July 30, 2020

#2: Emmanuel Acheampong - AI/ML Technologist and Online Learning Enthusiast


Emmanuel is an Artificial Intelligence and Machine Learning enthusiast/hobbyist. His love for technology and math has influenced him to create cool projects such as detailed data analyses on social media platforms, Google Assistant conversational actions, and many others.

In this episode he shares how he got into computer science, and what motivates him to build out personal side projects and take online courses outside of class.

Key Points

  • [01:32] How Emmanuel Got Into Computer Science
  • [05:06] Balancing School and Online Courses
  • [10:28] Motivation to take Online Courses
  • [13:48] Online Course Recommendations
  • [16:13] How Emmanuel Got Into Artificial Intelligence/Machine Learning
  • [24:23] Importance of Building Personal Projects
  • [26:36] What is "Tutorial Hell"?
  • [29:13] Do you need a PhD to do Artificial Intelligence and Machine Learning?
  • [33:44] Advice on How to Get Into AI/ML and Data Science

Resources Mentioned

Transcript

Note: Black Enterprise Network transcripts are generated using speech recognition software and human transcription. They may contain errors, although we do our best to avoid them. Please check the corresponding audio before quoting a transcript in print. Questions? Errors found in a transcript? Email us! 

Kimmiko James  [0:00]  
A small percentage of black people are currently represented in the tech industry and entrepreneurial spaces. This includes engineers, startup founders, investors, especially those that hold leaders. I want to share their stories. Welcome to the black enterprise network podcast. My name is Kimmiko. And today I'm going to be talking with Emmanuel archipel.

Emmanuel Acheampong  [0:22]  
Name is Emmanuel Champa, from a current graduate student at Notre Dame. But before that I used to be a machine learning engineer.

Kimmiko James  [0:29]  
Emmanuel majored in computer science and math during undergrad and is currently pursuing his master's in science, technology. And entrepreneurship, is enthusiasm for technology. And learning has led him creating cool projects, using natural language processing to analyze hip hop music, an app that uses deep learning to match skin tones to make a foundation and many more. He shares how he got started with creating these projects and price advice and how you can get started to be sure to check out his LinkedIn and GitHub profile if you want to keep up with him and the cool stuff he's building. And with that, let's get into the episode. What started or influenced your path into computer science, technology and programming.

Emmanuel Acheampong  [1:10]  
So I'm supposed to have like a deep, like, philosophical No, you will be like, when I was a child, I used to do this and this. I literally had no connection to my journey in computer science, right? So I literally, I came to Berea College, but an awesome school from from Ghana originally, and I came to school here. And I wanted to do electrical engineering. That's the honest truth. On this, I came, I wanted to do electrical engineering. But Brianna didn't have an electrical engineering course. I didn't have a track for electrical engineering, but they had a computer science track. As soon as it was free tuition. I was like, no what, let me just do computer science because I can't do electrical engineer. I have no idea what I was getting into. I literally had no clue what computer science was. Our programming was at all. So I took it, it was hard. It was very hard at first, but you know, we we press the jet. And that's, that's that's how I got into computer science. I wasn't very involved in my school CS program at all. I did the bare minimum. So I wasn't very interested, right? Because I learned differently from how CSS taught in schools. Right. And so I think that there came a time that I think one of the evolutionized my tracks in computer science was that I was failing really horribly, right. And by the time I started, I had started using LinkedIn. Right? And so I started reaching out to people for help, right? Because like people region, reach out to people on LinkedIn for like internship. And so I wanted people to actually help me learn computer science, because I really sucked at it. So I reached out to this guy, I don't I'm not gonna say his name, but like, he was a software engineer at Google. And he responded, we blew my mind. But he was like, Hey, you should take online courses, right? Because if you're not learning what you need to learn in school, online courses are very helpful. And that's what essentially changed the track of my computer science unit. Right? So it's because I wasn't doing class. But then I realized that, you know, with online courses, there's no fear of grades. Like you, there's no fear of like, there's no competition, there's no like, oh, somebody has to be better than other person. And online learning community, I don't know. But at the time, we were very open and open to help him, anyone and everyone who asks for help. So I took a course on Coursera. And then from there, I started taking courses a lot, especially Coursera. This is not sponsored by like this, because it was the best lesson for me at the moment at the time. And because the way they have structured their curriculum, I was able to learn things that I didn't really grasp in school, right. And that really allowed me to start like exploring things, I think, being able to learn without being penalized for not knowing stuff, right is such a blessing, because that's how I started like experimenting with these projects. And like, because there's no penalty, there's no penalty, right? It's not like you're gonna get a grade if a TA doesn't see it think you did the right thing. So that's where my experimentation projects mainly stemmed from. And that led to me exploring things in a very open minded way. And that's how mine the application of mine, computer science background comes in.

