# Machine Learning in Python

Sharpening your predictive modelling skills to set you apart as a data scientist instead of data analyst; covers regressions, classifications, and clustering.

with Iliya Valchanov

Start Course#### Course Overview

Machine Learning in Python builds upon the statistical knowledge you 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. 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 via an array of approaches such as linear regression, logistic regression, and cluster analysis. It combines comprehensive theory with lots of practice to allow you to exercise your Python skills.

#### Curriculum

- 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 Seaborn 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 Simple linear regression - summary table Free A Note on Normalization Free Multiple linear regression Free Adjusted R-squared Free Feature Selection through p-values Free Creating a summary table Free A Note on Calculation of P-Values with sklearn 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 Example6 Lesson 37 Min
- Logistic Regression11 Lesson 41 MinIntroduction to logistic regression A simple example in Python Logistic vs logit function Building a logistic regression An invaluable coding tip Understanding the tables What do the odds actually mean Binary predictors in a logistic regression Calculating the accuracy of the model The concept of overfitting Testing the model
- Cluster Analysis (Basics and Prerequisites)4 Lesson 15 Min
- K-Means Clustering10 Lesson 50 MinK-means clustering A simple example of clustering Clustering categorical data How to choose the number of clusters Pros and Cons of K-means and clustering To standardize or to not standardize Relationship between clustering and regression Market Segmentation with Cluster Analysis (Part 1) Market Segmentation with Cluster Analysis (Part 2) How is clustering useful
- Other Types of Clustering3 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.”

##### Iliya Valchanov

##### Co-founder at 365 Data Science

##### Machine Learning in Python

with Iliya Valchanov