
This course includes our updated coding exercises so you can practice your skills as you learn.
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Master Python basics by exploring comparison and logic operators, if/elif/else control flow, for and while loops, range usage, and list comprehension with emphasis on indentation.
Get an overview of the NumPy topics we will discuss in this course! Numpy is a key part of data science and machine learning.
Learn to create NumPy arrays from Python lists and built-in functions, and generate random data with NumPy. Explore array attributes, shapes, and methods like reshape, max, min, argmax, and argmin.
Explore a tips data frame in pandas, inspecting columns, index, and basic attributes with df.head, df.tail, and df.info; compute descriptive statistics using df.describe and experiment with transpose for readability.
Explore pandas dt time methods to extract year, month, day, and day of week, enabling feature engineering, date parsing with to_datetime, and time-based resampling.
Create a blank Matplotlib figure canvas, add axes, and plot data, then adjust x and y limits, labels, and titles for multiple plots.
Explore seaborn grids to automatically generate subplot grids from categorical data using catplot and PairGrid, map plots to grid regions, and customize with hue and legends.
Identify the two main machine learning types: supervised learning uses historical labeled data to predict future values, while unsupervised learning uses unlabeled data to discover patterns.
Train and evaluate polynomial regression by transforming three features into nine via polynomial features, then compare MAE and RMSE with linear regression. Save the polynomial converter for future data points.
Explore the bias-variance trade-off in supervised learning, from overfitting and underfitting to understanding how model complexity and training versus test error guide polynomial regression and other algorithms.
Learn how regularization reduces overfitting by adding bias and penalties to the loss, and compare L1, L2, and elastic net with lambda, alpha, and cross-validation to tune them.
Explore ridge regression as a regularization technique for linear regression, adding a shrinkage penalty on beta coefficients to reduce overfitting, with lambda tuned via cross-validation.
Explore L1 regularization with Lasso regression, learn how LassoCV selects alpha values via cross-validation, and compare sparsity and interpretability with Ridge in 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 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