Kimmiko James  [4:39]  
So let's jump into motivation to work on and complete this online courses. Because, you know, you're a student at the time taking these courses in class, but you also want to learn something online, like what motivated you to just continue working on that site. hustle, if you want to call it that.

Emmanuel Acheampong  [5:03]  
I got a shoot. So I mean, a are the beginning, right? It was it was very difficult trying to balance like schoolwork with online coursework. It, it was hard because you still need a grade, right? Like you're still at the end of your curriculum, like you're still gonna be graded. So you want to do well in school, however, like things that I was learning outside of school were very interesting. And I think that makes sense. So it was super interesting to me. So I didn't want to take like machine learning courses for my campus. I was thinking them online. I don't at the time, I don't think they had it may introduce them later. But it didn't really have it. But I was learning online. And it was very interesting to me. Right? And so like the motivation was that I'm actually learning stuff. I know, it's not a perfect answer. But that really was my motivation. Because at first, it's really difficult to balance the two. But then as time goes on, you're like, Oh, my God, I can build this. And then you build from it. And people use it, and they love it. And then they're like, Oh, no, it kind of it was just a gradual thing.

Kimmiko James  [6:05]  
Kind of just adding on to that, since we've been connected for a while, I have seen you just complete at least maybe 100 courses, if not more. And you've also done some of the certification tests as well, and which for anyone that doesn't know, certain companies or websites, they, they have these courses where you take them and you learn everything. But at the end, you can apply that knowledge to a test and which sometimes you have to pay for it. And if you pass you get the certification. But if you don't, then you have to like you don't get your money back, you have to study up and then retry again. So adding to that, my question for you is why did you put so many so much effort into trying to earn these certifications? Because I saw you fail a few times. But then a few months later, you would come back and say, Yeah, I earned it. So what made you want to try for these instead of just you know, walking away with stuff you learned from them?

Emmanuel Acheampong  [7:06]  
That's a great question. That's actually a very good question. So I was, I was introduced to like cloud computing, in the class in class in school in actual school, right? To me as do like networking with like Google Cloud. And that's the first time I got introduced to it. What the one thing that I loved about Google Cloud, I don't know if people still do it. But last time that we had to like do a boot our computers, we would install Linux and also windows, right to run them. Like if you wanted to do Linux always wanted to do windows. But then with the Google Cloud, I realized I could just spin up a VM without having to like, install anything and mess up my machine. I could run Linux on my machine in the cloud without having to install other things. So that was how I got into like, cloud computing, right? So virtual machines. But then again, as I was taking these courses, I realized that these cloud technologies are these. Yeah, these cloud technologies could be used for more than just virtual machines. And like Google Cloud hadn't at the time, because also I'm used to like Coronavirus, AWS and Azure, but I'm focusing on Google Cloud, because that was my entryway. And so I started to do more and more things on the cloud, right? I started to do like, data analytics on a cloud, like sequel in the cloud. And then I started doing machine learning in the cloud. And I have done that enough that I felt that like, I could take the certification exam, because of how much I have worked with these technologies. I set up my Hey, I'm going to take this certification for Google Cloud, because I will want like recognition of really those that I have worked so much and build so many proof of concept in the cloud, that it was like, if I already know this, I can take that exam, right? Mm hmm. So the builder knows the motivation to take the courses because I have worked well, I had enough experience with it to warrant me taking the certification. It had nothing to do with what it'd be like career prospects or anything of those jobs that I had worked enough. And I thought I could do it. Yeah. So I mean, the failing part is just that. I don't know if you've ever taken any of the certification exam. So especially for Google Cloud, it's very tricky, right? It's like, you know, the answer, like, yes, multiple similar answers, right? Any of those assets could be right, where you have to pick the very right one, right? So I think we take those exams that like, I know the answer, but I didn't pick the right answer from certification. So I'm like, hey, you've worked so hard for this, you know it, you didn't get it right on the first try. That doesn't mean you give up right? You just keep trying to get so then that was the motivation to take the certification.

