Name: Machine Learning Algorithms Unpacked
Uploaded: 2025-12-20
Duration: 9 min 37 s
Description: Machine Learning Algorithms Unpacked. ABDULLAHI USMAN ISAH.

Machine Learning Algorithms Unpacked. ABDULLAHI USMAN ISAH. 

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Learning Paradigms. Core Algorithms. Advanced Models. 

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Supervised Learning. Essentials. Trains models on labeled data to predict outcomes by minimizing a loss function, powering applications from spam detection to price forecasting.. 

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Trains models on labeled data to predict outcomes by minimizing a loss function, powering applications from spam detection to price forecasting.

Learns from input-output pairs to map new, unseen inputs to the correct output.

Optimizes a loss function comparing predictions to ground truth to improve accuracy.

Enables both discrete category prediction (classification) and continuous value estimation (regression).

APPLE
ORANGE
ORANGE
APPLE
ORANGE
Labeled Dataset
Training
Prediction
Test Data
APPLE
ORANGE

Unsupervised Learning Insights. Discovers hidden structure in unlabeled data, enabling customer segmentation, anomaly detection, and exploratory analysis without predefined targets.. 

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Discovers hidden structure in unlabeled data, enabling customer segmentation, anomaly detection, and exploratory analysis without predefined targets.

Groups similar data points to find inherent structures.

Reduces features while preserving essential information.

Uncovers relationships between variables in large datasets.

Input Raw Data
Unlabeled Data
DatabaseTown
Interpretation
Outputs
Processing
08
00
08
o
Algorithms

Reinforcement Learning Basics. Optimizes sequential decision-making by rewarding beneficial actions. Agents learn optimal policies through trial-and-error interactions with an environment.. 

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Optimizes sequential decision-making by rewarding beneficial actions. Agents learn optimal policies through trial-and-error interactions with an environment.

Key Methods: Model-free approaches like Q-learning and Policy Gradients are widely used.

Input Raw Data
Vidva
Environment
Best Action
Selection of
Algorithm
Agent
Output
Reward
State

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Linear Regression. Fits a weighted sum to predict continuous values, providing an interpretable baseline for forecasting.. 

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Fits a weighted sum to predict continuous values, providing an interpretable baseline for forecasting.

Passes a weighted sum through a sigmoid to estimate class probabilities for binary classification.

Y=O
Linear Regression
X-Axis
Logistic Regression
X-Axis

Tree-Based Models Explained. Decision trees partition data via recursive if-else rules. Ensembles combine multiple trees for superior performance.. 

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Decision trees partition data via recursive if-else rules. Ensembles combine multiple trees for superior performance.

Simple, interpretable, but prone to overfitting.

Averages many trees to reduce variance and improve accuracy.

Sequentially corrects errors, building a powerful predictor.

Decision Tree-I
It-I
Dataset
Tree-2
Majority Voting / Averaging
Find Result
Dec Tree—N

Support Vector Machines. SVMs find the hyperplane that maximizes the margin between classes. They use kernel tricks to handle non-linear boundaries, making them robust for high-dimensional classification.. 

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SVMs find the hyperplane that maximizes the margin between classes. They use kernel tricks to handle non-linear boundaries, making them robust for high-dimensional classification.

The critical samples that lie closest to the decision boundary and define the maximum margin hyperplane.

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Neural Networks Overview. Multilayer perceptrons stack weighted, activated layers to learn complex mappings from inputs to outputs, making them versatile for a wide range of tasks.. 

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Multilayer perceptrons stack weighted, activated layers to learn complex mappings from inputs to outputs, making them versatile for a wide range of tasks.

Adjusts weights by calculating the gradient of the loss function.

Ideal for image, text, and numeric prediction tasks when large datasets are available.

Neural Network Architecture
Input
Weights Activation function
x,
Input
2' | f(2)
Node
Hidden
output
Output

Deep Learning Variants. Specialized architectures designed to exploit different types of data structure, driving modern AI breakthroughs.. 

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Specialized architectures designed to exploit different types of data structure, driving modern AI breakthroughs.

Exploit spatial locality with convolutional filters for image recognition.

Capture temporal dependencies in sequential data.

Use self-attention to model long-range context in language.

Convolution Neural Network (CNN)
Input
PØIing
Pooling
Convolutbn
Feature Maps
Prx*ing
Convdutbn
Adi vation
Feature Extraction

Positionwnse
Multi-head
attention
Position wise
Mutl•head
attention
Masked
mufti-head
attention

Generative Adversarial Networks. GANs train two networks in competition: a Generator that crafts fake data and a Discriminator that judges authenticity.. 

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GANs train two networks in competition: a Generator that crafts fake data and a Discriminator that judges authenticity.

This adversarial game yields photorealistic images, synthetic data, and creative content, advancing the field of generative AI.

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Algorithm Selection Criteria. Choose algorithms by matching data characteristics and project requirements to model assumptions and capabilities.. 

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Choose algorithms by matching data characteristics and project requirements to model assumptions and capabilities.

Size, dimensionality, linearity, and quality.

Interpretability, training time, and scalability.

Always validate with cross-validation to ensure generalization.

New
Dataset
Real—world Data from 10T
Sensors
Edge ML:
Level 1
Reasoning
Meta-features Extractor
Net
Algorithm Family &
Meta-features
Edge ML:
Level 1
Reasoning
Algorithms
Characterization
Case-Base
Algorithms
Performance
Evaluation
Achieved
Datasets
Level 2
Reasoning

Evaluation Metrics Mastery. Using the right metrics is crucial for guiding iterative improvement and making a fair comparison of competing models. They provide a quantitative measure of performance.. 

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Using the right metrics is crucial for guiding iterative improvement and making a fair comparison of competing models. They provide a quantitative measure of performance.

Choose metrics that align with your business objective. A single metric is rarely sufficient.

Accuracy, Precision, Recall, F1-Score, ROC-AUC.

Emerging Trends in ML. Pushing accuracy frontiers while reducing dependence on costly manual labels.. 

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Pushing accuracy frontiers while reducing dependence on costly manual labels.

Leverages unlabeled data by creating predictive pretext tasks (e.g., predicting missing words).

Learns by aligning different augmented views of the same data point, pulling them closer in the representation space.

Scales transformers to massive datasets, creating versatile models that can be adapted to many downstream tasks with little fine-tuning.

THANK YOU. ABDULLAHI USMAN ISAH. 2025/08/05. 

Machine Learning Algorithms Unpacked

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