on predictive modelling with multiple different approaches such as linear regression, logistic regression, and cluster analysis. It allows you to exercise your Python skills as the theory is backed by a lot of practice.
- Linear Regression Free23 Lesson 84 MinCourse Introduction Free The linear regression model Free Correlation vs regression Free Geometrical representation of the Linear Regression Model Free Setting up the Environment Free Python packages installation Free First regression in Python Free Using Searborn for graphs Free How to interpret the regression table Free Decomposition of variability Free What is the OLS? Free R-squared Free Multiple linear regression Theory Free Adjusted R-squared Theory Free F-test Free OLS assumptions Free A1: Linearity Free A2: No endogeneity Free A3: Normality and homoscedasticity Free A4: No autocorrelation Free A5: No multicollinearity Free Dealing with categorical data - Dummy variables Free Making predictions with the linear regression Free
- Linear Regression with sklearn Free15 Lesson 55 MinWhat is sklearn Free Game plan for sklearn Free Simple linear regression Free A Note on Normalization Free Simple linear regression - summary table Free Multiple linear regression Free Adjusted R-squared Free Feature Selection through p-values Free A Note on Calculation of P-Values with sklearn Free Creating a summary table Free Feature Scaling Free Feature Selection through standardization Free Making predictions with standardized coefficients Free Underfitting and overfitting Free Training and testing Free
- Linear Regression Practical Example Free6 Lesson 37 Min
- Logistic Regression Free11 Lesson 41 MinIntroduction to logistic regression Free A simple example in Python Free Logistic vs logit function Free Building a logistic regression Free An invaluable coding tip Free Understanding the tables Free What do the odds actually mean Free Binary predictors in a logistic regression Free Calculating the accuracy of the model Free The concept of overfitting Free Testing the model Free
- Cluster Analysis (Basics and Prerequisites) Free4 Lesson 15 Min
- K-Means Clustering Free10 Lesson 50 MinK-means clustering Free A simple example of clustering Free Clustering categorical data Free How to choose the number of clusters Free Pros and Cons of K-means and clustering Free To standardize or to not standardize Free Relationship between clustering and regression Free Market Segmentation with Cluster Analysis (Part 1) Free Market Segmentation with Cluster Analysis (Part 2) Free How is clustering useful Free
- Other Types of Clustering Free3 Lesson 14 Min
“This is the place where you will learn the advanced statistical techniques that are used by successful data scientists. I will teach you regression analysis, clustering, and factor analysis. After this course, you’ll be able to fill your resume with skills and have plenty left over to show off at the interview.”
Co-founder at 365 Data Science
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Machine Learning in Python
With Iliya Valchanov