Kimmiko James  [9:58]  
Is that also your main motivation? To just, you know, take these online courses in general, because you know, a lot of people, you maybe you've seen these comments, too, I see them quite a bit for online courses, they just say like, Hey, can I get a job right after this? Or, you know, do I have enough knowledge in this because a lot of people, they take the course and they just stop, or they just list it on their LinkedIn. And they just stop. And I'm guilty of doing this. So I'm not judging you. But, yeah. Is your motivation to take these courses and the certifications to just learn as much as you can and then apply it to projects? Or is it just like a mix of both? Maybe?

Emmanuel Acheampong  [10:39]  
Yeah, so I think, again, I think I was very fortunate to get introduced to, like online learning, with the perspective of is going to help my education, right, because I think a lot of people they get introduced to online learning with, I need to get a job, I need to get this skill to get a job, right. But I was very fortunate that because I was in school, and I got introduced to it, it wasn't because I was taking the courses to get a job was like I'm taking these courses, because I want to know what I'm doing. And this is a personal belief I have now so anything that if you do enough of you form certain habits to them that like they become very unconscious, right? Like, it's just like, if you take enough online courses, like you get to see certain trends you get like right now, I feel like I can determine how good the course would be by listening to the instructor for a few couple of minutes, right? I can just like because of how many courses that I've taken. So the motivation to complete those courses began with the desire to learn, right. But I think when you do it long enough, it gets to a point where it's just like, it becomes intense, you get part of your lifestyle, if that makes sense. Right. So I might even mentioned this earlier, but like, at the beginning, I had a timetable for my online courses. And that has never actually led me I think the potential of online courses that I want to take, even though like there's been jobs and everything, like I still keep that timetable, because I'm used to it, right? So it's become more of a habit now. Right? So complete is no longer vague. tasking is more like part of my habitual things that I do. And that makes sense.

Kimmiko James  [12:24]  
That makes sense, because that's something I've been working on, and probably other people too, in terms of just like, learning how to treat certain things as not as a chore, you know, because that's basically what you need, right? Like you don't, you shouldn't feel like you have to take this online course. And you have to complete it by this and that. Because if you really want to learn, like, if you're really passionate about learning, you should just learn how to make it a part of your lifestyle without feeling pressured to make it a to do list item. If that makes sense.

Emmanuel Acheampong  [12:54]  
It makes a lot of sense, right? Well, I think the point is that if you do it enough, right, you become part of your lifestyle, or at the beginning is going to be hard. So you need to have like passion for it. Right? But sometimes, like if you do it like enough, like it's like people who trade right? Or people who do powerlifting and other very interesting other green beginning, it's very hard, right? Like, trying to live all that metal is hard, but it becomes an integral part of your life that become lifted without thinking.

Kimmiko James  [13:25]  
That makes sense. I mean, it applies to a lot of software engineering concepts.

Emmanuel Acheampong  [13:30]  
Exactly. So that's that's how I see it. Right. But it's hard. I'm not gonna, it's it takes a lot of discipline, especially at the beginning. Yeah. So I'm not gonna downplay that. So you're absolutely right.

Kimmiko James  [13:42]  
Just a little bit of an advice section, I suppose. Because he kind of did give a little bit advice before. So would you have any, I guess, online course recommendations for people looking to get started learning to code or to learn, in general, especially for students that might not be able to afford certain courses or certifications, or if their school isn't associated with any of those sites, because some schools are associated with Coursera, or some schools are associated with LinkedIn learning. But yeah, for the, for the people that don't go to the schools, I guess, what advice could you offer?

