Machine Learning in Python

Machine Learning in Python builds upon the statistical knowledge you have gained earlier in the program. This couse focuses on predictive modelling and enters 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.

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Section 1

Linear regression

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 for 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.

Premium course icon Welcome to Advanced Statistics!
Premium course icon Introduction to Regression Analysis
Premium course icon The Linear Regression Model
Premium course icon Correlation vs Regression
Premium course icon Geometrical Representation of the Linear Regression Model
Premium course icon First Regression in Python
Premium course icon Using Seaborn for Graphs
Premium course icon How to Interpret the Regression Table
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Premium course icon Decomposition of Variability
Premium course icon What is the OLS?
Premium course icon R-Squared
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Section 2

Multiple Linear Regression

After we learn about the simple linear regression, we build up on that knoweldge to multiple linear regression. In this part, we explore models with many input variables, no matter of numerical or categorical and learn how to make predictions using them.

Premium course icon Multiple Linear Regression
Premium course icon Adjusted R-Squared
Premium course icon Test for Significance of the Model (F-Test)
Premium course icon OLS Assumptions
Premium course icon A1: Linearity
Premium course icon A2: No Endogeneity
Premium course icon A3: Normality and Homoscedasticity
Premium course icon A4: No Autocorrelation
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Premium course icon A5: No Multicollinearity
Premium course icon Dealing with Categorical Data - Dummy Variables
Premium course icon Making Predictions with the Linear Regression
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Section 3

Linear Regression with sklearn

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.

Premium course icon What is sklearn?
Premium course icon Game Plan for sklearn
Premium course icon Simple Linear Regression with sklearn
Premium course icon Multiple Linear Regression with sklearn
Premium course icon Adjusted R-Squared
Premium course icon Creating a Summary Table with the p-values
Premium course icon Feature Scaling
Premium course icon Feature Selection through Standardization
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Premium course icon Making Predictions with Standardized Coefficients
Premium course icon Underfitting and Overfitting
Premium course icon Training and Testing
Premium course icon Linear Regression with sklearn - Practical Example
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Section 4

Logistic Regression

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, how to understand tables, how to interpret the coefficients of a logistic regression, calculate the accuracy of the model, and how to test. We will introduce under and overfitting and will teach you how to test your models.

Premium course icon Introduction to Logistic Regression
Premium course icon A Simple Example in Python
Premium course icon Logistic vs Logit Function
Premium course icon Building a Logistic Regression
Premium course icon Understanding Logistic Regression Tables
Premium course icon What do the Odds Actually Mean
Premium course icon Binary Predictors in a Logistic Regression
Premium course icon Calculating the Accuracy of the Model
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Premium course icon Underfitting and Overfitting
Premium course icon Testing the Model
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Section 5

Cluster Analysis (Basics and Prerequisites)

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.

Premium course icon Introduction to Cluster Analysis. Some Examples of clustering
Premium course icon Difference between Classification and Clustering
Premium course icon Math Prerequisites

Section 6

K-Means Clustering

In this section, you will learn how to do Cluster analysis. Cluster analysus 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, often 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.

Premium course icon K-Means Clustering
Premium course icon Clustering Categorical Data
Premium course icon How to Choose the Number of Clusters
Premium course icon Pros and Cons of K-Means Clustering
Premium course icon Relationship between Clustering and Regression
Premium course icon Market Segmentation with Cluster Analysis (Part 1)
Premium course icon How is Clustering Useful?

Section 7

Other Types of Clustering

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

Premium course icon Types of Clustering
Premium course icon Dendrograms and Heatmaps
MODULE 3

Machine and Deep Learning

This course is part of Module 3 of the 365 Data Science Program. The complete training consists of four modules, each building up on your knowledge from the previous one. Expanding on your statistical and programming skills from Modules 1 and 2, Module 3 is designed to improve your programming skills and develop your advanced statistical thinking. You will learn how to build complete linear and logistic regression models, how to cluster data, and how to build deep learning models with TensorFlow 2.0.

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