Data Analysis and Machine Learning with Python

Data Analysis and Machine Learning with Python, available at $19.99, has an average rating of 3, with 34 lectures, based on 3 reviews, and has 1041 subscribers.

You will learn about 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. This course is ideal for individuals who are 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 or Software developers who want to add data analysis and machine learning capabilities to their skillset or Any one who wants to gain in-depth understanding of data cleaning, preparation, visualization, data analysis and machine learning models It is particularly useful for 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 or Software developers who want to add data analysis and machine learning capabilities to their skillset or Any one who wants to gain in-depth understanding of data cleaning, preparation, visualization, data analysis and machine learning models.

Enroll now: Data Analysis and Machine Learning with Python

Summary

Title: Data Analysis and Machine Learning with Python

Price: $19.99

Average Rating: 3

Number of Lectures: 34

Number of Published Lectures: 34

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $39.99

Quality Status: approved

Status: Live

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.

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Overview of the course and learning objectives

Lecture 2: Installing VS Code

Lecture 3: Installing Anaconda

Chapter 2: Introduction to Pandas

Lecture 1: Indexing and slicing of Series and DataFrame

Lecture 2: Filtering, sorting, and aggregating data

Lecture 3: removing duplicate data

Lecture 4: Data encoding and normalization in pandas

Lecture 5: Merging and joining DataFrames

Lecture 6: Handling Dates and Times

Lecture 7: GroupBy operations

Lecture 8: Pivot table in Pandas

Lecture 9: Reading and writing data from various file formats (e.g. CSV, Excel, JSON)

Lecture 10: Calculating summary statistics

Chapter 3: Data Visualization with Matplotlib Seaborn and Plotly

Lecture 1: Line, Scatter, Histograms and Pie charts in Matplotlib

Lecture 2: Subplots in Matplotlib

Lecture 3: Line, Scatter and Bar plots in Seaborn

Lecture 4: Pairplot, Jointplot and FacetGrid in Seaborn

Lecture 5: Customizing appearance of plots in Seaborn

Lecture 6: Scatter, Bar, Histogram and Line plots in Plotly

Lecture 7: 3D scatter plot in Plotly

Chapter 4: Introduction to Numpy

Lecture 1: Numpy Basics

Lecture 2: Advanced Numpy techiniques

Chapter 5: Exploratory Data Analysis

Lecture 1: Introduction to Exploratory Data Analysis

Lecture 2: Exploratory Data Analysis Case Study

Chapter 6: Get started with Linear Regression Model

Lecture 1: Introduction to Gradient Descent

Lecture 2: Loss functions in linear regression: mean squared error (MSE)

Lecture 3: Single variable linear regression using Python and Numpy

Lecture 4: Multiple variable linear regression using Python and Numpy

Lecture 5: Linear regression Case using Scikit-learn library in Python

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

Lecture 1: Introduction to World Bank Dataset

Lecture 2: Data Preprocessing and Analysis

Lecture 3: Building a linear regression model – Part 1 split dataset into train and test

Lecture 4: Building a linear regression model – Part 2 model training

Lecture 5: Evaluating model performance using Visualization Techniques

Instructors

LunchCoffee Education
Company

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3 stars: 1 votes

4 stars: 1 votes

5 stars: 0 votes

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