Emmanuel Acheampong  [14:17]  
So I think there's a plethora of online learning platforms, including YouTube. And yeah, and I think that I think that finding the right course, for you at the beginning is very difficult, right. And I think that's the main reason is, my personal opinion, I think, is one of the main reasons people don't really complete the courses they take, because they get advice of like, hey, this particular course is good. But then they take it and it has nothing. It doesn't match the skill set or the experience. Right. So there's a lot of trial and error in the beginning, right. And so that's I don't want to tell people which causes they should take because just because the court helped me, doesn't mean I won't necessarily help them. Right. So but What I would say is that Coursera has financial assistance, especially for the college students, even if your college isn't affiliated with Coursera. If there's a course that you like, there's probably a financial aid link. Every course I took on Coursera in college, even though my school was not affiliated, Coursera was free. So the 100 plus courses, I never paid a cent. Also, there are always like, these challenges that are always around, right? I think Microsoft and Udacity did one Udacity is always doing a lot of these challenges, as well. So I think just as a lot of research, right, and there's a lot of research involved to find courses that are, hopefully somebody solves this problem, but like to find courses that are very tailored to you. Yeah, but don't write on YouTube, because I think that there's a lot of things that you can learn on YouTube, even though you won't get like a certificate at the end. You should definitely check that out.

Kimmiko James  [15:58]  
Yeah, YouTube is definitely great. It's taught me a lot of things when I get stuck on projects. And I just find a YouTube tutorial, kind of skip through some sections and get what I mean, so Exactly, exactly. It's pretty great. Yeah, so something I'm excited to talk about. Because just seeing your projects on LinkedIn, I want to talk about your interest in artificial intelligence and machine learning and how you've kept the drive to keep learning it despite the difficulty surrounding it, because it involves extensive knowledge and math, specifically in calculus in statistics, a lot of programming skills necessary data analysis, it cetera, like it's just a lot. So what sparked that interest in this specific field of CES? And how are you motivated to keep going,

Emmanuel Acheampong  [16:39]  
I was very fortunate. In my sophomore year, I got an internship in New York, when era, and then I was matched up with a startup called pcapng. I have awesome managers, like my manager, just like To this day, I'm still connected to them. And they are the greatest of all time. But one thing that one of my managers let me do was that, instead of doing like a traditional software engineering activity, he gave me this project that was very stemmed in my data, right? So instead of like building a website, he was like, no manual focus on this data project. And at that time, it was around 2016, I had no idea of like, the AI field, or machine learning or data, analytics or anything. I have no idea about it. Right. So he, he introduced me to Jupyter Notebooks to these into pandas. And like, we were trying to analyze data for the company. And it was really fun for me, right? It was, it was very exploratory. It wasn't like, this is how it's done. It has to be a bingo analysis of, of one has to be a big O of n or whatever, whatever it was, it wasn't like that. It was like, Hey, this is data, you're trying to gain insights. For me, there was no memorization. So I really enjoyed it for that project. And that's what sparked that interest in this part of like, software engineering for me right. After that, that's when I started taking these online courses, and then learning more about what machine learning is, and then trying to grow from there. But my main thought was God project that I worked on during my internship, where I realized that wow, like this, this other side of like software engineer, like cuz like, whenever people think of CSS just, I felt engineering, like, web design back end. And I didn't know that like things like the data stuff was available. So that's what sparked my interest. So I think keeping it going, right, I think I shared this post before one of my mentors told me that like, as I was explaining, like, data science, machine learning, like to work on projects that interesting. And I think I at the time, I was hard at the beginning. But I think what it helped me to do was that, like you said, like data science, and it can get heavy sometimes, right? Especially the theoretical part. But when you work on things that actually interests you, that makes it easier, right? So let's say you're interested in I don't know, NASCAR, right? And you're trying to learn data science, and then find the balance of how data science is being used in NASCAR. That to me, but I believe that it celebrates your learning, right? So it's like, you're you're doing something that you enjoy, right? Let's say you're interested in the TV series, I don't know Game of Thrones. And then you want to do data science and you're doing a project in data science that involves Game of Thrones, it becomes less of a chore and more of a hobby, right? Something that's what has kept me interested in the field because instead of like, memorize because I suck at memorizing things, right? I suck at like, the very type of things. I'm a very, like, I have to do things in order to learn and move toward information and dispute items. I can't do that. I suck at that. But because the thing that I work on are things that I'm actually interested in when there's other I'm trying to pick up a new algorithm or stuff, because I'm applying it to things that I'm interested in is less of a chore and is more of a hobby, right. So let me give an example of a from a personal example. So I wanted to learn like natural language processing, right. And it has so many things that you can do in natural language processing, translation, summarization, tuning other things, right. And like, in order to learn all those things, instead of going to the theory, I was like, I'm gonna work on a project that I care about. And then I realized, I love reading, I love literature. And I love hip hop. So I'm going to try to learn natural language processing by building a project that merged those two interests. So working on this project, I got the chance to, again, review some of my favorite books, and also to listen to a whole lot of albums, right? So it was less of a Charles, like I'm doing a hobby, and I'm learning from it. So that's what keeps my interest, right. So every time there's something new in the field that I want to learn, instead of like, going straight to like, the theory part, I always try to relate it to something that I'm interested in so that it makes it easier to absorb. And then I can lend a theory afterwards.

