Think of algorithms like recipes in a cookbook 🍳. Each recipe (algorithm) helps the computer learn a different skill — like predicting house prices, detecting spam emails, or even recognising your face in a photo!
ML algorithms are divided into 4 big families:
🟢 Supervised Learning — Learning with a teacher (labelled examples)
🔵 Unsupervised Learning — Learning without a teacher (find patterns yourself)
🟡 Reinforcement Learning — Learning by trial and reward (like training a dog 🐶)
🔴 Deep Learning — Super-powered learning using Brain-like networks 🧠
🗺️ The Big Map of ML Algorithms
┌─────────────────────────────────────────────────────────────────────────┐ │ MACHINE LEARNING UNIVERSE │ ├───────────────────┬─────────────────┬────────────────┬──────────────────┤ │ SUPERVISED │ UNSUPERVISED │ REINFORCEMENT │ DEEP LEARNING │ │ LEARNING │ LEARNING │ LEARNING │ │ ├───────────────────┼─────────────────┼────────────────┼──────────────────┤ │ • Linear │ • K-Means │ • Q-Learning │ • Neural Nets │ │ Regression │ Clustering │ • Deep Q-Net │ • CNNs │ │ • Logistic │ • DBSCAN │ (DQN) │ • RNNs / LSTMs │ │ Regression │ • PCA │ • PPO / A3C │ • Transformers │ │ • Decision Tree │ • Autoencoders │ • Multi-Agent │ • GANs │ │ • Random Forest │ • Anomaly Detect│ RL │ • Diffusion │ │ • SVM │ • t-SNE / UMAP │ │ Models │ │ • Naive Bayes │ │ │ • Vision │ │ • KNN │ │ │ Transformers │ │ • Gradient Boost │ │ │ │ │ • XGBoost │ │ │ │ │ • LightGBM │ │ │ │ │ • CatBoost │ │ │ │ │ • Time Series │ │ │ │ │ (ARIMA/Prophet) │ │ │ │ └───────────────────┴─────────────────┴────────────────┴──────────────────┘
🎓 Part 1: Supervised Learning Algorithms
Imagine a teacher sitting next to you, showing you 1,000 maths problems — each with the correct answer already written next to it. You study them, find patterns, and then you can solve NEW problems on your own!
That is Supervised Learning. The computer is given data (the maths problems) along with the correct answers (labels). It learns the pattern and can then predict answers for brand-new data.
Whenever you have data AND you know the correct answers for that data already. Examples: predicting house prices (you know past prices), spam detection (you know which emails are spam).
📈 1. Linear Regression
Imagine this: You notice that every time your age increases by 1 year, your pocket money increases by ₹50. That relationship — a straight-line pattern — is exactly what Linear Regression finds in data!
It draws the best possible straight line through a cloud of data points. Once the line is drawn, you can plug in any new value and get a prediction.
HOUSE SIZE → PRICE (Example)
500 sq ft → ₹30 Lakhs ●
750 sq ft → ₹45 Lakhs ●
1000 sq ft → ₹60 Lakhs ●
1250 sq ft → ₹75 Lakhs ●
← Best fit line drawn here!
NEW: 900 sq ft → Prediction: ~₹54 Lakhs ✅
🌍 Real-world uses:
- Predicting apartment rental prices in your city
- Forecasting electricity consumption for the next month
- Estimating delivery time based on distance
- Stock price trend estimation (as one input among many)
We will load house size data (in square feet) and known prices. We train a Linear Regression model, and then ask it: "If the house is 900 sq ft, what should its price be?" The model will give us a predicted number based on the pattern it learned.
# Step 1: Import the tools we need from sklearn.linear_model import LinearRegression import numpy as np # Step 2: Our training data — house sizes and known prices house_sizes = np.array([[500], [750], [1000], [1250], [1500]]) prices = np.array([30, 45, 60, 75, 90]) # in Lakhs # Step 3: Create and train the model model = LinearRegression() model.fit(house_sizes, prices) # Step 4: Predict the price of a 900 sq ft house prediction = model.predict([[900]]) print(f"Predicted price: ₹{prediction[0]:.1f} Lakhs") # Output → Predicted price: ₹54.0 Lakhs ✅
✉️ 2. Logistic Regression
Despite having the word "Regression" in its name, Logistic Regression is used for Classification — meaning, it answers YES or NO questions.
Think of it like a strict post office guard 📮. Every email that arrives, the guard checks it and stamps either "SPAM" or "NOT SPAM". That decision process — is this or is this not — is Logistic Regression.
