Use your data to predict future events with the help of machine learning. This course will walk you through creating a machine learning prediction solution and will introduce Python, the scikit-learn library, and the Jupyter Notebook environment.
Hello! My name is Jerry Kurata, and welcome to Understanding Machine Learning with Python. In this course, you will gain an understanding of how to perform Machine Learning with Python. You will get there by covering major topics like how to format your problem to be solvable, how to prepare your data for use in a prediction, and how to combine that data with algorithms to create models that can predict the future.
By the end of this course, you will be able to use Python and the scikit-learn library to create Machine Learning solutions. And you will understand how to evaluate and improve the performance of the solutions you create.
Before you begin, make sure you are already familiar with software development and basic statistics. However, your software experience does not have to be in Python, since you will learn the basics in this course. When you use Python together with scikit-learn, you will see why this is the preferred development environment for many Machine Learning practitioners.
You will do all the demos using the Jupyter Notebook environment. This environment combines live code with narrative text to create a document with can be executed and presented as a web page.
I hope you’ll join me, and I look forward to helping you on your learning journey here at Pluralsight.
Course Overview Hi, my name is Jerry Kurata, and welcome to my course, Understanding Machine Learning with Python. These days, machine learning is all around us, from helping doctors diagnose patients to assisting us in driving our cars. As we go about our day, we may be utilizing machine learning applications and not even realize it. It silently scans our email inboxes for spam emails and ensures that stores are stocked with the goods we want to buy when we need them. This course will introduce you to machine learning and the technology behind it. You will see why companies are in such a rush to use machine learning to grow their business and increase profits. You will learn how developers and data scientists use machine learning to predict events based on data, specifically you will learn how to format a problem to be solvable, where to get the data, and how to combine that data with algorithms to create models that can predict the future. Throughout this course, we'll utilize Python and a number of its libraries to make creating machine learning solutions easy. However, you do not need prior experience with Python. In this course, we learn by doing, and the code we will use will be explained as we create our solution. By the end of this course, you will know the how, when, and why of building a machine learning solution with Python. You will have the skills you need to transform a one-line problem statement into a tested prediction model that solves the problem. I look forward to you joining me on this journey of understanding machine learning with Python from Pluralsight.
Getting Started in Machine Learning Hi I'm Jerry Kurata. Welcome to the Pluralsight course on Understanding Machine Learning with Python. In this course you'll learn how to apply machine learning to solve problems that are difficult, and some might say impossible to solve with standard coding techniques. This first module will provide some basic information about machine learning. This includes examples of machine learning, a definition of machine learning, and importantly, how machine learning differs from traditional programming. We will go over the two basic types of machine learning, supervised and unsupervised. We will see each of these types in action, which will clarify how they differ and when each type of machine learning should be used. After that we will review the content of this course, and the skills you need and the skills you do not need for this course. We will then have a brief discussion of how machine learning fits into the larger subject of data science. There's also a section on programming in Python and using the Jupyter Notebook environment we will use in the demos.
Training the Model Hi, I'm Jerry Kurata. Welcome back to the Pluralsight course, Understanding Machine Learning with Python. In the previous modules, we covered the workflow steps of asking the right question, where we defined our solution statement, preparing data in which we obtained raw data and transformed it into the data we will use for training, and selecting the algorithm, where we selected the initial algorithm we will train and evaluate. In this module, we'll put the pieces together and train the algorithm we selected with the data we prepared. When we are done with this training process, we will have a model that can predict if a person is likely to develop diabetes. In this module, we will get a detailed understanding of the training process, introduce the Scikit-learn library, which can make the training and evaluation process much easier. Then, we will go back to Python and train our algorithm with our diabetes data and produce a trained model. A good definition of Machine Learning training is letting specific data teach a Machine Learning algorithm to create a specific prediction model. Notice the use of the term specific. Data drives the training, and if the data changes over time or new data is used, in man case, we need to retrain. And we want to retrain if the data changes. Retraining will ensure that our model can take advantage of the new data to make better predictions. And also verify the algorithm can still create a high-performance model with the new data. Now let's dive deeper into Machine Learning training process.
Testing Your Model's Accuracy Hello, I'm Jerry Kurata. Welcome back to the Pluralsight course on Understanding Machine Learning with Python. In the previous modules, we went through the workflow steps of defining our solution statement, getting the data, and selecting an initial algorithm. In the last module, we trained our initial algorithm with our training data and produced a trained naïve Bayes model. In this module, we will evaluate this trained model and see how well it can predict the likelihood of a person developing diabetes. We will evaluate our trained model by using a set of test data. Remember this test data was not used to train the model, so it should give us an accurate evaluation of the real-world performance of our model. This evaluation will provide us with a series of results that we can use to decide if the performance of the model is acceptable. The results will also give some ideas on how we might revise the workflow steps to improve the performance. Throughout our evaluation process, we need to keep in mind that statistics only provide us data. We are the ones that interpret this data and decide if it is good or bad, and we need to define good or bad in the context of how we will use our model, but enough theory for now. Let's go back to Python and evaluate the model.