Python for Machine Learning & Data Science Masterclass

Python for Machine Learning & Data Science Masterclass, available at $129.99, has an average rating of 4.64, with 232 lectures, 4 quizzes, based on 15973 reviews, and has 112516 subscribers.

You will learn about You will learn how to use data science and machine learning with Python. You will create data pipeline workflows to analyze, visualize, and gain insights from data. You will build a portfolio of data science projects with real world data. You will be able to analyze your own data sets and gain insights through data science. Master critical data science skills. Understand Machine Learning from top to bottom. Replicate real-world situations and data reports. Learn NumPy for numerical processing with Python. Conduct feature engineering on real world case studies. Learn Pandas for data manipulation with Python. Create supervised machine learning algorithms to predict classes. Learn Matplotlib to create fully customized data visualizations with Python. Create regression machine learning algorithms for predicting continuous values. Learn Seaborn to create beautiful statistical plots with Python. Construct a modern portfolio of data science and machine learning resume projects. Learn how to use Scikit-learn to apply powerful machine learning algorithms. Get set-up quickly with the Anaconda data science stack environment. Learn best practices for real-world data sets. Understand the full product workflow for the machine learning lifecycle. Explore how to deploy your machine learning models as interactive APIs. This course is ideal for individuals who are Beginner Python developers curious about Machine Learning and Data Science with Python It is particularly useful for Beginner Python developers curious about Machine Learning and Data Science with Python.

Enroll now: Python for Machine Learning & Data Science Masterclass

Summary

Title: Python for Machine Learning & Data Science Masterclass

Price: $129.99

Average Rating: 4.64

Number of Lectures: 232

Number of Quizzes: 4

Number of Published Lectures: 231

Number of Published Quizzes: 4

Number of Curriculum Items: 236

Number of Published Curriculum Objects: 235

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

You will learn how to use data science and machine learning with Python.
You will create data pipeline workflows to analyze, visualize, and gain insights from data.
You will build a portfolio of data science projects with real world data.
You will be able to analyze your own data sets and gain insights through data science.
Master critical data science skills.
Understand Machine Learning from top to bottom.
Replicate real-world situations and data reports.
Learn NumPy for numerical processing with Python.
Conduct feature engineering on real world case studies.
Learn Pandas for data manipulation with Python.
Create supervised machine learning algorithms to predict classes.
Learn Matplotlib to create fully customized data visualizations with Python.
Create regression machine learning algorithms for predicting continuous values.
Learn Seaborn to create beautiful statistical plots with Python.
Construct a modern portfolio of data science and machine learning resume projects.
Learn how to use Scikit-learn to apply powerful machine learning algorithms.
Get set-up quickly with the Anaconda data science stack environment.
Learn best practices for real-world data sets.
Understand the full product workflow for the machine learning lifecycle.
Explore how to deploy your machine learning models as interactive APIs.

Who Should Attend

Beginner Python developers curious about Machine Learning and Data Science with Python

Target Audiences

Beginner Python developers curious about Machine Learning and Data Science with Python

This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla’s over 3 million students to learn about the future today!

What is in the course?

Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I’ve worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python!

This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientists can be over $150,000 dollars, and we’ve created this course to help guide students to learning a set of skills to make them extremely hirable in today’s workplace environment.

We’ll cover everything you need to know for the full data science and machine learning tech stack required at the world’s top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies! We’ve structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but whywe use them.This course is balanced between practical real world case studiesand mathematical theory behind the machine learning algorithms.

We cover advanced machine learning algorithms that most other courses don’t! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.

This comprehensive course is designed to be on par with Bootcamps that usually cost thousands of dollars and includes the following topics:

Programming with Python
NumPy with Python
Deep dive into Pandas for Data Analysis
Full understanding of Matplotlib Programming Library
Deep dive into seaborn for data visualizations
Machine Learning with SciKit Learn, including:
- Linear Regression
- Regularization
- Lasso Regression
- Ridge Regression
- Elastic Net
- K Nearest Neighbors
- K Means Clustering
- Decision Trees
- Random Forests
- Natural Language Processing
- Support Vector Machines
- Hierarchal Clustering
- DBSCAN
- PCA
- Model Deployment
- and much, much more!

As always, we’re grateful for the chance to teach you data science, machine learning, and python and hope you will join us inside the course to boost your skillset!

-Jose and Pierian Data Inc. Team

Course Curriculum

Chapter 1: Introduction to Course

Lecture 1: Welcome to the Course!

Lecture 2: COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP!

Lecture 3: Anaconda Python and Jupyter Install and Setup

Lecture 4: Note on Environment Setup – Please read me!