EMAIL ARRIVES
│
▼
┌─────────────────────────────────────┐
│ Logistic Regression checks: │
│ • Has "FREE MONEY"? → +high │
│ • Has "Click here NOW!"? → +high │
│ • From known sender? → -high │
│ • Has suspicious link? → +high │
└─────────────────────────────────────┘
│
▼
Probability Score: 0.93 → SPAM! 🚫
Probability Score: 0.07 → NOT Spam ✅
🌍 Real-world uses :
- Bank fraud detection (is this transaction fraud or not?)
- Medical diagnosis (does this scan show cancer or not?)
- Loan approval (should we approve this loan?)
- Login security (is this login attempt real or a bot?)
We feed the model examples of emails (represented as numbers) and tell it which ones were spam. It learns the pattern. Then we give it a new email and it tells us the probability it's spam.
from sklearn.linear_model import LogisticRegression import numpy as np # Features: [has_free_money_word, has_suspicious_link, known_sender] X = np.array([ [1, 1, 0], # Spam example [0, 0, 1], # Not spam [1, 0, 0], # Spam [0, 0, 1], # Not spam ]) y = [1, 0, 1, 0] # 1 = Spam, 0 = Not Spam model = LogisticRegression() model.fit(X, y) # New email: has free money word, no suspicious link, unknown sender new_email = [[1, 0, 0]] probability = model.predict_proba(new_email)[0][1] print(f"Spam probability: {probability:.0%}") # Output → Spam probability: 75% 🚨
🌳 3. Decision Tree
A Decision Tree is like playing the game "20 Questions" 🎮. You ask one question at a time, and based on the YES/NO answer, you go down a different path until you arrive at the final answer.
Should I go outside today?
│
┌─────────┴─────────┐
│ │
Is it raining? No rain!
│ │
YES Is it hot?
│ ┌───┴───┐
Stay Home 🏠 YES NO
│ │
Drink water! Go play! 🏃
Stay in shade ⛱️
In ML, the computer automatically builds this tree of questions from your training data. It figures out which questions (features) are most useful to ask first.
🌍 Real-world uses :
- Medical symptom checkers (which disease does this patient have?)
- Customer churn prediction (will this customer leave our app?)
- HR systems (should we shortlist this job applicant?)
If you let a Decision Tree grow too big, it memorises your training data perfectly but fails on new data. This is called Overfitting. Solution? Use Random Forest (next algorithm)! 🌲🌲🌲
🌲 4. Random Forest
Imagine instead of asking one friend for advice, you ask 100 different friends — each with slightly different opinions. Then you take a majority vote. That crowd wisdom is much more reliable than any one person. That's Random Forest!
Random Forest builds hundreds of Decision Trees, each trained on a slightly different random portion of your data. Then it combines all their answers to make one final, much more accurate prediction.
Your Data
│
├── Tree 1 (random sample) → Prediction: SPAM
├── Tree 2 (random sample) → Prediction: SPAM
├── Tree 3 (random sample) → Prediction: NOT SPAM
├── Tree 4 (random sample) → Prediction: SPAM
└── Tree 5 (random sample) → Prediction: SPAM
MAJORITY VOTE → 4 say SPAM → FINAL ANSWER: SPAM 🚫
🌍 Real-world uses :
- Fraud detection in banking systems
- Loan approval scoring
- Disease prediction in hospitals
- Product recommendation on e-commerce sites
We create a Random Forest that looks at patient data (age, blood pressure, cholesterol) and predicts if they have heart disease or not. The forest votes, and the majority wins!
from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.datasets import load_breast_cancer # Load a real medical dataset (breast cancer detection) data = load_breast_cancer() X, y = data.data, data.target # Split data: 80% for training, 20% for testing X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Create a forest of 100 trees forest = RandomForestClassifier(n_estimators=100, random_state=42) forest.fit(X_train, y_train) # How accurate is our forest? accuracy = forest.score(X_test, y_test) print(f"Forest Accuracy: {accuracy:.1%}") # Output → Forest Accuracy: 96.5% 🎯
🏋️ 5. Support Vector Machine (SVM)
Imagine two groups of coloured balls on a table — red balls and blue balls — all mixed up. You want to draw a line that perfectly separates them. SVM finds the best possible line (or in higher dimensions, a hyperplane) that keeps the two groups as far apart from each other as possible.
