The course is part of these learning paths
Learn the ways in which data comes in many forms and formats with the second course in the Data and Machine Learning series.
Traditionally, machine learning has worked really well with structured data but is not as efficient in solving problems with unstructured data. Deep learning works very well with both structured and unstructured data, and it has had successes in fields like translation, and image classification, and many others. Learn and study how to explain the reasons deep learning is so popular. With many different data types, learn about its different formats, and we'll analyze the vital libraries that allow us to explore and organize data.
This course is made up of 8 lectures, accompanied by 5 engaging exercises along with their solutions. This course is part of the Data and Machine Learning learning paths from Cloud Academy.
- Learn and understand the functions of machine learning when confronted with structured and unstructured data
- Be able to explain the importance of deep learning
- It would be recommended to complete the Introduction to Data and Machine Learning course, before starting.
The Github repo for this course, including code and datasets, can be found here.
Hey guys, welcome back. In this video we will review the exercise one of section two. Exercise one is a data manipulation problem. We are asked to load the data sets from the data folder, the international airline passengers folder. This is a data set that contains the number of passengers within a certain span of years for the airline industry. And you're required to inspect the data set once you've loaded it in Pandas with the info and heads command. And then you'll have to use the pd.to_datetime function, it's a Pandas function to change the column containing the month to a datetime type.
What this means, it means that as it's written in the dataset the column month is a string. And the pd.to_datetime function converts the strings in that column on to objects that Pandas understands as datetimes. After we've done that we can use the datetime column to index the dataframe so we can set the index to be that column using the said index method. And then plot the data with the appropriate plot. I'm not going to tell you which is the most appropriate plot for this data. You have to decide between, amongst the plot that we've seen. Remember to choose the appropriate scale and to level the axis. So this is exercise one, go ahead and do it. And when you're done, feel free to check the solution. Good luck!
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.