🎥 Click di image wey dey up for short video wey go show you how dis lesson dey work.
Welcome to dis course wey dey about classical machine learning for beginners! Whether you be person wey no sabi anything about dis topic, or you be ML expert wey wan refresh your mind for one area, we happy say you join us! We wan make dis place friendly for you to start your ML study, and we go happy to check, reply, and add your feedback.
🎥 Click di image wey dey up for video: MIT's John Guttag dey explain machine learning
Before you go start dis curriculum, you need make your computer ready to run notebooks for your system.
- Set up your machine with dis videos. Use di links wey dey here to learn how to install Python for your system and setup text editor for development.
- Learn Python. E good make you sabi small about Python, one programming language wey data scientists dey use and we go use am for dis course.
- Learn Node.js and JavaScript. We go still use JavaScript small for dis course when we dey build web apps, so you go need node and npm for your system, plus Visual Studio Code for Python and JavaScript development.
- Create GitHub account. Since you find us for GitHub, you fit don get account already, but if you never get, create one and fork dis curriculum make you use am. (No forget to give us star 😊)
- Check Scikit-learn. Make you sabi Scikit-learn, one ML library wey we go dey use for dis lessons.
Di word 'machine learning' na one of di popular and common words wey people dey use today. E get chance say you don hear dis word before if you sabi technology small, no matter di area wey you dey work. But how machine learning dey work na mystery for many people. For person wey dey start machine learning, e fit look like say e too much. So e good make we understand wetin machine learning be, and learn am step by step, with practical examples.
Google Trends dey show di recent 'hype curve' of di word 'machine learning'
We dey live for one world wey full with plenty mystery. Big scientists like Stephen Hawking, Albert Einstein, and others don use their life dey find better information wey go show di mystery of di world wey dey around us. Na di way human beings dey learn: pikin dey learn new things and dey understand di world wey dey around am as e dey grow.
Di brain and senses of pikin dey see wetin dey around am and dey learn di hidden patterns of life wey go help di pikin sabi how to use logic to understand di patterns wey e don learn. Di way human brain dey learn na wetin make humans be di most advanced living thing for dis world. Di way we dey learn and dey improve dey help us dey better as we dey grow. Dis learning ability and di way we dey change dey connect to one idea wey dem dey call brain plasticity. If we look am small, we fit see di way human brain dey learn and di idea of machine learning dey similar.
Di human brain dey see wetin dey happen for di real world, e dey process di information wey e see, e dey make sense of di information, and e dey act based on di situation. Na wetin we dey call intelligent behavior. When we program machine to act like say e get intelligent behavior, we dey call am artificial intelligence (AI).
Even though di words fit confuse person, machine learning (ML) na one important part of artificial intelligence. ML dey use special algorithms to find better information and hidden patterns from di data wey e see to help di process of making sense of di data.
One diagram wey dey show di relationship between AI, ML, deep learning, and data science. Infographic by Jen Looper wey e take inspiration from dis graphic
For dis curriculum, we go talk about di main ideas of machine learning wey beginner suppose sabi. We go focus on wetin we dey call 'classical machine learning' wey dey use Scikit-learn, one better library wey many students dey use to learn di basics. To understand di bigger ideas of artificial intelligence or deep learning, you need strong foundation for machine learning, and na wetin we wan give you here.
- di main ideas of machine learning
- di history of ML
- ML and fairness
- regression ML techniques
- classification ML techniques
- clustering ML techniques
- natural language processing ML techniques
- time series forecasting ML techniques
- reinforcement learning
- real-world ways to use ML
- deep learning
- neural networks
- AI
To make di learning easy, we no go talk about di hard parts of neural networks, 'deep learning' - di many-layered model-building wey dey use neural networks - and AI, we go talk about am for another curriculum. We go still bring data science curriculum later to focus on dat part of dis big field.
Machine learning, if we look am from system side, na di way we dey create systems wey fit learn hidden patterns from data to help make smart decisions.
Dis idea dey somehow connect to how human brain dey learn things based on di data wey e see from di world.
✅ Think small why business go wan use machine learning instead of creating system wey dey use fixed rules.
Machine learning dey everywhere now, e dey as common as di data wey dey flow for our society, wey dey come from our smart phones, connected devices, and other systems. Because of di big potential of di latest machine learning algorithms, researchers dey use am to solve big problems for different areas with better results.
You fit use machine learning for plenty things:
- To predict di chance of disease from patient medical history or reports.
- To use weather data predict wetin go happen for weather.
- To understand di meaning of text.
- To catch fake news to stop propaganda.
Finance, economics, earth science, space exploration, biomedical engineering, cognitive science, and even humanities don dey use machine learning to solve di hard problems wey dey their area.
Machine learning dey automate di process of finding patterns by getting better insights from real-world or generated data. E don show say e dey very useful for business, health, and financial areas, plus others.
For di future wey dey come, to sabi di basics of machine learning go dey important for people from any area because e don dey everywhere.
Draw, for paper or use online app like Excalidraw, wetin you understand about di difference between AI, ML, deep learning, and data science. Add some ideas of di kind problems wey each of dis techniques dey good to solve.
To learn more about how you fit work with ML algorithms for di cloud, follow dis Learning Path.
Take one Learning Path about di basics of ML.
Disclaimer:
Dis docu don dey translate wit AI translation service Co-op Translator. Even though we dey try make am accurate, abeg sabi say automatic translation fit get mistake or no correct well. Di original docu for im native language na di main correct source. For important information, e go beta make professional human translator check am. We no go fit take blame for any misunderstanding or wrong interpretation wey fit happen because you use dis translation.