Continue the journey to data and machine learning, with this course from Cloud Academy.
In previous courses, the core principles and foundations of Data and Machine Learning have been covered and best practices explained.
This course gives an informative introduction to deep learning and introducing neural networks.
This course is made up of 12 expertly instructed lectures along with 4 exercises and their respective solutions.
- Understand the core principles of deep learning
- Be able to execute all factors of the framework of neural nets
- It would be advisable to complete the Intro to Data and Machine Learning course before starting.
Hey guys, welcome back. In this video, we'll review exercise one of section four. In exercise one of section four, we're asked to predict whether or not some people will be diagnosed diabetes from a set of variables of exams. So this is the population of Pima Indians. It's a very famous dataset that we got from UCI and it contains information about the patients including pregnancies, glucose, blood pressure, and then a few other medical examinations, and the last column is the outcome which is a binary variable.
So it's a classification problem, and you're guided through a series of steps that go from loading the data, creating a histogram to inspect the features, and exploring the correlations between the features and the outcome column. We suggest using the seaborn pairplot, but you can also draw a heat map as we saw in the lecture. Then there are a few open questions. Do features need standardization? And if so, what kind? Are we gonna use MinMax or standard? And then finally, prepare x and y using a machine learning model. Do you need dummy columns? And make sure you define your target variable. So as usual, try to do the exercise first, and then feel free to watch the video with the solution. Good luck.
Please note: the Pima Indians Diabetes dataset can be found at this GitHub repository.
I am a Data Science consultant and trainer. With Catalit I help companies acquire skills and knowledge in data science and harness machine learning and deep learning to reach their goals. With Data Weekends I train people in machine learning, deep learning and big data analytics. I served as lead instructor in Data Science at General Assembly and The Data Incubator and I was Chief Data Officer and co-founder at Spire, a Y-Combinator-backed startup that invented the first consumer wearable device capable of continuously tracking respiration and activity. I earned a joint PhD in biophysics at University of Padua and Université de Paris VI and graduated from Singularity University summer program of 2011.