Imagine teaching a child to recognize ripe mangoes, not by giving a list of rules, but by showing them many examples until they just
know. That’s how Machine Learning works.
In my AI journey, I realized ML is the engine that powers many of the AI systems we use daily - product recommendations on Jumia or Netflix, spam filters in Gmail, credit scoring systems in banks, and many more scenarios.
If you understood the building blocks of AI in my last post, this is the natural next step. Let’s break ML down together.
💡 What is Machine Learning, Really?
In traditional programming, you give the computer rules + data, and it gives you answers.
In Machine Learning, you give the computer data +/- answers, and it figures out the rules by itself 💪.
Example:
- Traditional programming: If marks ≥ 50 ⇒
Pass, else ⇒Fail - ML: Give the computer lots of past student scores with labels -
PassorFail, and it learns the pattern to decide for new students without hardcoding the rule.
📌 The Core Ingredients of Machine Learning
Data – The raw material. This could be numbers, images, text, or audio.
Features – The key attributes or variables in your data that help make predictions.
Model – The mathematical structure that learns patterns from data.
Training – Feeding data into the model so it can learn.
Evaluation – Testing the model to see how well it performs on new, unseen data.
📌 Types of Machine Learning
✍️ 1. Supervised Learning
Supervised Learning is learning from labelled data. Labelled data have both the questions and correct answers. The learning process is to be able to map a new question (not part of the training set) to an answer based on experience.
- Examples: Predicting house prices, detecting spam emails.
✍️ 2. Unsupervised Learning
Unsupervised Learning is finding patterns in data without labels. Unlabelled data have the questions but no answers. The learning process is to identify a group based on similarities.
- Examples: Grouping customers into segments, finding similar products.
✍️ 3. Reinforcement Learning
Reinforcement Learning is learning by trial and error and getting rewards (when correct) or penalties (when incorrect).
- Examples: Teaching a robot to walk, training AI to play chess.
📌 Popular ML Algorithms for Beginners
- Linear Regression: Predicts continuous values like house prices.
- Logistic Regression: Binary classification like spam or not spam.
- Decision Trees: Works for both classification and regression.
- Support Vector Machines (SVM): Finds boundaries to separate categories.
- K-Nearest Neighbors (KNN): Predicts based on closest data points.
- Naive Bayes: Great for text classification like spam detection.
📌 Practical Tools for Machine Learning
- Scikit-learn: Beginner-friendly tool that covers most ML basics.
- XGBoost: Great for credit scoring and churn prediction.
- LightGBM: Good for ranking and recommendations.
- CatBoost: Works well with categorical features.
- Statsmodels: Perfect for time series and statistical analysis.
📌 Example Workflow of a Machine Learning Project
- Define the problem: What are you trying to solve
- Collect data: Gather relevant and sufficient data
- Clean data: Handle missing values, duplicates and outliers (outliers refer to data points that are significantly different from the rest of the dataset like 500 in this set [2, 4, 7, 9, 500])
- Split data: Use 70 to 80% of the datasets for training and 20 to 30% of the datasets on testing the model
- Choose algorithm: Select an appropriate ML algorithm based on the problem and type of data
- Train model: Feed the training datasets to the model
- Test model: Use unseen data (the testing datasets) to evaluate the model
- Tune parameter: Improve model performance
- Deploy model: Integrate model into production
📌 Why ML Matters in the AI Journey
Machine Learning is the heart of modern AI. NLP, LLMs, computer vision all depend on ML to understand, predict, and improve over time.
For me, learning ML isn’t just about understanding algorithms. It’s about learning how to frame problems as data problems, analyze and process data, and then build intelligent systems from those insights.
📌 My Learning Path in ML
- Understanding the ML theory
- Learn Python libraries - NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
- Practice with Kaggle and HuggingFace datasets
- Build small real-world projects
- Deploy models in web apps.
Summary
Machine Learning isn’t magic, and just like learning to cook, you don’t have to start with a buffet. One small, simple recipe can get you started.
For practice, I built my first ML project on predicting mental health treatment:
🔗 Data Science Workflow: My First ML Project on Mental Health Treatment
Happy coding!
Top comments (2)
hii mam u r one of helpfull person who i ever i meet thanks for our conversation
Thank you so much. That truly means a lot. I am really glad I could help, and I am always here whenever you need guidance, support, or even just a conversation. Keep building yourself