Lecture 5: Environment Setup

Chapter 2: OPTIONAL: Python Crash Course

Lecture 1: OPTIONAL: Python Crash Course

Lecture 2: Python Crash Course – Part One

Lecture 3: Python Crash Course – Part Two

Lecture 4: Python Crash Course – Part Three

Lecture 5: Python Crash Course – Exercise Questions

Lecture 6: Python Crash Course – Exercise Solutions

Chapter 3: Machine Learning Pathway Overview

Lecture 1: Machine Learning Pathway

Chapter 4: NumPy

Lecture 1: Introduction to NumPy

Lecture 2: NumPy Arrays

Lecture 3: NumPy Indexing and Selection

Lecture 4: NumPy Operations

Lecture 5: NumPy Exercises

Lecture 6: Numpy Exercises – Solutions

Chapter 5: Pandas

Lecture 1: Introduction to Pandas

Lecture 2: Series – Part One

Lecture 3: Series – Part Two

Lecture 4: DataFrames – Part One – Creating a DataFrame

Lecture 5: DataFrames – Part Two – Basic Properties

Lecture 6: DataFrames – Part Three – Working with Columns

Lecture 7: DataFrames – Part Four – Working with Rows

Lecture 8: Pandas – Conditional Filtering

Lecture 9: Pandas – Useful Methods – Apply on Single Column

Lecture 10: Pandas – Useful Methods – Apply on Multiple Columns

Lecture 11: Pandas – Useful Methods – Statistical Information and Sorting

Lecture 12: Missing Data – Overview

Lecture 13: Missing Data – Pandas Operations

Lecture 14: GroupBy Operations – Part One

Lecture 15: GroupBy Operations – Part Two – MultiIndex

Lecture 16: Combining DataFrames – Concatenation

Lecture 17: Combining DataFrames – Inner Merge

Lecture 18: Combining DataFrames – Left and Right Merge

Lecture 19: Combining DataFrames – Outer Merge

Lecture 20: Pandas – Text Methods for String Data

Lecture 21: Pandas – Time Methods for Date and Time Data

Lecture 22: Pandas Input and Output – CSV Files

Lecture 23: Pandas Input and Output – HTML Tables

Lecture 24: Pandas Input and Output – Excel Files

Lecture 25: Pandas Input and Output – SQL Databases

Lecture 26: Pandas Pivot Tables

Lecture 27: Pandas Project Exercise Overview

Lecture 28: Pandas Project Exercise Solutions

Chapter 6: Matplotlib

Lecture 1: Introduction to Matplotlib

Lecture 2: Matplotlib Basics

Lecture 3: Matplotlib – Understanding the Figure Object

Lecture 4: Matplotlib – Implementing Figures and Axes

Lecture 5: Matplotlib – Figure Parameters

Lecture 6: Matplotlib – Subplots Functionality

Lecture 7: Matplotlib Styling – Legends

Lecture 8: Matplotlib Styling – Colors and Styles

Lecture 9: Advanced Matplotlib Commands (Optional)

Lecture 10: Matplotlib Exercise Questions Overview

Lecture 11: Matplotlib Exercise Questions – Solutions

Chapter 7: Seaborn Data Visualizations

Lecture 1: Introduction to Seaborn

Lecture 2: Scatterplots with Seaborn

Lecture 3: Distribution Plots – Part One – Understanding Plot Types

Lecture 4: Distribution Plots – Part Two – Coding with Seaborn

Lecture 5: Categorical Plots – Statistics within Categories – Understanding Plot Types

Lecture 6: Categorical Plots – Statistics within Categories – Coding with Seaborn

Lecture 7: Categorical Plots – Distributions within Categories – Understanding Plot Types

Lecture 8: Categorical Plots – Distributions within Categories – Coding with Seaborn

Lecture 9: Seaborn – Comparison Plots – Understanding the Plot Types

Lecture 10: Seaborn – Comparison Plots – Coding with Seaborn

Lecture 11: Seaborn Grid Plots

Lecture 12: Seaborn – Matrix Plots

Lecture 13: Seaborn Plot Exercises Overview

Lecture 14: Seaborn Plot Exercises Solutions

Chapter 8: Data Analysis and Visualization Capstone Project Exercise

Lecture 1: Capstone Project Overview

Lecture 2: Capstone Project Solutions – Part One

Lecture 3: Capstone Project Solutions – Part Two

Lecture 4: Capstone Project Solutions – Part Three

Chapter 9: Machine Learning Concepts Overview

Lecture 1: Introduction to Machine Learning Overview Section

Lecture 2: Why Machine Learning?

Lecture 3: Types of Machine Learning Algorithms

Lecture 4: Supervised Machine Learning Process

Lecture 5: Companion Book – Introduction to Statistical Learning

Chapter 10: Linear Regression

Lecture 1: Introduction to Linear Regression Section

Lecture 2: Linear Regression – Algorithm History

Lecture 3: Linear Regression – Understanding Ordinary Least Squares

Lecture 4: Linear Regression – Cost Functions

Lecture 5: Linear Regression – Gradient Descent

Lecture 6: Python coding Simple Linear Regression

Instructors

Jose Portilla
Head of Data Science at Pierian Training
Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 73 votes
2 stars: 81 votes
3 stars: 747 votes
4 stars: 4758 votes
5 stars: 10314 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!

Menu