🔴 🔴 🔴 🔵 🔵 🔵
🔴 🔴 🔵 🔵
🔴 | 🔵
| ← Best Boundary Line (maximises gap)
MARGIN│MARGIN
|
Red Group | Blue Group
The trick? SVM tries to make the gap (margin) as wide as possible around that boundary. A wider margin means the model is more confident and generalises better to new data.
🌍 Real-world uses :
- Face recognition in security systems
- Handwriting recognition (which letter did you write?)
- DNA sequencing and cancer classification in bioinformatics
- Text categorisation (news: sports or politics?)
📬 6. Naive Bayes
Naive Bayes uses basic probability maths (chance / likelihood) to classify things. It is called "Naive" because it assumes all features are independent of each other — which is a simplification, but it works surprisingly well!
Think of it like this: If you see dark clouds ☁️, you might think it will rain. If you also feel cool air 💨, the probability of rain goes up more. Each clue independently adds to your final probability estimate. That's Naive Bayes!
🌍 Real-world uses :
- Email spam filters (Gmail still uses it!)
- Sentiment analysis ("Is this review positive or negative?")
- News article categorisation
- Medical diagnosis assistance
👫 7. K-Nearest Neighbours (KNN)
Have you ever heard: "Show me your friends, and I'll tell you who you are"? That's exactly how KNN works!
When a new data point arrives, KNN looks at its K nearest neighbours in the existing data and assigns it the category that the majority of those neighbours belong to.
K=3 example: Who are the 3 closest neighbours?
🔴 🔴 🔵 🔵
🔴 ← ❓ NEW POINT
(3 nearest are all 🔴)
Answer: The new point is most likely → 🔴 RED!
🌍 Real-world uses :
- Netflix / YouTube recommendation ("Users similar to you also liked…")
- Credit scoring based on similar customer profiles
- Anomaly detection in network security
KNN gets very slow with large datasets because it has to measure distance to every single point. Not ideal when you have millions of records!
🚀 8. Gradient Boosting (XGBoost, LightGBM, CatBoost)
Gradient Boosting is like a team of students doing a relay race, but with a twist — each new student focuses only on correcting the mistakes of the previous student.
Round 1: Student A makes some predictions. Some are wrong.
Round 2: Student B looks at where A went wrong, and tries to fix those specific errors.
Round 3: Student C fixes B's remaining errors…
After many rounds, the combined team is incredibly accurate!
Model 1: Prediction = 50 (Actual = 80, Error = 30) Model 2: Adds = +25 (Reduces error to 5) Model 3: Adds = +4 (Reduces error to 1) Model 4: Adds = +0.8 (Almost perfect!) ────────────────────────────────────── Final: 50 + 25 + 4 + 0.8 = 79.8 ✅ (Very close to 80!)
XGBoost, LightGBM, and CatBoost are three famous implementations of Gradient Boosting. They are the kings of structured/tabular data (think spreadsheets and databases) and win most Kaggle machine learning competitions!
🌍 Real-world uses :
- Fraud detection at Visa, Mastercard
- Click-through rate prediction in advertising
- Risk scoring in insurance companies
- Supply chain demand forecasting
We use XGBoost (the most popular Gradient Boosting library) to predict whether a customer will buy a product based on their age, income, and browsing time. It builds models in rounds, each one learning from the mistakes of the one before it.
import xgboost as xgb from sklearn.model_selection import train_test_split import numpy as np # Customer data: [age, income (lakhs), browsing_time_mins] X = np.array([ [25, 3, 45], [35, 8, 120], [45, 15, 30], [22, 2, 90], [50, 20, 20], [30, 6, 80], [28, 4, 60], [40, 12, 15], [33, 7, 100] ]) y = np.array([0, 1, 0, 1, 0, 1, 1, 0, 1]) # 1=Will Buy, 0=Won't Buy X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # Create and train XGBoost model (200 rounds of boosting) model = xgb.XGBClassifier(n_estimators=200, learning_rate=0.1, random_state=42) model.fit(X_train, y_train) accuracy = model.score(X_test, y_test) print(f"XGBoost Accuracy: {accuracy:.1%}")
📅 9. Time Series Forecasting (ARIMA, Prophet)
Some data comes in a time-ordered sequence — daily stock prices, monthly sales, hourly website traffic. To predict the future values in these sequences, we use special Time Series algorithms.
ARIMA is the classic mathematical approach (great for smooth, linear trends).
Prophet (built by Meta/Facebook still widely used) handles
seasonality, holidays, and weird spikes automatically. Perfect for business forecasting!
