Financial markets are fickle beasts that can be extremely difficult to navigate for the average investor. This course will introduce you to machine learning, a field of study that gives computers the ability to learn without being explicitly programmed, while teaching you how to apply these techniques to quantitative trading. Using Python libraries, you’ll discover how to build sophisticated financial models that will better inform your investing decisions. Ideally, this one will buy itself back and then some!
- Access 64 lectures & 11 hours of content 24/7
- Get a crash course in quantitative trading from stocks & indices to momentum investing & backtesting
- Discover machine learning principles like decision trees, ensemble learning, random forests & more
- Set up a historical price database in MySQL using Python
- Learn Python libraries like Pandas, Scikit-Learn, XGBoost & Hyperopt
- Access source code any time as a continuing resource
R is a programming language and software environment for statistical computing and graphics that is widely used among statisticians and data miners for data analysis. In this course, you’ll get a thorough run-through of how R works and how it’s applied to data science. Before you know it, you’ll be crunching numbers like a pro, and be better qualified for many lucrative careers.
- Access 82 lectures & 9 hours of content 24/7
- Cover basic statistical principles like mean, median, range, etc.
- Learn theoretical aspects of statistical concepts
- Discover datatypes & data structures in R, vectors, arrays, matrices & more
- Understand Linear Regression
- Visualize data in R using a variety of charts & graphs
- Delve into descriptive & inferential statistics
All featured courses are designed for educational purposes only and do not reflect our views or recommendations. Please note that all course purchasers invest at their own risk.
Big Data sounds pretty daunting doesn’t it? Well, this course aims to make it a lot simpler for you. Using Hadoop and MapReduce, you’ll learn how to process and manage enormous amounts of data efficiently. Any company that collects mass amounts of data, from startups to Fortune 500, need people fluent in Hadoop and MapReduce, making this course a must for anybody interested in data science.
- Access 71 lectures & 13 hours of content 24/7
- Set up your own Hadoop cluster using virtual machines (VMs) & the Cloud
- Understand HDFS, MapReduce & YARN & their interaction
- Use MapReduce to recommend friends in a social network, build search engines & generate bigrams
- Chain multiple MapReduce jobs together
- Write your own customized partitioner
- Learn to globally sort a large amount of data by sampling input files
Java seems an appropriate name for a language that seems so dense, you may need a cuppa joe after 10 minutes of self-study. Luckily, you can learn all you need to know in this short course. You’ll scale the behemoth that is object-oriented programming, mastering classes, objects, and more to conquer a language that powers everything from online games to chat platforms.
- Learn Java inside & out w/ 35 lectures & 7 hours of content
- Master object-oriented (OO) programming w/ classes, objects & more
- Understand the mechanics of OO: access modifiers, dynamic dispatch, etc.
- Dive into the underlying principles of OO: encapsulation, abstraction & polymorphism
- Comprehend how information is organized w/ packages & jars
Are you familiar with self-driving cars? Speech recognition technology? These things would not be possible without the help of Machine Learning–the study of pattern recognition and prediction within the field of computer science. This course is taught by Stanford-educated, Silicon Valley experts that have decades of direct experience under their belts. They will teach you, in the simplest way possible (and with major visual techniques), to put Machine Learning and Python into action. With these skills under your belt, your programming skills will take a whole new level of power.
- Get introduced to Machine Learning w/ 14.5 hours of instruction
- Learn from a team w/ decades of practical experience in quant trading, analytics & e-commerce
- Understand complex subjects w/ the help of animations
- Use hundreds of lines of source code w/ comments to implement natural language processing & machine learning for text summarization, text classification in Python
- Study natural language processing & sentiment analysis w/ Python
Sentiment Analysis or Opinion Mining is a field of Neuro-linguistic Programming (NLP) that aims to extract subjective information like positive/negative, like/dislike, emotional reactions, and the like. It’s an essential component to Machine Learning as it provides valuable training data to a machine. Over this course, you’ll learn real examples why Sentiment Analysis is important and how to approach specific problems using Sentiment Analysis.
- Access 19 lectures & 4 hours of content 24/7
- Learn Rule-Based & Machine Learning-Based approaches to solving Sentiment Analysis problems
- Understand Sentiment Lexicons & Regular Expressions
- Design & implement a Sentiment Analysis measurement system in Python
- Grasp the underlying Sentiment Analysis theory & its relation to binary classification
- Identify use-cases for Sentiment Analysis
- Perform a real Twitter Sentiment Analysis
Decision trees and random forests are two intuitive and extremely effective Machine Learning techniques that allow you to better predict outcomes from a selected input. Both methods are commonly used in business, and knowing how to implement them can put you ahead of your peers. In this course, you’ll learn these techniques by exploring a famous (but morbid) Machine Learning problem: predicting the survival of a passenger on the Titanic.
- Access 19 lectures & 4.5 hours of content 24/7
- Design & implement a decision tree to predict survival probabilities aboard the Titanic
- Understand the risks of overfitting & how random forests help overcome them
- Identify the use-cases for decision trees & random forests
- Use provided source code to build decision trees & random forests
Deep Learning is an exciting branch of Machine Learning that provide solutions for processing the high-dimensional data produced by Computer Vision. This introductory course brings you into the complex, abstract world of Computer Vision and artificial neural networks. By the end, you’ll have a solid foundation in a core principle of Machine Learning.
- Access 9 lectures & 2 hours of content 24/7
- Design & implement a simple computer vision use-case: digit recognition
- Train a neural network to classify handwritten digits in Python
- Build a neural network & specify the training process
- Grasp the central theory underlying Deep Learning & Computer Vision
- Understand use-cases for Computer Vision & Deep Learning
Assuming you’re an internet user (which seems likely), you use or encounter recommendation systems all the time. Whenever you see an ad or product that seems eerily in tune with whatever you were just thinking about, it’s because of a recommendation system. In this course, you’ll learn how to build a variety of these systems using Python, and be well on your way to a high-paying career.
- Access 20 lectures & 4.5 hours of content 24/7
- Build Recommendation Engines that use content based filtering to find products that are most relevant to users
- Discover Collaborative Filtering, the most popular approach to recommendations
- Identify similar users using neighborhood models like Euclidean Distance, Pearson Correlation & Cosine
- Use Matrix Factorization to identify latent factor methods
- Learn recommendation systems by building a movie-recommending app in Python
Python’s one of the easiest yet most powerful programming languages you can learn, and it’s proven its utility at top companies like Dropbox and Pinterest. In this quick and dirty course, you’ll learn to write clean, efficient Python code, learning to expedite your workflow by automating manual work, implementing machine learning techniques, and much more.
- Dive into Python w/ 10.5 hours of content
- Acquire the database knowledge you need to effectively manipulate data
- Eliminate manual work by creating auto-generating spreadsheets w/ xlsxwriter
- Master machine learning techniques like sk-learn
- Utilize tools for text processing, including nltk
- Learn how to scrape websites like the NYTimes & Washington Post using Beautiful Soup
- Complete drills to consolidate your newly acquired knowledge
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