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Data Analysis and Machine Learning with Python
Exploring Data with NumPy, Matplotlib, Seaborn, Plotly, Pandas, and Linear Regression

What you will learn

How to use the powerful data analysis and manipulation capabilities of the Pandas library in Python to prepare, clean, and analyze data.

How to use machine learning model such as linear regression to make predictions and interpret data insights.

Techniques for handling missing values, removing duplicates, working with categorical data, and reshaping and pivoting data.

How to use various visualization and statistical techniques to understand the structure and characteristics of your data through Exploratory Data Analysis (EDA)

How to implement linear regression model in Pandas and Scikit-learn, evaluate the performance using various metrics.

Description

Welcome to our course, “Data Analysis with Python Pandas and Machine Learning Model”!

This course is designed to provide you with a comprehensive understanding of the powerful data analysis and manipulation capabilities of the Pandas library in Python, as well as the fundamental concepts and techniques of linear regression, one of the most widely used machine learning models.

You will learn how to use the Pandas library to prepare, clean, and analyze data, as well as how to use machine learning models such as linear regression to make predictions and interpret data insights. The course places a strong emphasis on data cleaning and preparation, which is a critical step in the data analysis process and is often overlooked in other courses.

Throughout the course, you will gain hands-on experience with data cleaning, preparation, and visualization techniques, including handling missing values,  working with categorical data, and reshaping and pivoting data. You will also learn how to use various visualization and statistical techniques to understand the structure and characteristics of your data through Exploratory Data Analysis (EDA).


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You will learn how to implement linear regression model in Pandas and Scikit-learn, evaluate their performance using various metrics, and interpret model coefficients and their significance.

This course is suitable for different levels of audiences, from beginner to advanced, who are interested in data analysis and machine learning. The course provides a hands-on approach to learning, with real-world examples that allow learners to apply the concepts and techniques they’ve learned.

By the end of the course, you will have a solid understanding of the data analysis and manipulation capabilities of Pandas and the concepts and techniques of linear regression, as well as the ability to analyze, report, and interpret data using a machine learning model.

Join us now and take your data analysis and machine learning skills to the next level!

English
language

Content

Introduction

Overview of the course and learning objectives
Installing VS Code
Installing Anaconda

Introduction to Pandas

Indexing and slicing of Series and DataFrame
Filtering, sorting, and aggregating data
removing duplicate data
Data encoding and normalization in pandas
Merging and joining DataFrames
Handling Dates and Times
GroupBy operations
Pivot table in Pandas
Reading and writing data from various file formats (e.g. CSV, Excel, JSON)
Calculating summary statistics

Data Visualization with Matplotlib Seaborn and Plotly

Line, Scatter, Histograms and Pie charts in Matplotlib
Subplots in Matplotlib
Line, Scatter and Bar plots in Seaborn
Pairplot, Jointplot and FacetGrid in Seaborn
Customizing appearance of plots in Seaborn
Scatter, Bar, Histogram and Line plots in Plotly
3D scatter plot in Plotly

Introduction to Numpy

Numpy Basics
Advanced Numpy techiniques

Exploratory Data Analysis

Introduction to Exploratory Data Analysis
Exploratory Data Analysis Case Study

Get started with Linear Regression Model

Introduction to Gradient Descent
Loss functions in linear regression: mean squared error (MSE)
Single variable linear regression using Python and Numpy
Multiple variable linear regression using Python and Numpy
Linear regression Case using Scikit-learn library in Python

Case Study: Examining GDP per capita and investment in education

Introduction to World Bank Dataset
Data Preprocessing and Analysis
Building a linear regression model – Part 1 split dataset into train and test
Building a linear regression model – Part 2 model training
Evaluating model performance using Visualization Techniques