Kimmiko James  [21:15]  
That is honestly a very unique way of thinking about it. Like I, I didn't even think of it that way, because I love online courses as you do. But I think the problem with some of them is they just throw all this theory at you. And sometimes they walk you through a project, but it's not even like a super in depth one. And even if it is you're not taking the time to learn the concepts. And so that's really, that's really awesome. Like, I kind of wanted to jump into that into personal projects, then because I think they're really cool. And I've seen a few of them on LinkedIn. Two of them so far have been really interesting to me, even though most of them are interesting. Okay, like, I think the two that you released last year, I think it was it was either the end of last year in terms of December, or it was started this year, January, January, because it was just a summary slash personal data analysis on your personal LinkedIn slash Coursera. profile. Ah, yeah, so those are pretty cool. I don't even know where I'm going with this. I'm just, I'm just naming. But you're gonna cervia on Google Assistant, I don't own a Google Assistant. But it's cool that you created a trivia game of West West African culture. And you also created and feel free to correct me. How do you pronounce this aka n? conference? A con translate? So what is it translate English to

Emmanuel Acheampong  [22:49]  
cheap? Does a few words in English too cheap?

Kimmiko James  [22:54] 
Oh, my God, he's perfect. So cool. And I think the most recent ones that really caught my eye because, you know, I don't always admit it. But I am a fan of murder mystery stores and shows. So when I saw you were working on the Ted Bundy project, which involved NLP natural language processing, I was like, wow, that's genuinely something I'm interested in. And I don't have any, you know, experience in ai, ai or ml, but I kind of want to learn how to do what he's doing. Or you know, are going with it. I think I'm just fan girling over the projects, but they're, but they're pretty great. Something, something I really appreciated about a good chunk of your projects is that most of them are related to black and African culture. Because I remember back in February, during African American History Month, you put out quite a few of those, too. So what inspired you to want to create projects based on black and African culture?

Emmanuel Acheampong  [23:53]  
So, again, like I said, I think one of the best ways to actually learn is to apply things that you're learning to things that you're interested in, right. And I am an avid lover of black history, I think, like I read a lot of it right. And I'm not going to get into in depth of it. Right here. But I do read a little back of shoe I'm certainly inspired by it. And I, I hope, yeah. Let's let's get into that fun. Well, the thing again, was that one of them trying to learn something, I try to integrate it to something that I'm interested in. And since I'm interested in black history, now I want to learn things like I wanted to learn about voice chat bots, right voice AI chatbots. So I wanted to build things on the Google Assistant. And so in order to do that, I'll have to build things that I was interested in that would allow me to complete the project, right? Because if you told me to, I don't know, build a voice template for I don't know, something in accountant like accountant is great, but like, I'm dad interested in accounting to put all that energy into it, right. So those are things that are I'm interested in and I think one thing that you can we can notice that like, even though like the machine learning field is growing really large, right, very few people focus on these types of projects, right? And it's a unique way to stand out. Right? Like, like, everybody's building, like, if everybody's building the same kaggle projects, everybody's doing the same data sets. Every single machine learning resume, if you're hiring manager for machine learning resume, you probably have an iris classify a data set on it, right. And so building these projects is also a way for me to differentiate myself. Right. And I think that especially students like us, since very few people focus on these types of issues. It's a unique opportunity for us to build solutions for ourselves that also make us stand up. Right? And I think that's a that's a it's another sort of as the two tone answer. I build them because I'm very interested in them. And also because their unique way for me to stand out in the field of AI and ml, right. So everybody's building a COVID data analytics. Everybody is building, like, I built this thing that nobody's even looking at. So and it's not because it's so crazy, unique opportunity to like, diversify, not diversify was the word to make yourself stand out. Right? So that's it. That's, that's what I see on that.

