Python for Data Science and Machine Learning Bootcamp

Python for Data Science and Machine Learning Bootcamp, available at $109.99, has an average rating of 4.57, with 184 lectures, 1 quizzes, based on 145375 reviews, and has 728139 subscribers.

You will learn about Use Python for Data Science and Machine Learning Use Spark for Big Data Analysis Implement Machine Learning Algorithms Learn to use NumPy for Numerical Data Learn to use Pandas for Data Analysis Learn to use Matplotlib for Python Plotting Learn to use Seaborn for statistical plots Use Plotly for interactive dynamic visualizations Use SciKit-Learn for Machine Learning Tasks K-Means Clustering Logistic Regression Linear Regression Random Forest and Decision Trees Natural Language Processing and Spam Filters Neural Networks Support Vector Machines This course is ideal for individuals who are This course is meant for people with at least some programming experience It is particularly useful for This course is meant for people with at least some programming experience.

Enroll now: Python for Data Science and Machine Learning Bootcamp

Summary

Title: Python for Data Science and Machine Learning Bootcamp

Price: $109.99

Average Rating: 4.57

Number of Lectures: 184

Number of Quizzes: 1

Number of Published Lectures: 165

Number of Published Quizzes: 1

Number of Curriculum Items: 185

Number of Published Curriculum Objects: 166

Original Price: $189.99

Quality Status: approved

Status: Live

What You Will Learn

Use Python for Data Science and Machine Learning

Use Spark for Big Data Analysis

Implement Machine Learning Algorithms

Learn to use NumPy for Numerical Data

Learn to use Pandas for Data Analysis

Learn to use Matplotlib for Python Plotting

Learn to use Seaborn for statistical plots

Use Plotly for interactive dynamic visualizations

Use SciKit-Learn for Machine Learning Tasks

K-Means Clustering

Logistic Regression

Linear Regression

Random Forest and Decision Trees

Natural Language Processing and Spam Filters

Neural Networks

Support Vector Machines

Who Should Attend

This course is meant for people with at least some programming experience

Target Audiences

This course is meant for people with at least some programming experience

Are you ready to start your path to becoming a Data Scientist!?

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With?over 100 HD video lectures?and?detailed code notebooks for every lecture?this is one of?the most comprehensive course for data science and machine learning on Udemy!

We’ll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

Programming with Python

NumPy with Python

Using pandas?Data Frames to solve complex tasks

Use pandas?to handle Excel Files

Web scraping with python

Connect Python?to SQL

Use matplotlib and seaborn for data visualizations

Use plotly for interactive visualizations

Machine Learning with SciKit Learn, including:

Linear Regression

K Nearest Neighbors

K Means Clustering

Decision Trees

Random Forests

Natural?Language Processing

Neural Nets and Deep Learning

Support Vector?Machines

and much, much more!

Enroll in the course and become a data scientist today!

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Introduction to the Course

Lecture 2: Course Help and Welcome

Lecture 3: Course FAQs

Chapter 2: Environment Set-Up

Lecture 1: Python Environment Setup

Chapter 3: Jupyter Overview

Lecture 1: Updates to Notebook Zip

Lecture 2: Jupyter Notebooks

Lecture 3: Optional: Virtual Environments

Chapter 4: Python Crash Course

Lecture 1: Welcome to the Python Crash Course Section!

Lecture 2: Introduction to Python Crash Course

Lecture 3: Python Crash Course – Part 1

Lecture 4: Python Crash Course – Part 2

Lecture 5: Python Crash Course – Part 3

Lecture 6: Python Crash Course – Part 4

Lecture 7: Python Crash Course Exercises – Overview

Lecture 8: Python Crash Course Exercises – Solutions

Chapter 5: Python for Data Analysis – NumPy

Lecture 1: Welcome to the NumPy Section!

Lecture 2: Introduction to Numpy

Lecture 3: Numpy Arrays

Lecture 4: Quick Note on Array Indexing

Lecture 5: Numpy Array Indexing

Lecture 6: Numpy Operations

Lecture 7: Numpy Exercises Overview

Lecture 8: Numpy Exercises Solutions

Chapter 6: Python for Data Analysis – Pandas

Lecture 1: Welcome to the Pandas Section!