🌍 Real-world uses :
- Predicting sales for Diwali season planning 🪔
- Forecasting electricity demand for power grid management
- Predicting hospital patient load for staff scheduling
- Financial market trend prediction
🔍 Part 2: Unsupervised Learning Algorithms
Now, what if you have a mountain of data but no labels, no answers? You just have raw data and want to discover hidden patterns inside it. That's Unsupervised Learning!
Think of it like organising a massive pile of LEGO bricks 🧱. No one tells you the rules — you naturally group similar shapes and colours together. You discover the structure yourself!
🎯 10. K-Means Clustering
K-Means groups your data into K clusters (groups), where K is a number you choose. It places K random centre points (centroids), then pulls each data point into the cluster of the nearest centre. It then moves the centre to the middle of its group. Repeat. Repeat. Repeat — until everything stabilises!
BEFORE K-Means (K=3): AFTER K-Means: ● ● ● ○ ○ △ △ △ 🔴 🔴 🔴 🔵 🔵 🟢 🟢 🟢 ● ● ○ ○ △ △ △ ● 🔴 🔴 🔵 🔵 🟢 🟢 🟢 🔴 (All same colour, no groups) (3 distinct groups found!) ✅
🌍 Real-world uses :
- Customer segmentation (VIP buyers vs budget buyers vs window shoppers)
- Image compression (grouping similar pixel colours)
- Document grouping (clustering news articles by topic)
- Anomaly detection (data points far from any cluster = suspicious!)
We give K-Means a list of customers with their spending score and age. We ask it to find 3 natural customer groups — without us telling it what those groups should be. The algorithm figures it out on its own!
from sklearn.cluster import KMeans import numpy as np # Customer data: [age, monthly_spending_score] customers = np.array([ [25, 80], [27, 70], [30, 85], # Young high spenders [45, 30], [50, 20], [55, 35], # Middle-aged low spenders [35, 50], [38, 55], [40, 52], # Middle group ]) # Find 3 natural customer groups kmeans = KMeans(n_clusters=3, random_state=42) kmeans.fit(customers) # See which group each customer was assigned to print("Customer Groups:", kmeans.labels_) # Output → [0, 0, 0, 1, 1, 1, 2, 2, 2] # Group 0 = Young high spenders 🛍️ # Group 1 = Middle-aged low spenders 💼 # Group 2 = Middle group 🤷
📉 11. Principal Component Analysis (PCA)
Imagine you have a 3D object — a ball. You can cast a shadow of it on the wall to get a 2D circle. You lose one dimension, but the shadow still captures the most important shape information. That's what PCA does with data!
PCA reduces the number of features in your data while keeping the most important information. This makes models faster, reduces storage, and helps visualise data that originally had hundreds of columns.
🌍 Real-world uses:
- Compressing face recognition datasets
- Speed up model training on very wide datasets
- Visualising high-dimensional data in 2D charts
- Noise removal from sensor data
🚨 12. Anomaly Detection (Isolation Forest, One-Class SVM)
Think of a classroom where everyone is sitting quietly. Suddenly, one student starts dancing on their desk 💃. They're the anomaly — they stand out as different.
Isolation Forest works by randomly splitting data. Anomalies (unusual points) get isolated very quickly — they're easy to separate from the crowd. Normal points take many more splits to isolate.
🌍 Real-world uses :
- Credit card fraud detection (unusual transaction = anomaly)
- Server health monitoring (unusual CPU spike = anomaly)
- Quality control in manufacturing (defective products = anomaly)
- Cybersecurity intrusion detection
🎮 Part 3: Reinforcement Learning (RL)
This is the most exciting type of learning! 🎉 Imagine training a puppy 🐶. When it sits on command, you give it a treat (reward). When it chews your shoe, no treat (penalty). Over time, it learns to do things that get more treats!
Reinforcement Learning is exactly this. An AI agent takes actions in an environment, receives rewards or penalties, and over time learns the strategy (policy) that maximises total reward.
┌─────────────────────────────────────────────────┐ │ AGENT (AI Brain) ←──── Reward/Penalty ────┐ │ │ │ │ │ │ │ Takes Action │ │ │ ▼ │ │ │ ENVIRONMENT (Game/Robot/Market) │ │ │ │ │ │ │ │ Returns new State │ │ │ └────────────────────────────────────►┘ │ └─────────────────────────────────────────────────┘ This loop repeats millions of times → Agent gets smarter!
