Udemy

Platform

English

Language

Data Science

Category

2021 Python for Machine Learning & Data Science Masterclass

Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!

4.69 (1097 reviews)

2021 Python for Machine Learning & Data Science Masterclass

15187

Students

30.5 hours

Content

Dec 2020

Last Update
$94.99
Regular Price

What you will learn

Machine Learning with Python

Data Science with Python




Description

Early Bird Release for the full upcoming 2021 Python for Machine Learning and Data Science Masterclass!

Please note! This is currently in an Early Bird Beta access, meaning we are still going to be continually adding content to the course (even though we are already at over 20 hours of content!) Since we're still adding content and taking student feedback as we complete the course through the start of 2021, students who enroll now will get access to a wide variety of benefits!

What do you get with Early Bird Access?

You will get exclusive access to weekly live video streams where we will go through interactive machine learning projects! You'll be able to directly ask questions during the streams that will coincide with section launches corresponding to new machine learning algorithms added to the course content! These weekly streams will also include live Q&A with the instructor of the course, Jose Portilla. We will also be taking in student feedback to shape certain upcoming streaming projects. These streams will only be accessible to early bird students, and will be removed once the course is fully complete and launched!

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 comprehensive course is designed to be on par with bootcamps that usually cost thousands of dollars, the final course will include 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

    • Manifold Learning

    • 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



Content

Introduction to Course

EARLY BIRD INFO

COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!

Anaconda Python and Jupyter Install and Setup

Environment Setup

OPTIONAL: Python Crash Course

OPTIONAL: Python Crash Course

Python Crash Course - Part One

Python Crash Course - Part Two

Python Crash Course - Part Three

Python Crash Course - Exercise Questions

Python Crash Course - Exercise Solutions

Machine Learning Pathway Overview

Machine Learning Pathway

NumPy

Introduction to NumPy

NumPy Arrays

NumPy Indexing and Selection

NumPy Operations

NumPy Exercises

Numpy Exercises - Solutions

Pandas

Introduction to Pandas

Series - Part One

Series - Part Two

DataFrames - Part One - Creating a DataFrame

DataFrames - Part Two - Basic Properties

DataFrames - Part Three - Working with Columns

DataFrames - Part Four - Working with Rows

Pandas - Conditional Filtering

Pandas - Useful Methods - Apply on Single Column

Pandas - Useful Methods - Apply on Multiple Columns

Pandas - Useful Methods - Statistical Information and Sorting

Missing Data - Overview

Missing Data - Pandas Operations

GroupBy Operations - Part One

GroupBy Operations - Part Two - MultiIndex

Combining DataFrames - Concatenation

Combining DataFrames - Inner Merge

Combining DataFrames - Left and Right Merge

Combining DataFrames - Outer Merge

Pandas - Text Methods for String Data

Pandas - Time Methods for Date and Time Data

Pandas Input and Output - CSV Files

Pandas Input and Output - HTML Tables

Pandas Input and Output - Excel Files

Pandas Input and Output - SQL Databases

Pandas Pivot Tables

Pandas Project Exercise Overview

Pandas Project Exercise Solutions

Matplotlib

Introduction to Matplotlib

Matplotlib Basics

Matplotlib - Understanding the Figure Object

Matplotlib - Implementing Figures and Axes

Matplotlib - Figure Parameters

Matplotlib - Subplots Functionality

Matplotlib Styling - Legends

Matplotlib Styling - Colors and Styles

Advanced Matplotlib Commands (Optional)

Matplotlib Exercise Questions Overview

Matplotlib Exercise Questions - Solutions

Seaborn Data Visualizations

Introduction to Seaborn

Scatterplots with Seaborn

Distribution Plots - Part One - Understanding Plot Types

Distribution Plots - Part Two - Coding with Seaborn

Categorical Plots - Statistics within Categories - Understanding Plot Types

Categorical Plots - Statistics within Categories - Coding with Seaborn

Categorical Plots - Distributions within Categories - Understanding Plot Types

Categorical Plots - Distributions within Categories - Coding with Seaborn

Seaborn - Comparison Plots - Understanding the Plot Types

Seaborn - Comparison Plots - Coding with Seaborn

Seaborn Grid Plots

Seaborn - Matrix Plots

Seaborn Plot Exercises Overview

Seaborn Plot Exercises Solutions

Data Analysis and Visualization Capstone Project Exercise

Capstone Project Overview

Capstone Project Solutions - Part One

Capstone Project Solutions - Part Two

Capstone Project Solutions - Part Three

Machine Learning Concepts Overview

Introduction to Machine Learning Overview Section

Why Machine Learning?