Kimmiko James  [26:33]  
Yeah, we kind of touched up on this, um, how, like, you can get out of tutorial, hell. I've been there. But we want

Emmanuel Acheampong  [26:43]  
to know, is your experience into tutorial hell? Oh,

Kimmiko James  [26:48]  
yeah, learning to code and you've seen it, you've seen the advertisements of these websites, and Coursera is guilty of it, too. All of them are just like, learn this and blank days or blank weeks, or learn how to build this and blank amount of time. And I bought into that of learning how to build a website learning, I build a website and like, maybe one to two hours, and then I think I'm not an expert, but just I know what the hell I'm doing. But when I try to build it on my own, I'm like, Oh, I don't, I don't know what I just did. So I was in that loop for quite some time. I'm not proud to be in that loop. But I'm slowly getting out of it and trying to adopt your mindset of just building stuff you like, instead of just relying on the courses to teach everything for you, because they, they only touch the surface of what you need to know, for real world projects. That's just my experience. Maybe about a year and a half ago, again, I'm like easing my way out of it.

Emmanuel Acheampong  [27:51]  
Instead, yeah, so I think that like another perspective would be that I think a lot of us, especially young people, like me, one worked at a particular companies, right, we want to learn the software engineering title. And so it's not because we were not in it, because we love the technologies per se, right? But it's because you want to get a job at XYZ, right? And so people are trying to learn these things, so they can pass the technical interviews, instead of like learning them because the love it right. I think that's how come a lot of people get stuck in the tutorial hell, like you

Kimmiko James  [28:25]  
said, Yeah. That's a good point. That's also a reason why I was just trying to learn all these different technologies. Yeah, like, you know, I know, React HTML, CSS, but also I know Python, but you can say that's on your resume. But But if you're not using it for anything, then you don't really know it. So solid advice. I think certain people need to know what tutorial Hell is because I didn't know what it was, for a long time, I thought I was absorbing this information. But in reality, it was just kind of like you were saying, just trying to memorize something you don't really need to memorize if you put it into practice. So and I think something that I've been interested in, which I'll probably ask future guests, if they're in AI and ml data science is, what are your thoughts on the idea of needing a PhD to be able to work in this field of artificial intelligence, machine learning and data science? Because you don't have a PhD here? You're probably around my age, but you're pretty skilled in this field and you you've even had jobs in it. And Morgan Stanley and Booz Booz Allen Hamilton. So what are your thoughts on that? Yeah.