Key RL Algorithms :
- Q-Learning — The classic RL algorithm. Builds a table of "state → best action" values.
- Deep Q-Network (DQN) — Q-Learning + Neural Network. Used by DeepMind to master Atari games!
- PPO (Proximal Policy Optimisation) — OpenAI's algorithm. Powers ChatGPT's training (RLHF)!
- A3C (Asynchronous Advantage Actor-Critic) — Multiple agents learning in parallel.
🌍 Real-world uses :
- Self-driving cars learning to navigate traffic 🚗
- Robot arms learning to pick up objects in warehouses 🤖
- Game-playing AIs (AlphaGo, AlphaStar, OpenAI Five)
- Training Large Language Models (the RLHF in ChatGPT, Claude!)
- Trading bots in financial markets
🧠 Part 4: Deep Learning Algorithms
Deep Learning is the rockstar of modern AI 🎸. It's inspired by the human brain — millions of tiny connected neurons working together. These algorithms excel at images, speech, text, and video — tasks that were nearly impossible for older algorithms.
🧠 13. Artificial Neural Networks (ANN)
Think of a Neural Network like a series of filters in a water purification plant 🏭. Raw water (raw data) goes in. Layer 1 removes big particles. Layer 2 removes bacteria. Layer 3 adds minerals. What comes out is clean, pure water (accurate predictions)!
INPUT LAYER HIDDEN LAYER 1 HIDDEN LAYER 2 OUTPUT LAYER (Raw Data) (Finds patterns) (Combines them) (Final Answer) ●──────────●──────────●──────────→ Result: 🐱 or 🐶? ●──────────●──────────●─────────/ ●──────────●──────────●────────/ ●──────────●──────────●───────/ (Each ● is a Neuron, lines are weights/strengths of connections)
🖼️ 14. Convolutional Neural Networks (CNN)
CNNs are the superheroes of image recognition 🦸. When you look at a photo, you don't analyse every single pixel at once — your eyes scan small patches first (is this a circle? Is this an edge?) and build up. CNNs do the same using convolutional filters — small windows that scan across an image looking for features like edges, shapes, and textures.
📷 Cat Photo
│
▼
┌──────────────────┐
│ Filter Layer 1: │ → Detects edges (fur outline)
│ Filter Layer 2: │ → Detects shapes (eyes, nose, ears)
│ Filter Layer 3: │ → Detects complex patterns (face structure)
└──────────────────┘
│
▼
"This is a CAT" 🐱 (96.7% confidence)
Popular CNN Architectures:
- ResNet — Uses "skip connections" to train very deep networks without vanishing gradients
- EfficientNet — Optimised for speed and accuracy balance, great for mobile devices
- Vision Transformer (ViT) — Applies Transformer architecture to images
🌍 Real-world uses :
- Face Unlock on your smartphone 📱
- Medical imaging — detecting cancer in X-rays, MRIs
- Self-driving car vision systems
- Quality inspection in factories (detecting defects)
- Instagram and Google Photos image search
🔄 15. Recurrent Neural Networks (RNN) & LSTMs
Normal neural networks process each input independently — they have no memory. But language, music, and stock prices are sequential — the current word depends on previous words! RNNs have memory — they pass information from previous steps to the next.
LSTM (Long Short-Term Memory) is an upgraded RNN that has a special "memory cell" — it can remember important things from far back in the sequence and forget unimportant things. It's like a student with a great notebook vs. one with a terrible memory!
🌍 Real-world uses :
- Language translation (before Transformers took over)
- Speech recognition systems
- Time-series forecasting (LSTMs are still strong here)
- Music generation
⚡ 16. Transformers & Large Language Models (LLMs)
This is the algorithm that changed the world 🌍. In 2017, Google researchers published a paper called "Attention Is All You Need" and introduced the Transformer architecture.
Transformers use a mechanism called Self-Attention — when processing a word, they look at all other words in the sentence simultaneously to understand context. This is why they understand language so much better than old models!
Sentence: "The bank near the river is flooded."
When processing "bank":
Transformer looks at ─── "river" (very relevant! River bank, not money bank)
─── "flooded" (confirms it's a river bank)
─── "The" (less important)
Self-Attention figures out: "bank" here means RIVER BANK! ✅
LLMs (Large Language Models) like GPT-4, Claude, Gemini, and Llama 3 are all built on the Transformer architecture, scaled up to billions of parameters and trained on trillions of words.
