Implementing AI Algorithms from Scratch – Free Certification by CodeSignal

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Implementing AI Algorithms from Scratch – Free Certification by CodeSignal

Unlock the core of machine learning and AI by building powerful algorithms from the ground up—without using high-level libraries like scikit-learn. This advanced course path challenges you to understand, implement, and optimize classic ML techniques entirely from scratch, giving you a strong foundational grasp of AI.

Course Details

• Platform: CodeSignal

• Language: English

• Level: Advanced

• Certificate: Free Certificate Available

• Ratings: 67 reviews on CodeSignal

• Includes: 6 detailed courses covering the theoretical and practical implementation of AI algorithms

Syllabus Overview

• Regression and Gradient Descent – Build core regression models from scratch, including simple and multiple linear regression, and logistic regression. Learn the mechanics of gradient descent in-depth to optimize these models without external libraries.

• Classification Algorithms and Metrics – Implement major classification algorithms such as Logistic Regression, k-Nearest Neighbors, Naive Bayes, and Decision Trees. Understand key evaluation metrics like AUC-ROC and construct them by hand.

• Gradient Descent: Building Optimization Algorithms from Scratch – Go beyond basic gradient descent by implementing advanced optimization techniques like Stochastic Gradient Descent, Mini-Batch Gradient Descent, Momentum, RMSProp, and Adam from scratch.

• Ensemble Methods from Scratch – Master ensemble techniques by hand-coding Bagging, Random Forest, AdaBoost, and Gradient Boosting (XGBoost), gaining practical insights into how these models improve accuracy and reduce variance.

• Unsupervised Learning and Clustering – Implement unsupervised learning methods including k-Means, mini-batch k-Means, DBSCAN, and Principal Component Analysis (PCA). Learn to evaluate cluster performance using homogeneity, completeness, and V-measure metrics.

• Neural Networks Basics from Scratch – Get hands-on with neural network architecture by building Perceptrons, activation functions, and multi-layer networks from zero. Understand the inner workings of modern AI, including forward and backward propagation without relying on any ML libraries.

Why Join This Course?

• Pure Hands-On Learning – No external libraries like scikit-learn—just pure algorithmic building

• Deep Theoretical Foundation – Grasp the why and how behind the math of ML models

• Real-World Applications – Apply your self-built models to real AI problems

• Boost Your ML Portfolio – Impress recruiters with projects that show real algorithmic understanding

• Free Certification – Showcase your skills with a certificate upon completion