Machine learning gives you unimaginably powerful insights into data. Today, implementations of machine learning have been adopted throughout Industry and its concepts are numerous. This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases. By the end of the course, you will have learned to apply various machine learning algorithms and will have mastered Python's packages and libraries to facilitate computation. You will be able to implement your own machine learning models after taking this course.
About The Author
Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City.
We need the air pricing data from a website to work with. You will learn to do that in this section.
After determining the source of the data, we need to retrieve the data.
DOM is the structure of elements that form the web page. We need to get some details of the structure by parsing it.
To get real-time alerts when a particular event occurs, we need to use IFTTT.
To deploy our app, we'll move on to working in a text editor. You will put together the entire code to get the final result.
Before deciding strategies for the IPO market, we need to study the IPO market and derive inferences from it.
The consideration and inclusion of all factors affecting the market is called feature engineering. Modeling this is as important as the data used in building the model.
Instead of giving the value of the return, you can predict the IPO for a trade you will buy or not buy. The model used is logistic regression.
It is important to know which features will make the offering successful. You can find that out in this section.
To create a model, we have to first have a training dataset. We will use the pocket app for this.
You can't move forward with just the URLs of the stories. You would need the full article. So let's check out how to do that in this video.
Machine learning models work on numerical data. So we will need to transform our text into numerical data using NLP.
You will learn about the linear support vector machine in this video. The SVM algorithm separates data points linearly into classes.
We have provided a training dataset. But we also need a stream of articles as a testing dataset to run our model against.
It would make life easier if you get a personalized e-mail of your stories, right? So you will learn how to do that in this video.
Research is the most important thing before we start working on designing a strategy.
Once you have studied the various aspects of the market, it is time to develop a trading strategy. You will learn it in this video.
Now that we have our baseline, we will build our first regression model for prediction of stocks.
Another algorithm to work with is dynamic time warping. It provides us a metric which will inform us about the similarity between two time series.
It is very important to understand machine learning's concepts before working with it.
In order to work with images, we need to transform them into a matrix form, that is, numerical form.
We will use algorithms to find similar images in the database.
We will combine what we have studied so far to build an image similarity engine.
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