Machine Learning in Python

Curriculum

• Linear Regression Free

  23 Lessons 84 Min

  In this part of the course, we will discuss what the course covers, why you need to learn advanced statistics, what’s the differences are with machine learning, and how to get the most out of this training. In this section, you will also expand on what you learned in our statistics training with additional concepts and will apply all the theory in Python. This section serves two purposes: 1) a useful refresher of regression, and 2) a great way to reinforce what you have learned, applying it in practice while coding.

  Course 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 Free

  15 Lessons 55 Min

  While there are many libraries that can compute a regression model, the most numerically stable one is sklearn. It is also the preferred choice of many machine learning professionals. In this section, we implement all we know about regressions in this amazing library.

  What 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 Example

  6 Lessons 37 Min

  An all in one practical example, which will test your understanding of each of the concepts that we have discussed so far. We will focus on a used cars dataset and create a linear regression model to predict the prices of cars. At the end you will have a big assignment where you can dive deep into the optimization of a machine learning model.

  Linear Regression Practical Example (Part 1) Linear Regression Practical Example (Part 2) A note on multicollinearity Linear Regression Practical Example (Part 3) Linear Regression Practical Example (Part 4) Linear Regression Practical Example (Part 5)
• Logistic Regression

  11 Lessons 41 Min

  Data scientists use logistic regressions when the dependent variable is binary (0 and 1, true and false, etc.). This type of data is encountered on a daily basis when working as a data scientist, and here, you will learn how to build a logistic regression, understand tables, interpret the coefficients of a logistic regression, calculate the accuracy of the model, as well as how to test. We will introduce under and overfitting, and will teach you how to test your models.

  Introduction 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 Lessons 15 Min

  Cluster analysis is the most intuitive and important example of unsupervised learning. However, to be able to understand cluster analysis, we must first explore the mathematics behind it.

  Introduction to Cluster Analysis Some examples of clusters Difference between classification and clustering Math prerequisites
• K-Means Clustering

  10 Lessons 50 Min

  In this section, you will learn how to do Cluster analysis. Cluster analysis consists in dividing your data into separate groups based on an algorithm. Clustering is an amazing technique often employed in data science. But what’s more, it makes much more sense to study patterns observed in a particular group rather than trying to find patterns in the entire dataset. We will provide several practical examples that will help you understand how to carry out cluster analysis and the difference between classification and clustering.

  K-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 Clustering

  3 Lessons 14 Min

  In previous sections, we focus extensively on k-means clustering, as it is the fastest and most efficient method for clustering. In this section, we explore other approaches that are less common.

  Types of clustering Dendrogram Heatmaps using Seaborn