Python Statistical Methods: Machine Learning & Data Science | Free Udemy Course

In this Introductory lecture, I will show an overview of the course curriculum and the contents of this course.

In this lecture, you will learn how to install anaconda on your local computer to access its integrated Jupyter notebooks.

In this lecture, you will learn how to download the course materials including course notebooks, exercises and projects of this course.

Students will learn how to create tuples and lists in python. Additionally, the most common methods and functions for manipulating tuples and lists will be explained.

Two built-in data structures will be explained, which are dictionary and set. Students will learn how to create and how to use python functions to manipulate data structured as dictionaries or sets.

Students will learn about NumPy arrays.  They will learn how to create and manipulate NumPy arrays.

Students will learn how to create pandas series and pandas dataframe. In addition to the common functions and methods used by data scientists to manipulate pandas dataframes.

Students will learn the common data types in data science. They will learn numerical and categorical data types.

This is a hands-on exercise for students to apply the skills in this section.

Students will learn what is the mean in statistics and how it can be calculated in python.

You will learn what is the weighted average in statistics and how it can be calculated in python.

Students will learn about the median and how it can be calculated in python.

You will learn two important concepts in statistics which are population and sample. And how they are related to t data analysis.

In this lecture, you will learn how to apply the concepts explained in this module in data science using real-world data.

This is a hands-on exercise to apply the knowledge that you learned in this section.

You will learn what is the range and how to calculate the range of data using python.

You will learn what are the variance and the standard deviation. You will also learn how to calculate variance and standard deviation with python.

Students will learn about percentiles and quartiles in data analysis and how to calculate the percentiles in python.

In this lecture, you will learn about outliers and how they can be detected in data.

In this lecture, we will continue the discussion of the outliers where you will learn how to detect outliers in real-world data.

In this lecture, we will apply what we discussed in this section to real-world data.

This is an exercise for you to put your knowledge into practical skills by solving tasks in this exercise.

In this lecture, you will learn how to create box plots in python. You will learn also how to read box plots in terms of data distribution.

In this lecture you will learn how to create and use violin plots to explore the distribution of data.

In this lecture, we will discuss histograms and density plots. You will learn how to create and read histograms and density plots in terms of data distribution.

Students will learn how to create bar plots in python to describe categorical data.

In this lecture, another visualization for categorical data will be explored which is pie charts. students will learn how to create pie charts with python.

In this lecture, we will have more practical examples of using visualizations to describe data distribution.

This lecture is an exercise for you to practice the skills explained in this section.

In this lecture, you will learn how to create correlations and covariance between variables in data sets. Also, you will learn how to create a correlation matrix.

In this lecture, you will learn how to visualize the correlation between variables using scatter plots.

In this lecture, you will learn how to explore the relationship between more than two variables using mapping and scatter plots.

In this lecture, you will learn how to create heat maps in python to explore the relationship between three variables and to create heatmaps for correlation matrix.

In this lecture, we will discuss more practical examples of exploring the relationships between variables in datasets using correlation,  scatter plots, and heat maps.

This is an exercise for you to put the skills that you learned in this section into practice.

In this lecture, students will find the description of the tasks for the capstone project.

This lecture is a walkthrough of the suggested solution of the capstone project.

In this lecture, you will learn through practical examples what is random sampling in statistics, and you will learn about bias and how it can be calculated in python.

Students will learn through practical examples about an important theorem in statistics and data analysis which is the central limit theorem.

Students will learn a very important probability distribution in statistical modeling which is the normal distribution.

Students will learn how to perform the normality test to check if the data is normally distributed.

Students will learn about skewed data, and how to measure skewness in real-world data.

This lecture is a practical introduction to probability in statistics.

In this lecture, you will learn the common probability distributions in statistics such as the normal distribution and the binomial distribution.

This lecture is an exercise for the students to apply the skills learned in this section.

You will learn how to standardize data to have the same scale, which is a pre-requisite for some machine learning models.

Students will learn how to normalize the data to have the same scale.

Students will learn how to fix skewed data to have a normal distribution using log and square root transformations.

Students will learn how to use the power transformer to transform the data to a more normal distribution and to fix skewed data.

In this lecture, we will apply the skills in this section to solve real-world examples in data science and machine learning.

This lecture is an exercise for you to practice what you learned in this section about data scaling and data transformation.

In this lecture, you will learn how to calculate confidence intervals for continuous data in python.

In this lecture, you will learn how to calculate confidence intervals for classification data in python.

Students will learn the bootstrapping method in statistics and how it can be used in machine learning and python.

You will learn how to use bootstrapping to create non-parametric confidence intervals in python.

This lecture is an exercise for the students to apply what they learned about confidence intervals.

Students will learn very important terms in statistical modeling which are bias and variance.

Students will learn what is overfitting and underfitting in statistical and data modeling.

In this lecture, you will learn the common information criteria that are used in statistical modeling and machine learning for model selection.

Students will learn how to calculate the information criteria for regression models in machine learning.

Students will learn how to calculate the information criteria for classification models in machine learning.

In this lecture, you will learn how to calculate confusion matric and evaluation metrics for classification models such as accuracy, precision, recall, and f1-score metrics.

In this lecture, we will apply the skills in this section to solve real-world examples in data science and machine learning.

This lecture is an exercise for the students to apply what they learned about confidence intervals.

Students will learn how to perform the K-fold cross-validation technique to evaluate machine learning models.

Students will learn how to perform the K-fold cross-validation technique to evaluate machine learning models.

Students will learn how to perform the Leave-One Out validation technique to evaluate machine learning models.

In this lecture, we will apply the skills in this section to solve real-world examples in data science and machine learning.

This lecture is an exercise for the students to apply what they learned about confidence intervals.

In this lecture, students will find a detailed description of the final project.

This lecture contains the first part of the walk-through solution of the project.

This lecture contains the second part of the walk-through solution of the project.

This lecture contains the second part of the walk-through solution of the project.