Machine Learning
Why?
Machine learning is revolutionizing every industry - from healthcare diagnosis to autonomous vehicles, from recommendation systems to fraud detection. This course provides you with the fundamental algorithms and techniques needed to build intelligent systems that can learn from data. Whether you want to become a data scientist, AI engineer, or simply understand how modern technology works, machine learning is an essential skill that opens doors to high-demand careers in tech.
What?
This comprehensive course covers both supervised and unsupervised learning algorithms. You'll learn classification techniques like logistic regression, naive Bayes, and k-nearest neighbors, as well as clustering methods and neural networks. The course emphasizes both theoretical understanding and practical implementation, teaching you how to evaluate model performance, optimize algorithms, and solve real-world problems using machine learning.
Curriculum:
Evaluation Metrics for Classifiers
Model performance assessment including accuracy, precision, recall, F1-score, confusion matrix, ROC curves, and AUC. Understanding when to use different metrics and how to interpret classification results.
Clustering Methods
Unsupervised learning techniques including K-means clustering, hierarchical clustering, and model-based approaches. Understanding cluster quality measures and determining optimal number of clusters.
K-Nearest Neighbors (K-NN)
Instance-based learning algorithm for classification and regression. Understanding distance metrics, choosing optimal K values, and handling the curse of dimensionality.
Logistic Regression
Linear classification method using sigmoid function, maximum likelihood estimation, gradient descent optimization, and handling binary and multiclass classification problems.
Naive Bayes Classifier
Probabilistic classification based on Bayes' theorem. Understanding conditional independence assumptions, handling categorical and continuous features, and applications in text classification and sentiment analysis.
Gradient Descent Optimization
Fundamental optimization algorithm for machine learning. Understanding cost functions, learning rates, batch vs stochastic gradient descent, and convergence criteria.
Perceptron and Neural Network Basics
Introduction to artificial neurons, perceptron learning algorithm, activation functions, and the foundation for understanding more complex neural networks.
Feed Forward Neural Networks
Multi-layer perceptrons, backpropagation algorithm, weight initialization, activation functions (sigmoid, ReLU, tanh), and training deep networks including cost functions and optimization.
Backpropagation Algorithm
Detailed understanding of how neural networks learn through backward pass, chain rule application, gradient computation, and weight updates across multiple layers.
Notes
Real talk: in your job, you'll mostly use libraries like scikit-learn or TensorFlow, not code these algorithms from scratch. But understanding the theory is crucial for choosing the right model for each problem and absolutely essential for interviews - they will ask about these concepts to test your understanding.