Types of Machine Learning Algorithms

Supervised Machine Learning Process

Companion Book - Introduction to Statistical Learning

Linear Regression

Introduction to Linear Regression Section

Linear Regression - Algorithm History

Linear Regression - Understanding Ordinary Least Squares

Linear Regression - Cost Functions

Linear Regression - Gradient Descent

Python coding Simple Linear Regression

Overview of Scikit-Learn and Python

Linear Regression - Scikit-Learn Train Test Split

Linear Regression - Scikit-Learn Performance Evaluation - Regression

Linear Regression - Residual Plots

Linear Regression - Model Deployment and Coefficient Interpretation

Polynomial Regression - Theory and Motivation

Polynomial Regression - Creating Polynomial Features

Polynomial Regression - Training and Evaluation

Bias Variance Trade-Off

Polynomial Regression - Choosing Degree of Polynomial

Polynomial Regression - Model Deployment

Regularization Overview

Feature Scaling

Introduction to Cross Validation

Regularization Data Setup

L2 Regularization - Ridge Regression Theory

L2 Regularization - Ridge Regression - Python Implementation

L1 Regularization - Lasso Regression - Background and Implementation

L1 and L2 Regularization - Elastic Net



Reviews

S
S.10 November 2020

I have Finished the old version of this Course two years ago, I just start this version. I love the way that Jose teach and explain the course. This course is the best in the world.

K
Kevin10 November 2020

Jose is a fantastic teacher; he does an excellent job thoroughly explaining every topic in an intuitive way and the provided resources allow you to work alongside the videos to follow his work as he walks through it. If you know a little bit of Python or another language but are looking to really dive in, this is the best course I've found.

A
Andres10 November 2020

THis is awesome, the most complete course I've seen so far, and I've already done a lot of other courses on this topic (Most of them also from Jose)

E
Emiel10 November 2020

Jose Portilla's courses have helped me learn python to the point where I can write my own scripts with ease in just a couple weeks of forced covid lockdown vacation. I can't think of a single point of improvement and this is the third course I'm following from Jose.

M
Muthu9 November 2020

Very clear explanations. Very Knowledgeable Instructure. Covers the basics and methodically builds on it. Warns you of common mistakes students make (and surprisingly I made several of them)! Wish you covered more than linear regressions. (But 23 hour ... course is a lot of value). Overall Excellent course! I wish I could give you more than 5 stars Jose!

W
Wade8 November 2020

Great pacing, very helpful explanations, and very comprehensive. I get tired of sifting through tutorial after tutorial of individual pieces, and I like how the instructor breaks down the entire libraries and shows common themes and goals that all programmers will want.

Y
Yixiao8 November 2020

It is really helpful for my understanding of python and machine learning. Especially the part of regression and classification concepts.

J
John6 November 2020

Perfect! I am currently working through the Udacity Data Analyst Nanodegree and finding my skills lacking. I believe this will help me get through that program successfully.

J
Jose2 November 2020

Excellent course. Ideal for people who has worked with Python before and want to start working with Pandas and SKLearn. Excellent complement course very useful to get along with machine learning

L
Leandro1 November 2020

Jose Portilla is great teacher, very experienced in his field and also shares his thoughts and knowledge with great simplicity on these complex topics.

P
Paulo30 October 2020

Before start this course I had previous experience on the basics of Python and I was already using it to produce some stuff at my job and my personal life. As this is my first experience with Machine Learning, I decided to go through all the review lectures on data analysis and have watched all the first classes on Pandas, Numpy, Matplotlib and Seaborn. It was like learning it for the first time, Jose is an amazing teacher and makes everything much clear. All the remaining doubts I use to have were clarified. I do not consider this course to be a good option if you had no previous experience with Python. But if you have already taken some introductory classes or course, this is for sure a great option to get your Python skills on the next level.

C
Christian30 October 2020

Anche se ho appena cominciato il corso, si capisce subito quanto sia professionale e utile. L'insegnante come sempre si distingue per le spiegazioni chiare e dettagliate. Complimenti!!! Even if I have just started the course, it soon becomes clear how professional and useful it is. The teacher is always distinguished by clear and detailed explanations. Compliments !!!

S
Sagar28 October 2020

I Just Started Out But I Have Completed Jose's Python Bootcamp & Sure That This Is Gonna Be A Great Experience! Jose's Teaching Style Is Very Awesome! I Appreciate His Ability To Teach Complex Things In Easily Understandable Way.

D
David27 October 2020

After some initial frustration in the setup, I was able to move through the course material with no problems. I found it enjoyable and very logically presented. The inclusion of the theory and overviews helped expand my understanding of the subject and gain perspective and context.

C
Christopher27 October 2020

So far so good, I've enjoyed taking other courses from Jose, so I knew going in that it would be a fun and engaging course.


Coupons

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2769460

Udemy ID

1/20/2020

Course created date

10/27/2020

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