🌍 Real-world uses :
- Claude, ChatGPT — conversational AI assistants
- GitHub Copilot — writing code for you
- Google Search AI Overviews
- Real-time translation systems (DeepL, Google Translate)
- Document summarisation in legal, medical, finance sectors
🎨 17. Generative Adversarial Networks (GANs)
GANs involve two neural networks fighting each other in a competition 🥊 — like a forger and a detective:
- Generator (the forger) — tries to create fake images that look real
- Discriminator (the detective) — tries to catch the fakes
As they compete, the Generator gets better and better at making fakes, until eventually it can create photorealistic images from nothing!
🌍 Real-world uses :
- Generating photorealistic human faces (thispersondoesnotexist.com)
- Video game asset generation
- Medical data augmentation (generating synthetic patient data)
- DeepFakes (and the challenges they create)
🌊 18. Diffusion Models (Stable Diffusion, DALL·E, Midjourney)
Diffusion Models are the newest kings of image generation . They work by slowly adding noise (like static TV 📺) to a real image until it becomes pure random noise. Then the model learns to reverse this process — starting from random noise and gradually removing it to create a clean, beautiful image.
You type: "A peaceful Indian village at sunset, watercolour painting style"
The model: starts from random noise → gradually shapes pixels → beautiful artwork appears! 🎨
🌍 Real-world uses :
- Midjourney, Adobe Firefly, Stable Diffusion — AI art generation
- Drug molecule design in pharmaceutical research
- Fashion design — generating new clothing designs
- Architecture — generating building concept art
📊 Quick Comparison: Which Algorithm Should You Use?
| Problem Type | Best Algorithm(s) | Why? |
|---|---|---|
| Predict a number (price, temp) | Linear Regression, XGBoost | Simple, fast, interpretable |
| Yes/No classification | Logistic Regression, Random Forest | Well-tested, explainable |
| Tabular data (spreadsheets) | XGBoost, LightGBM, CatBoost | Proven best on structured data |
| Image recognition | CNN (EfficientNet, ViT) | Built for spatial data |
| Text / Language tasks | Transformers / LLMs | Dominates NLP |
| Find hidden groups | K-Means, DBSCAN | Great for unsupervised clustering |
| Time series / forecasting | Prophet, LSTM, XGBoost | Handles temporal patterns well |
| Detect fraud / anomalies | Isolation Forest, Autoencoders | Finds outliers effectively |
| Generate images / art | Diffusion Models, GANs | State-of-the-art generation |
| Game AI / Robotics | PPO, DQN (Reinforcement Learning) | Learn by trial and reward |
🐍 Python — The language of ML (free, always)
📦 scikit-learn — All classic algorithms in one library
🔥 PyTorch — Best for deep learning research and production
🤗 Hugging Face — Ready-made LLMs and Transformers
📓 Google Colab — Free GPU in your browser, no setup needed!
🏆 Kaggle — Practice on real datasets, join competitions
❌ Don't jump straight to Deep Learning before understanding basics
❌ Don't ignore data cleaning — garbage in, garbage out!
❌ Don't skip model evaluation — accuracy alone is not enough
❌ Don't memorise algorithms — understand why each one works
❌ Don't just read — CODE every day, even 30 minutes counts!
🎯 Quick Recap — All Algorithms at a Glance
- 📈 Linear Regression — Predicts numbers using a straight line
- ✉️ Logistic Regression — Yes/No classification using probabilities
- 🌳 Decision Tree — Asks questions to reach a decision
- 🌲 Random Forest — 100s of trees voting together for accuracy
- 🏋️ SVM — Finds the best boundary between groups
- 📬 Naive Bayes — Uses probability to classify things quickly
- 👫 KNN — "You are like your neighbours"
- 🚀 XGBoost / LightGBM / CatBoost — Error-correcting ensemble champions
- 📅 ARIMA / Prophet — Forecasting future values in time sequences
- 🎯 K-Means — Automatically finds groups in unlabelled data
- 📉 PCA — Shrinks data size while keeping important patterns
- 🚨 Isolation Forest — Spots the unusual and suspicious
- 🎮 Q-Learning / PPO / DQN — Learning by reward and punishment
- 🧠 ANN — Brain-inspired layered learning
- 🖼️ CNN — Sees and understands images like humans
- 🔄 RNN / LSTM — Remembers sequences over time
- ⚡ Transformers / LLMs — Understands and generates human language
- 🎨 GANs — Two networks competing to create realistic fakes
- 🌊 Diffusion Models — Creates stunning images from random noise
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