/Data Science - Advanced

Course Curriculum

Section 1 · Module 1: Advanced Data Processing & Feature Engineering

○ Feature selection and dimensionality reduction (PCA, LDA) ○ Handling categorical and numerical data ○ Outlier detection and feature scaling

Section 2 · Module 2: Supervised Learning Techniques

○ Regression models (Linear, Polynomial, Ridge, Lasso) ○ Classification models (Logistic Regression, Decision Trees, Random Forest, SVM) ▶ Model evaluation metrics (RMSE, Precision, Recall, F1-score, ROC-AUC)

Section 3 · Module 3: Unsupervised Learning Techniques

○ Clustering techniques (K-Means, DBSCAN, Hierarchical) ○ Anomaly detection ○ Dimensionality reduction with t-SNE

Section 4 · Module 4: Deep Learning with Neural Networks

○ Introduction to TensorFlow & Keras ○ Building Artificial Neural Networks (ANN) ○ Introduction to Convolutional Neural Networks (CNN) ○ Introduction to Recurrent Neural Networks (RNN)

Section 5 · Module 5: Model Deployment & Big Data

○ Deploying models with Flask and FastAPI ○ Working with cloud platforms (AWS, GCP, Azure) ○ Introduction to Big Data technologies (Hadoop, Spark)

Module 2: Supervised Learning Techniques

Model evaluation metrics (RMSE, Precision, Recall, F1-score, ROC-AUC)

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