Lecture 2: Introduction to Pandas

Lecture 3: Series

Lecture 4: DataFrames – Part 1

Lecture 5: DataFrames – Part 2

Lecture 6: DataFrames – Part 3

Lecture 7: Missing Data

Lecture 8: Groupby

Lecture 9: Merging Joining and Concatenating

Lecture 10: Operations

Lecture 11: Data Input and Output

Chapter 7: Python for Data Analysis – Pandas Exercises

Lecture 1: Note on SF Salary Exercise

Lecture 2: SF Salaries Exercise Overview

Lecture 3: SF Salaries Solutions

Lecture 4: Ecommerce Purchases Exercise Overview

Lecture 5: Ecommerce Purchases Exercise Solutions

Chapter 8: Python for Data Visualization – Matplotlib

Lecture 1: Welcome to the Data Visualization Section!

Lecture 2: Introduction to Matplotlib

Lecture 3: Matplotlib Part 1

Lecture 4: Matplotlib Part 2

Lecture 5: Matplotlib Part 3

Lecture 6: Matplotlib Exercises Overview

Lecture 7: Matplotlib Exercises – Solutions

Chapter 9: Python for Data Visualization – Seaborn

Lecture 1: Introduction to Seaborn

Lecture 2: Distribution Plots

Lecture 3: Categorical Plots

Lecture 4: Matrix Plots

Lecture 5: Grids

Lecture 6: Regression Plots

Lecture 7: Style and Color

Lecture 8: Seaborn Exercise Overview

Lecture 9: Seaborn Exercise Solutions

Chapter 10: Python for Data Visualization – Pandas Built-in Data Visualization

Lecture 1: Pandas Built-in Data Visualization

Lecture 2: Pandas Data Visualization Exercise

Lecture 3: Pandas Data Visualization Exercise- Solutions

Chapter 11: Python for Data Visualization – Plotly and Cufflinks

Lecture 1: Introduction to Plotly and Cufflinks

Lecture 2: READ ME FIRST BEFORE PLOTLY PLEASE!

Lecture 3: Plotly and Cufflinks

Chapter 12: Python for Data Visualization – Geographical Plotting

Lecture 1: Introduction to Geographical Plotting

Lecture 2: Choropleth Maps – Part 1 – USA

Lecture 3: Choropleth Maps – Part 2 – World

Lecture 4: Choropleth Exercises

Lecture 5: Choropleth Exercises – Solutions

Chapter 13: Data Capstone Project

Lecture 1: Welcome to the Data Capstone Projects!

Lecture 2: 911 Calls Project Overview

Lecture 3: 911 Calls Solutions – Part 1

Lecture 4: 911 Calls Solutions – Part 2

Lecture 5: Bank Data

Lecture 6: Finance Data Project Overview

Lecture 7: Finance Project – Solutions Part 1

Lecture 8: Finance Project – Solutions Part 2

Lecture 9: Finance Project – Solutions Part 3

Chapter 14: Introduction to Machine Learning

Lecture 1: Welcome to Machine Learning. Here are a few resources to get you started!

Lecture 2: Welcome to the Machine Learning Section!

Lecture 3: Supervised Learning Overview

Lecture 4: Evaluating Performance – Classification Error Metrics

Lecture 5: Evaluating Performance – Regression Error Metrics

Lecture 6: Machine Learning with Python

Chapter 15: Linear Regression

Lecture 1: Linear Regression Theory

Lecture 2: model_selection Updates for SciKit Learn 0.18

Lecture 3: Linear Regression with Python – Part 1

Lecture 4: Linear Regression with Python – Part 2

Instructors

Jose Portilla
Head of Data Science at Pierian Training

Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 619 votes

2 stars: 1129 votes

3 stars: 9549 votes

4 stars: 51632 votes

5 stars: 82439 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!