Emmanuel Acheampong  [29:39]  
So okay, again, I graduated from college with a math degree as well. So my answer might be biased a bit. Right. Okay. I yeah, I just want to make a claim. So I think that whenever we look at machine learning and AI, like we can look at it from multiple angles, because I think we always look at it from one particular angle. So let me put it this way, like the field researchers, right? We people who are going to be able to study things and develop new algorithms for the field, right? And that's what like, I think the PhD comes in. So I'm not, I am not gonna say I'm anti PhD, because I'm actually pro PhD, because we need people who are going to generate new information for the field. Right? And that's what a PhD is important as well, right? It's just like, even in software engineering, right? Even though some people say things like, you don't need a CS degree to be a software engineer, which is true. But the field also needs researchers who are going to come up with new technologies, right, not just people who are creating API's. And like, one of the new people who haven't come up with new algorithms, I don't know, for cybersecurity or some stuff like that, right? Like, there's a need for all talents and all skill set, right. So let's even get into machine learning and you don't have a PhD. Now, that means that there's a specific field that in ML AI that you can fill, but you might not be able to fill in a researcher role. Right? So, for example, I had a friend who was like, hey, Google has stumped what your requirements for computer science degree, yeah, blah, blah. And so went to check the Google website, right. And there was some researchers that required PhDs, like it was part of the requirements. So I think that this does a thing, right. Whatever skill sets you have, as long as you have interest in it, there's a part of, there's a part of the machine learning field that you can feel when PhDs are needed, right? We want people to generate new information, to write papers to advance the field. But there are people who also don't have PhDs who are visualizing the knowledge that these people have built to build applications, right? So for example, I don't have a PhD, at least not yet, right? But I've used these natural language processing algorithms that these PhDs built to build my projects. Right? So that's, that's the thing. There's a need for all talent, right? Like you, because I know some people say things like, you don't need a PhD to do machine learning, which is true, but like, what, if you want to be a research and machine learning? Right, you would still need that formal education. So that's what I think that if you have interest in it, there's a there's a play. there's a there's a season in machine learning table for you. But there's also sees other machine learning table for PhDs as well. So all talent is welcome. I think.

Kimmiko James  [32:44]  
Okay, that's that's a good way of distinguishing it. Because, yeah, this field gets pretty deep and broad sometimes. Yeah, since that, I've heard the same thing. You said people saying, it's a waste of time to get a PhD in CS, or you don't need a PhD in blank to do this and CSR engineering. But yeah, like you just described, depending on what you're trying to do, and the work you're trying to do, then that PhD does matter. So yeah, thank you for that.

Emmanuel Acheampong  [33:13]  
Yeah. Like if you're trying to come up with a new algorithm for the community, right? Light, a PhD is probably important, right? But if you're just like, Hey, I just want to use what is already there to build things. Like, let me be, so it's just about? Yeah. So I think that the the thing people talk about PhD is, I don't think it's fair, right? Like to say that, like getting a PhD is a waste of time, because you don't know what people's goals and ambitions are. Right? That's it?

Kimmiko James  [33:43]  
Would you have any general advice for people that are intimidated to get started in this field, especially black technologists that might not come from a traditional background advice for anyone that's intimidated to get started in the field of artificial intelligence or machine learning, because a lot of people, myself included, sometimes it's just, you get intimidated by the skills you think you need to have. And then you see all these different libraries, and it gets intimidating to the point where you don't even want to try it. See if you have any advice for people like that.

Emmanuel Acheampong  [34:19]  
Yeah, so let me give my story. Like related to myself, so whenever I started learning, like deep learning, right, I was intimidated as well. There was a lot of terms, there's a lot of times, and especially the researchers, I mean, they assume that everybody is as smart as they are or something like that. So they just seem like all this information. Right? So what what I ended up doing was that instead of looking at the product picture, right, and like listening to what everybody was saying about deep learning and what it can be used for, and all these algorithms are like, No, I'm gonna focus on one single thing, right. And at the time, it was like neural networks. So What a neuron was, I think I've heard a post about it. So strength was a motor neuron was. And then again, I like math. So I realized a neuron is just a function, write a function put in an input, you get an output. Right? So that's how I started learning. Deep Learning, right? Instead of trying to focus on everything, I just took the math function, there's an input, and an output, okay? So that outputs could be wrong output could be a wrong output. And so what would you do if you have a wrong output, you compare it to the correct output, and try to adjust it, right? Like, that's how simple I started learning what deep learning was, right? Instead of trying to learn about just simple, hey, there's an input here, you give an output, if the predicted output is wrong, you try to adjust it to match it as close to the correct output as possible, right. And in order to do that, you use another function to do that, right? So I started with that, and then I kept adding knowledge on to it, right. And that's how I see it, though. That's how I regard it. Like, if you're learning something new, like, there's a tendency to learn like a funnel, and then like, soak everything in. Again, everybody learns differently. But for me, I tried to learn like a brick house, like a Lego house, right? I try to piece the information together, by starting with the thing that I have the most connection to, right. So let's say you like biology, you can start looking at neurons from that biological perspective, right? And start adding to it. It's just, that's that's how I learned it. So again, today, like I was just start with the basic unit of understanding and like start building on it. That's what I'll say. But one thing that I would also like to notice that I've used this analogy again, right? There's so many people who use Twitter, and they have no idea what the Twitter architectures like, right? Like, definitely, we're always training today, we have no idea what the databases of Twitter looks like how the networking of Twitter works, how those tests that you have on your phone shows up on your profile, like, we have no idea, but they still use it. And I think that a lot more people should use that approach when it comes to machine learning, right? Like you don't need to know everything about how machine learning works, to figure out how it relates to your life. Right. So like, recognizing that even Netflix is using some elements of machine learning to recommend movies, TV, or when you try to type in your Google email, it autocompletes certain things, like elements in it. And then things like, again, the NBA, they use machine learning and some fraud, and then realizing that it's not this scary thing is this thing that basically that has been also being implemented in our daily lives will take away that fear of it. Right. So like, That's what I think. So learn by building blocks, and also recognizing it's already existing in your world. So it's not a very scary thing to take away that fear

