4.69 (1097 reviews)
☑ Machine Learning with Python
☑ Data Science with Python
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:
K Nearest Neighbors
K Means Clustering
Natural Language Processing
Support Vector Machines
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
Introduction to Course
EARLY BIRD INFO
COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
Anaconda Python and Jupyter Install and 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
Introduction to NumPy
NumPy Indexing and Selection
Numpy Exercises - Solutions
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
Introduction to Matplotlib
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
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
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
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.
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.
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)
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.
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!
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.
It is really helpful for my understanding of python and machine learning. Especially the part of regression and classification concepts.
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.
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
Jose Portilla is great teacher, very experienced in his field and also shares his thoughts and knowledge with great simplicity on these complex topics.
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.
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 !!!
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.
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.
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.