Python for Machine Learning & Data Science Masterclass

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

4.67 (14886 reviews)

Udemy

platform

English

language

Data Science

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.

Why take this course?

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 why we use them. This course is balanced between practical real world case studies and 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

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

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

Gustavo

November 9, 2023

Great course, this is the second course I've taken in this platform and definetly this is a must for anyone that's getting into data science and ML. This course combines great with the SQL course from Jose. Both of those courses helped me to get a job as data scientist. So if you are thinking about geting into this one, my advice is to take it.

Eric

November 6, 2023

It's an amazing lecture, it covers detail like Feature Engineering, pandas, seaborn...etc, I highly recommend this lecture !

Yogesh

November 3, 2023

Very good for someone like me with no knowledge of python, pandas or Machine learning. The course equips one with a basic knowledge of python before gradually building up towards the machine learning algorithms. The theory and intuition lectures provided a great background before delving into the algorithms themselves. I found the course very well structured and thought through. The exercises worked without any issues with rare minor exceptions, that I was able to resolve with a quick internet search. Overall great course and a great tutor. Thanks v much!

October 28, 2023

I have completed the section on NumPy up to this point. The content falls far short of what I would expect from a masterclass explanation on NumPy. It's on par with the free tutorials available on YouTube, and there doesn't seem to be any added value in a paid course. There is a noticeable lack of theoretical explanation on how NumPy works and its advantages. This is what I anticipated from a masterclass, not just a rundown of NumPy methods. I hope to see improvements in the following sections, particularly in Pandas and the other topics

Vanildo

October 26, 2023

So far, so good. Excellent didatics. Fantastic teacher, with pristine English, very different from the Indians. Jose is GREAT!!!!

Renato

October 24, 2023

Awesome course! I just want to know the location of the PDF presentations and this can be more awesome!

Earl

October 24, 2023

Yes, I need to learn Python, newbie at Python language, starting from ground zero, no previous coding experience. So far this course has shown me how to set up the Python environment, down load Jupyter and course materials. gained some confidence just from these initial steps!

Shambhab

October 23, 2023

Very detailed, instructive, even if you don't know python, it'll indulge you into machine learning based on python with many variations...

Jediah

October 21, 2023

Lots of excellent information in this course. I really appreciate the baseline details around the python tools (matplotlib, seaborn, pandas, numpy, etc.) - that definitely left me wanting more exposure, but was more than enough to prepare for the upcoming course lectures. I also like the historical overviews of the algorithms, as well as the various scenario based predictions used that could resonate at a basic level. In addition to the accessible outcomes, Jose goes into the details of the algorithms and gives a high-level basis on what they're doing and why - vs. just providing the outcome and glossing over the math. I likely spent as much (if not more) time in this course going out and researching more details of the algorithms and math itself, but that also helped ensure I was retaining context and information. I think the structure of the course (background, supervised, then unsupervised) was a good approach too, although when I got to the end of the course, I was a little disappointed how abruptly it ended, e.g. "See you in the next lecture"... [where is the next lecture!?]. Finally, given how prevalent NLP and LLMs are now, I would have liked to see a little more in the course on those topics. Overall though, 5+ stars - well worth the time it took to get through!

Emmanuel

July 28, 2023

I really fell in love with it. Honestly speaking this is what I have been looking for. Finally I have found it. Jose you are remarkable, thanks for this course.

Ishan

July 17, 2023

Greatly organized and structured course content delivered with simple and comprehensive theory and implementation with sufficient exercises to build the intuition of machine learning.

Doğa

July 15, 2023

I was very suspicious before starting this course, now I'm impressed. Normally I never ever give ratings before finish but believe me guys, I have taken 2 ML course before one of them was from a famous prof. and the other was the most populer course around which is Andrew NG ML Course. Maaan this is different, the way Jose explains the concepts is so f**cking clear. I want to hug this guy. Thank you bro.

Joel

July 10, 2023

Best Learning Experiences!! for ML Overview. Highly Recommend. You will not regret it. Jose way of explaining things are simple, humble, concise and precise.

Bohan

July 9, 2023

Actually, most of the course materials are based on the ISLR book (An Introduction to Statistical Learning), including the examples. The definition explanations in the videos are vague and lack precision and clarity. One positive aspect is the Python notebook, which is good to view.

Berhane

July 7, 2023

He is a savvy, magnanimous and informative data scientist/Professor. I wish I could talk to him on the phone to shake up my knowledge. Thanks Jose so much. I wish you a healthful, joyful and prosperous life. God bless angels!

Coupons

Submit by	Date	Coupon Code	Discount	Status
-	10/27/2020	EARLY_BIRDS_ONLY	95% OFF	expired
-	12/30/2020	2021_YEAR_END	95% OFF	expired
-	2/26/2021	PIERIAN_DATA	95% OFF	expired
-	6/3/2021	LARGEST_COURSE_EVER	95% OFF	expired
-	4/27/2022	SPRING_SALE	95% OFF	expired

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