JA1. Introduction¶
Statement¶
The Learning Journal is a tool for self-reflection on the learning process. In addition to completing directed tasks, you should use the Learning Journal to document your activities, record problems you may have encountered and to draft answers for Discussion Forums and Assignments. The Learning Journal should be updated regularly (on a weekly basis), as the learning journals will be assessed by yourinstructor as part of your Final Grade.
Your learning journal entry must be a reflective statement that considers the following questions:
1. Describe what you did¶
This was the first week of this course, It was an introductory week, to machine learning, data mining, and data analysis topics. I started this week as usual by trying the self-quiz which will give me a good sense of what to expect from my learning. I then started reading the required materials, however I was not able to finish them all, but I will finish them before the end of the week as I’m allocating more time for this course next weekend. I, then, did the discussion assignment and finally I’m doing the learning journal.
2. Describe your reactions to what you did¶
Apart from my inability to finish all the reading materials, I’m very excited about this course. I’m very interested in machine learning and data mining, as we have some small project at work and I would love to know how it works.
3. Describe any feedback you received or any specific interactions you had. Discuss how they were helpful¶
I did not receive a feedback that worth mentioning, especially as this is the first week of the course.
4. Describe your feelings and attitudes¶
My first interaction with R was in statistics course, and I really liked it, as it makes doing statistical computations very convenient. I still do not know the details of using R in machine learning; but I am very excited to learn it in this course.
5. Describe what you learned¶
In this week I learned about the definition of data mining, and how machine learning is considered a subset of data mining. The readings also mentioned different techniques used in machine learning including supervised, unsupervised, and reinforcement learning.
The readings also mentioned some use cases for machine learning, including Prediction, Classification, Clustering, and Association. The readings also mentioned some data analysis techniques like 5As, SEMMA, and CRISP-DM, along with some structures used in machine learning like Decision Trees, Neural Networks, and Support Vector Machines.
6. What surprised me or caused me to wonder?¶
I’m surprised that machine learning is a subset of data mining, I always thought that machine learning is a bigger concept or even a separate concept from data mining.
7. What happened that felt particularly challenging? Why was it challenging to me?¶
The definition of model is super confusing to me, as the word is over used in other topics like software engineering, database design, and web development. Unfortunately, I had some prior knowledge about the word model in my mind and still could not get the meaning of it in machine learning, but I hope next weeks will help on this matter.
8. What skills and knowledge do I recognize that I am gaining?¶
I am gaining theoretical knowledge about machine learning, data mining, and data analysis. I am also gaining practical knowledge about using R in machine learning. I was obsessed with data science before I came to computer science, but I found it hard; now I think my previous courses at UoPeople had given me enough mathematical and programming background to be able to understand data science topics (I still -personally- consider machine learning as part of data science, although it may not be correct).
9. What am I realizing about myself as a learner?¶
I am realizing that I miss a lot of terminology in machine learning and data domain in general which is good as I came to this course to learn those concepts. I still find it hard as I devoted a lot of time for the course in this week but still did not manage to finish all the reading, but I hope next weeks will be better.
10. In what ways am I able to apply the ideas and concepts gained to my own experience?¶
As I mentioned, I work in web development in a small start up but we have some data analysis projects. We use some services sold by Amazon Web Services (AWS), which abstracts a lot of concepts in ready-to-use services, but I would love to know how those services work under the hood.
11. Describe one important thing that you are thinking about in relation to the activity¶
First thing is to allocate more time for this course, and watch some good R tutorial to get more familiar with it.