Machine Learning Concepts
The course teaches core machine‑learning concepts—supervised and unsupervised learning, model evaluation, feature engineering, and neural network basics—using intuitive mathematics and pseudocode to build solid conceptual foundations.
Who Should Take This
Data analysts, software engineers, or product managers with basic statistics knowledge who want to understand how machine‑learning algorithms work and how to assess them. They seek a mathematically grounded yet code‑agnostic overview that prepares them to design, evaluate, and improve models across domains.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
67 learning goals
1
Supervised Learning
5 topics
Regression Algorithms
- Describe linear regression including the ordinary least squares objective, coefficient interpretation, and assumptions of linearity, independence, and homoscedasticity
- Apply polynomial regression and regularization techniques (Ridge, Lasso) to control model complexity and prevent overfitting
- Analyze residual plots and regression diagnostics to identify violations of model assumptions and determine corrective actions
Classification Algorithms
- Describe logistic regression including the sigmoid function, log-odds interpretation, and decision boundary formation for binary classification
- Describe k-nearest neighbors classification including distance metrics, the effect of k on decision boundaries, and the curse of dimensionality
- Describe naive Bayes classifiers including the independence assumption, Laplace smoothing, and why they perform well despite the naive assumption
- Compare logistic regression, k-NN, and naive Bayes classifiers evaluating trade-offs in interpretability, scalability, and performance on different data distributions
Tree-Based Methods
- Describe decision tree construction including splitting criteria (Gini impurity, information gain), tree depth, and pruning strategies
- Describe ensemble methods including bagging (Random Forests) and boosting (AdaBoost, Gradient Boosting) and explain how they reduce variance or bias
- Apply decision trees and random forests to classification problems and interpret feature importance rankings to understand model decisions
- Analyze when tree-based methods outperform linear models and vice versa based on feature interactions, non-linearity, and dataset characteristics
Support Vector Machines
- Describe the SVM maximum margin principle including support vectors, margin width, and the role of the soft margin parameter C
- Apply the kernel trick concept to explain how SVMs handle non-linearly separable data using RBF, polynomial, and linear kernels
- Analyze the trade-offs between SVM kernel choices and the impact of the C and gamma parameters on model complexity and generalization
Handling Imbalanced Data
- Describe the class imbalance problem and explain why standard accuracy is misleading when one class dominates the dataset
- Apply resampling strategies including oversampling (SMOTE), undersampling, and class weighting to improve classifier performance on minority classes
- Evaluate the trade-offs between different imbalanced data strategies and their effects on precision, recall, and real-world deployment performance
2
Unsupervised Learning
3 topics
Clustering Algorithms
- Describe k-means clustering including centroid initialization, the iterative assignment-update loop, and convergence criteria
- Describe hierarchical clustering including agglomerative vs divisive approaches, linkage criteria (single, complete, average, Ward), and dendrogram interpretation
- Apply cluster validation metrics including silhouette score, elbow method, and Davies-Bouldin index to determine optimal cluster count
- Analyze the limitations of k-means for non-spherical clusters and compare with DBSCAN for density-based cluster detection
Dimensionality Reduction
- Describe PCA including variance maximization, eigenvectors as principal components, and the scree plot for choosing component count
- Apply PCA to reduce high-dimensional data for visualization and preprocessing and explain the trade-off between dimensionality and information loss
- Compare PCA and t-SNE for visualization purposes explaining why t-SNE preserves local structure while PCA preserves global variance
Anomaly Detection
- Describe anomaly detection approaches including statistical methods, isolation forests, and distance-based outlier detection
- Apply anomaly detection to identify unusual patterns in datasets and distinguish between global outliers and contextual anomalies
- Evaluate the challenge of anomaly detection without labeled data and describe semi-supervised approaches that leverage small amounts of labeled anomalies
3
Model Evaluation & Selection
3 topics
Evaluation Metrics
- Describe classification metrics including accuracy, precision, recall, F1-score, and explain how class imbalance affects accuracy as a metric
- Describe regression metrics including MSE, RMSE, MAE, and R-squared and explain what each captures about prediction error distribution
- Apply confusion matrix analysis and ROC curve interpretation to evaluate classifier performance at different threshold settings
- Analyze precision-recall trade-offs and select appropriate evaluation metrics based on the business cost of false positives vs false negatives
Validation Strategies
- Describe cross-validation techniques including k-fold, stratified k-fold, and leave-one-out and explain when each is appropriate
- Apply proper data splitting including train/validation/test sets and explain why temporal data requires time-based splitting rather than random splitting
- Analyze data leakage scenarios including target leakage and train-test contamination and explain how they produce misleadingly high evaluation scores
Hyperparameter Tuning
- Describe hyperparameter tuning methods including grid search, random search, and Bayesian optimization and their computational trade-offs
- Apply nested cross-validation to simultaneously tune hyperparameters and estimate generalization performance without optimistic bias
