# 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.

##### Our graduates work at exciting places:     ## 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. Welcome to Advanced Statistics! Introduction to Regression Analysis The Linear Regression Model Correlation vs Regression Geometrical Representation of the Linear Regression Model First Regression in Python Using Seaborn for Graphs How to Interpret the Regression Table
Show all lessons Decomposition of Variability What is the OLS? R-Squared
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## 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. Multiple Linear Regression Adjusted R-Squared Test for Significance of the Model (F-Test) OLS Assumptions A1: Linearity A2: No Endogeneity A3: Normality and Homoscedasticity A4: No Autocorrelation
Show all lessons A5: No Multicollinearity Dealing with Categorical Data - Dummy Variables Making Predictions with the Linear Regression
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## 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. What is sklearn? Game Plan for sklearn Simple Linear Regression with sklearn Multiple Linear Regression with sklearn Adjusted R-Squared Creating a Summary Table with the p-values Feature Scaling Feature Selection through Standardization
Show all lessons Making Predictions with Standardized Coefficients Underfitting and Overfitting Training and Testing Linear Regression with sklearn - Practical Example
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## 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. Introduction to Logistic Regression A Simple Example in Python Logistic vs Logit Function Building a Logistic Regression Understanding Logistic Regression Tables What do the Odds Actually Mean Binary Predictors in a Logistic Regression Calculating the Accuracy of the Model
Show all lessons Underfitting and Overfitting Testing the Model
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## 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. Introduction to Cluster Analysis. Some Examples of clustering Difference between Classification and Clustering Math Prerequisites

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

## 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. Types of Clustering 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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