Machine Learning Introduction with Python
AI and machine learning are in demand and Python is the #1 programming language for data scientists. That means learning Python can set you up for a lucrative career.
In this path, you’ll work your way through the basics of Python, master various components of machine learning, like calculus, linear algebra, linear regression, and much more.
Start developing skills in statistics and probability to form valuable insights all from the comfort of your browser.
Learn the fundamentals of Python …
This interactive Python course is designed for beginners. It teaches Python programming basics. You don't need any prior programming experience. We start from the beginning. Even if your code skills are not the best, we can help you build your data science foundation that will allow you to start your journey towards becoming a data professional.
The following topics are the focus of this …
This course will continue to teach you the basics of Python data science. To make it easier and faster to master the basics, we divided these courses into four sections. This second part builds upon the knowledge gained in our Variables and Data Types in Python course.
This course is focused on the following:
This course will continue your learning of Python for data science. These courses were divided into four sections to make it easier and faster for you to grasp the basics. This third section builds upon the knowledge gained in our Variables Data Types and Lists in Python course, and our For Loops Conditional Statements course in Python.
Data science is in high demand. Take advantage of this …
This course will continue your learning of Python for data science. These courses were divided into four sections to make it easier and faster for you to grasp the basics. This fourth section builds upon the knowledge gained in our Variables Data Types and Lists in Python class, our For Loops & Conditional Statements In Python course, and our Dictionaries Frequency Tables and Functions in …
This intermediate course will allow you to continue your Python for Data Science journey. This intermediate course will allow you to improve your data science skills and introduce you to advanced techniques essential for data cleaning and analysis.
You've now learned the basics of Python programming and are ready to dig deeper into how you can optimize and optimize your code using Python libraries. This course will introduce you to two of the most popular Python libraries, NumPy or pandas.
Data visualization is an essential part of data science. This is the best way for data scientists to communicate powerful insights and drive action. Dataquest's Data Visualization Fundamentals course builds upon your Python programming knowledge and basic proficiency using NumPy and pandas.
This course will teach you the basics of data visualization in Python. It will provide a balance …
Our statistics intermediate course will teach you how to summarize distributions using mean, median, or mode. We'll also cover when and how to use them. Finally, we will discuss which statistic provides the most information about a particular distribution so you not only know how to apply them but why.
Our Machine Learning Fundamentals course will teach you the basics of machine-learning. We will cover concepts like K-Nearest Neighbors Algorithms (KNN), and discuss error metrics such the Mean Squared Error, and Root Mean Squared Error. Hyperparameter optimization is a technique that optimizes machine learning algorithms to improve the accuracy and performance. Next, you will learn about …
Calculus is an important area in mathematics, and plays an integral part in many machine-learning algorithms. A solid understanding of calculus is essential if you are to be able to understand the workings of machine learning algorithms as a data scientist.
Linear algebra is an important area of mathematics and essential to understanding how machine learning algorithms work. Linear algebra for machine learning will teach you the concepts of machine learning systems such as neural networks, and how to backpropagate them.
Our interactive intermediate course in machine learning will help you dive deeper into machine learning. Additional algorithms like logistic regression and kâ€“means clustering will be taught. Additionally, you'll learn how to detect overfitting as well as the bias-variance tradeoff.
Deep Learning Fundamentals teaches you the basics of deep learning networks. This course will teach you how to use scikit-learn for building and training neural networks. You will learn about graph theory, activation functions and hidden layers as well as how to classify images.
Our Kaggle Fundamentals course will show you how to get started, and how to participate in Kaggle competitions. Kaggle allows you to sign up and compete against other data scientists to create the most precise analysis of a given data set. Kaggle has a strong competition. Being among the top finishers will earn you bragging rights as well as a bullet point on your resume.