- Analyze the diminishing returns of hyperparameter tuning and evaluate when additional tuning effort is unlikely to produce meaningful performance gains
4
Feature Engineering
3 topics
Data Preprocessing
- Describe feature scaling methods including standardization (z-score) and normalization (min-max) and explain which algorithms require scaled features
- Apply encoding techniques for categorical variables including one-hot encoding, label encoding, and target encoding and explain when each is appropriate
- Analyze the impact of different preprocessing choices on model performance and identify when preprocessing order matters
- Apply missing value imputation strategies including mean, median, KNN, and indicator-variable approaches and explain how each affects downstream model behavior
Feature Creation & Transformation
- Apply feature creation techniques including interaction terms, polynomial features, binning, and log transformations to improve model expressiveness
- Evaluate the risk of feature explosion when creating polynomial and interaction features and apply strategies to manage high-dimensional feature spaces
Feature Selection
- Describe feature selection methods including filter methods (correlation, mutual information), wrapper methods (forward/backward selection), and embedded methods (Lasso, tree importance)
- Apply feature importance analysis from tree-based models and permutation importance to identify the most predictive features in a dataset
- Analyze multicollinearity among features using VIF and correlation matrices and explain how correlated features affect model stability and interpretability
5
Neural Network Basics
3 topics
Network Architecture
- Describe the perceptron model including weighted inputs, bias terms, and activation functions as the building block of neural networks
- Describe feedforward neural network architecture including input layers, hidden layers, output layers, and how depth and width affect model capacity
- Compare activation functions including sigmoid, tanh, ReLU, and softmax explaining their properties, use cases, and the vanishing gradient problem
Training & Optimization
- Describe the backpropagation algorithm at an intuitive level including forward pass, loss computation, gradient calculation, and weight updates
- Describe gradient descent variants including batch, stochastic, and mini-batch and explain the role of learning rate in convergence behavior
- Apply loss function selection based on task type including MSE for regression, cross-entropy for classification, and explain how the loss function shapes learning
- Analyze common training problems including vanishing/exploding gradients, local minima, and overfitting and describe mitigation strategies such as batch normalization and dropout
Neural Network Types (Overview)
- Describe convolutional neural networks at a high level including convolution operations, pooling, and their suitability for image data
- Describe recurrent neural networks at a high level including sequence processing, hidden state propagation, and their suitability for sequential data
- Compare traditional ML algorithms with neural networks evaluating trade-offs in data requirements, interpretability, training cost, and performance on structured vs unstructured data
6
ML Workflow & Best Practices
4 topics
End-to-End ML Process
- Describe the CRISP-DM methodology including business understanding, data understanding, preparation, modeling, evaluation, and deployment phases
- Apply problem framing techniques to translate business questions into well-defined ML tasks with clear success criteria and evaluation metrics
- Evaluate whether a given problem is suitable for ML vs simpler heuristic or rule-based approaches considering data availability, interpretability requirements, and maintenance cost
Experiment Management
- Describe experiment tracking practices including logging hyperparameters, metrics, and artifacts to enable reproducible model comparisons
- Apply baseline model establishment as a first step in ML projects and explain why comparing against simple baselines prevents wasted complexity
- Apply version control for ML experiments including tracking datasets, model artifacts, and pipeline configurations to ensure reproducibility
Model Interpretability
- Describe model interpretability techniques including SHAP values, LIME, and partial dependence plots and explain why interpretability matters for trust and debugging
- Analyze the trade-off between model complexity and interpretability and recommend the appropriate level of model transparency for different stakeholders and use cases
ML Ethics & Fairness
- Identify sources of bias in ML systems including data collection bias, label bias, and representation bias that can produce discriminatory outcomes
- Apply fairness metrics including demographic parity and equalized odds to evaluate whether a model produces equitable outcomes across demographic groups
- Analyze the tension between model accuracy and fairness and evaluate organizational frameworks for responsible ML deployment
Hands-On Labs
3 labs
~60 min total
Console Simulator
Code Sandbox
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- Supervised learning algorithms (linear regression, logistic regression, decision trees, random forests, SVMs, k-NN, naive Bayes), unsupervised learning algorithms (k-means, hierarchical clustering, PCA, t-SNE), model evaluation and selection (cross-validation, bias-variance trade-off, hyperparameter tuning), feature engineering and preprocessing, neural network basics (perceptrons, activation functions, backpropagation, gradient descent), ML workflow and best practices (experiment tracking, data splitting, pipeline design)
Not Covered
- Framework-specific code (TensorFlow, PyTorch, Keras implementation details)
- Advanced deep learning architectures (transformers, GANs, diffusion models, CNNs beyond basics)
- Reinforcement learning
- Distributed training and large-scale ML infrastructure
- MLOps and production deployment pipelines
- AutoML platforms and tools
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