Kimmiko James  [38:10]  
is a pretty good answer. Because I'm sorry, I probably keep relating it to myself, I don't care I will take. But yeah, from my experience, it's, and probably a lot of people starting to learn how to code as well, it's, it's pretty difficult to not be overwhelmed by all the stuff you're learning, whether it is AI, machine learning, or web dev, or whatever you're trying to learn. You just feel like you have to drill everything. But once you really start to understand what the point of computer science and engineering is, it's just problem solving is just working towards a solution or goal to accomplish what you're trying to do in the first place. It's trying to put all the pieces together, not just throwing it all in and then that's that's your project and which I've made that mistake and I'm super grateful to be doing this internship because they, How do I explain this? You get a project in which that project is broken up into different JIRA tickets. JIRA is a management software to manage the tasks within your team or whatever. So yeah, at first, my last internship, same place, I assumed I was just going to do all this work in one day and the day after that, and so forth. But no, my mentor, sit me down and help me break down each task. So I can Yeah, exactly, because I'm not going to know everything immediately. And also, you're not supposed to have a PR pull request. That's that big. So definitely chunking is a good set of advice.

Emmanuel Acheampong  [39:56]  
Exactly. Yeah.

Kimmiko James  [39:57]  
I think the last thing I just want to add is if for anyone that wants to follow your work and all your cool projects, because they are pretty great. And you always make something randomly on, you share it on LinkedIn, where can they find this content? Should they follow you on LinkedIn? Or do you have any social media?

Emmanuel Acheampong  [40:17]  
Or? Oh, I think we should get connected on LinkedIn as my social media of choice. Yeah, please get connected. I'm open to all kinds of connections. And also GitHub, right? Cuz I don't know, I, I use GitHub a lot. Right? Like I'm always scrolling through projects. I'm always talking profiles on GitHub, just trying to figure out what other people are building. And so I spend a lot of time on GitHub, which I spend as much time on GitHub as some people spend on like Twitter, like I spoil a lot. Because I enjoy seeing people build the project. And like, that's also a source of inspiration for me to work up in my Oh, this person used this Python module to do this. I never knew that was even existed,

or this person created a new Python module. Okay, that's cool. So I spent a lot of time on GitHub too.

Kimmiko James  [41:08]  
Well, yeah. Thank you for joining me. I'm definitely happy to have learned a bit more about your background and how to break down projects and how to learn how to build projects based on what you're interested in. Just solid advice all around.

Emmanuel Acheampong  [41:22]  
Thank you for having me. This was a fun conversation.

Kimmiko James  [41:27]  
Thank you for listening, and I can't wait for you to join me in the next episode.

Emmanuel Acheampong

Emmanuel a creative technologist who loves learning, experimenting, building cool things and writing python code.

His interests include VR, AR, AI, cloud architecture, python, machine learning in the cloud, deep learning, and traditional cultures.