Data Analysis And Machine Learning With Python

Data Analysis And Machine Learning With Python
Published 4/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

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.

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

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 13 Calculating summary statistics

Section 3: Data Visualization with Matplotlib Seaborn and Plotly

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

Lecture 25 Introduction to Gradient Descent

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

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 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