Data Analysis And Machine Learning With Python

Exploring Data with NumPy, Matplotlib, Seaborn, Plotly, Pandas, and Linear Regression

What you'll 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.

Requirements

Basic knowledge of programming concepts and experience with Python.

A laptop or computer with a recent version of Python and necessary libraries installed, such as Pandas, Numpy, Matplotlib, Seaborn, Sklearn. Access to a dataset to use as an example throughout the course

A desire to learn and apply data analysis and machine learning techniques to real-world problems.

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).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!

Overview

Section 1: Introduction

Lecture 1 Overview of the course and learning objectives

Lecture 2 Installing Anaconda

Lecture 3 Installing VS Code

Section 2: Introduction to Pandas

Lecture 4 Indexing and slicing of Series and DataFrame

Lecture 5 Filtering, sorting, and aggregating data

Lecture 6 removing duplicate data

Lecture 7 Data encoding and normalization in pandas

Lecture 8 Merging and joining DataFrames

Lecture 9 Handling Dates and Times

Lecture 10 GroupBy operations

Lecture 11 Pivot table in Pandas

Lecture 12 Reading and writing data from various file formats (e.g. CSV, Excel, JSON)

Lecture 13 Calculating summary statistics

Section 3: Data Visualization with Matplotlib Seaborn and Plotly

Lecture 14 Line, Scatter, Histograms and Pie charts in Matplotlib

Lecture 15 Subplots in Matplotlib

Lecture 16 Line, Scatter and Bar plots in Seaborn

Lecture 17 Pairplot, Jointplot and FacetGrid in Seaborn

Lecture 18 Customizing appearance of plots in Seaborn

Lecture 19 Scatter, Bar, Histogram and Line plots in Plotly

Lecture 20 3D scatter plot in Plotly

Section 4: Introduction to Numpy

Lecture 21 Numpy Basics

Lecture 22 Advanced Numpy techiniques

Section 5: Exploratory Data Analysis

Lecture 23 Introduction to Exploratory Data Analysis

Lecture 24 Exploratory Data Analysis Case Study

Section 6: Get started with Linear Regression Model

Lecture 25 Introduction to Gradient Descent

Lecture 26 Loss functions in linear regression: mean squared error (MSE)

Lecture 27 Single variable linear regression using Python and Numpy

Lecture 28 Multiple variable linear regression using Python and Numpy

Lecture 29 Linear regression Case using Scikit-learn library in Python

Section 7: Case Study: Examining GDP per capita and investment in education

Lecture 30 Introduction to World Bank Dataset

Lecture 31 Data Preprocessing and Analysis

Lecture 32 Building a linear regression model - Part 1 split dataset into train and test

Lecture 33 Building a linear regression model - Part 2 model training

Lecture 34 Evaluating model performance using Visualization Techniques

Students and recent graduates who are interested in data analysis and machine learning and want to learn how to use Python and Pandas for these tasks,Software developers who want to add data analysis and machine learning capabilities to their skillset,Any one who wants to gain in-depth understanding of data cleaning, preparation, visualization, data analysis and machine learning models