Machine Learning Course

This Machine Learning track on TechLearningPRO takes you from dataset hygiene through classical algorithms, forecasting, and production-minded deployment.

Machine learning builds programs whose behavior is shaped by data.

ML problems fall into a few recurring families.

Data Preprocessing Introduction — Turn raw tables into numeric features models can consume.

ML Workflow — Repeatable path from collection through deployment and monitoring.

Data Cleaning — Fix missing, duplicate, and invalid values before training.

Data Preprocessing in Python — pandas + scikit-learn pipelines for tabular prep.

Feature Scaling — Put features on comparable scales for distance-based models.

Feature Extraction — Derive signals from text, images, or timestamps.

Feature Engineering — Domain-aware columns that simplify the learning task.

Feature Selection Techniques — Drop noisy columns to reduce variance and cost.

Exploratory Data Analysis — Visual and numeric summaries before modeling.

Exploratory Data Analysis in Python — pandas, seaborn, matplotlib for quick insight.

Advanced EDA — Pair plots, heatmaps, target checks, leakage hunting.

Time Series Data Visualization — Trend, seasonality, and autocorrelation plots.

Regularization in Machine Learning — Penalties that curb overfitting on complex models.

A confusion matrix counts prediction outcomes in four buckets: true positive, false positive, true negative, and false negative.

Precision, Recall and F1-Score — Metrics when classes are imbalanced.

AUC-ROC Curve — Threshold-independent ranking quality for binary models.

Cross-validation — Rotate train/validation folds for stable estimates.

Hyperparameter Tuning — Search strategies for better model settings.

Supervised Learning — Learn from labeled examples to predict class or value.

Linear regression models a continuous target as a weighted combination of input features plus an intercept.

Gradient Descent in Linear Regression — Iterative weight updates minimizing squared error.

Multiple Linear Regression — Several input columns in one linear model.

Logistic Regression — Sigmoid outputs for binary probability estimates.

Cost Function in Logistic Regression — Cross-entropy as the training objective.

Decision Tree in Machine Learning — Recursive splits forming an interpretable tree.

Types of Decision Tree Algorithms — Split criteria across CART, ID3-family methods.

Decision Tree Regression — Leaf averages for numeric prediction.

Decision Tree Classification — Majority vote leaves for class labels.

SVMs — Max-margin separators with kernel extensions.

SVM Hyperparameter Tuning - GridSearchCV — Cross-validated search over C, kernel, gamma.

Non-Linear SVM — Kernel maps for curved decision surfaces.

KNN — Vote among nearest training neighbors.

Decision Boundaries in KNN — How k and metrics shape regions.

Naive Bayes — Fast probabilistic classifier with independence assumption.

Gaussian Naive Bayes — Continuous features as per-class Gaussians.

Multinomial Naive Bayes — Count features such as word frequencies.

Bernoulli Naive Bayes — Binary presence features.

Complement Naive Bayes — Variant tuned for skewed text classes.

Random forest trains many decision trees on bootstrap samples and random feature subsets, then aggregates their votes.

Random Forest Classifier — Ensemble vote for classification.

Random Forest Regression — Average tree outputs for regression.

Hyperparameter Tuning in Random Forest — Depth, estimators, and feature fraction search.

Ensemble Learning — Combine models via bagging or boosting.

Unsupervised Learning — Structure discovery without label columns.

K-means partitions points into k groups by repeatedly assigning each point to the nearest centroid and recomputing centroids as cluster means.

Elbow Method for Optimal k — Inertia curve to pick cluster count.

K-Means++ Clustering — Smarter centroid seeding.

K-Mode Clustering — K-means for categorical modes.

Fuzzy C-Means Clustering — Soft membership clustering.

Gaussian Mixture Models — Mixture of Gaussian components.

Expectation-Maximization Algorithm — EM for latent variable models.

Dirichlet Process Mixture Models — Non-parametric mixture sizing.

Hierarchical Clustering — Dendrogram-based nested clusters.

Agglomerative Clustering — Bottom-up hierarchical merges.

Divisive Clustering — Top-down hierarchical splits.

Affinity Propagation — Message passing without preset k.

DBSCAN — Density-based clusters and noise points.

OPTICS — Ordering points across density levels.

Principal Component Analysis (PCA) — Orthogonal directions of maximum variance.

t-SNE — Nonlinear 2D/3D embedding for visualization.

Non-negative Matrix Factorization — Parts-based factorization for non-negative data.

Independent Component Analysis — Separate statistically independent sources.

Isomap — Manifold learning preserving geodesics.

Locally Linear Embedding (LLE) — Preserve local linear neighborhoods.

Apriori Algorithm — Frequent itemsets for basket analysis.

Implementing Apriori Algorithm — Code support/confidence mining.

FP-Growth — Compact tree mining without candidate generation.

ECLAT Algorithm — Vertical tid-list pattern search.

Reinforcement Learning — Agents improve policies from reward feedback.

Markov Decision Processes — States, actions, transitions, rewards formalism.

Bellman Equation — Recursive optimality for value functions.

Value Iteration Algorithm — Dynamic programming on tabular MDPs.

Monte Carlo Tree Search — Simulation planning for game trees.

Q-learning learns action values Q(s,a) from experience without a model of the environment.

SARSA — On-policy TD control variant.

Monte Carlo Methods — Estimate returns from sample episodes.

REINFORCE Algorithm — Monte Carlo policy gradients.

Actor-Critic Algorithm — Joint policy and value networks.

A3C — Asynchronous advantage actor-critic training.

Semi-Supervised Learning — Exploit unlabeled mass when labels are costly.

Semi-Supervised Classification — Label propagation and graph methods.

Self-Training in Semi-Supervised Learning — Pseudo-label iterative refinement.

Few-Shot Learning — Generalize from handfuls of labeled examples.

ARIMA — Classical univariate time-series model.

SARIMA — Seasonal extension of ARIMA.

Exponential Smoothing (Holt-Winters) — Trend and seasonality via weighted averages.

Machine Learning Deployment — Expose models to applications safely.

Deploy ML Model using Streamlit — Interactive demos with Streamlit.

Deploy ML Web App on Heroku — Host a lightweight ML web app.

Gradio for ML Prototypes — Shareable UI for model trials.

Deploy ML Model using Flask — REST endpoints with Flask.

Deploying ML Models as API using FastAPI — Async APIs with OpenAPI docs.

MLOps extends DevOps practices to models: versioned data, reproducible training, staged deployment, monitoring drift, and retraining triggers.

CI/CD in MLOps — Automate train, test, promote pipelines.

End-to-End MLOps — From data ingest through monitored serving.