Curriculum
5 Sections
16 Lessons
10 Weeks
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Module 1: Advanced Data Processing & Feature Engineering
3
1.1
Feature selection and dimensionality reduction (PCA, LDA)
1.2
Handling categorical and numerical data
1.3
Outlier detection and feature scaling
Module 2: Supervised Learning Techniques
3
2.1
Regression models (Linear, Polynomial, Ridge, Lasso)
2.2
Classification models (Logistic Regression, Decision Trees, Random Forest, SVM)
2.3
Model evaluation metrics (RMSE, Precision, Recall, F1-score, ROC-AUC)
Module 3: Unsupervised Learning Techniques
3
3.1
Clustering techniques (K-Means, DBSCAN, Hierarchical)
3.2
Anomaly detection
3.3
Dimensionality reduction with t-SNE
Module 4: Deep Learning with Neural Networks
4
4.1
Introduction to TensorFlow & Keras
4.2
Building Artificial Neural Networks (ANN)
4.3
Introduction to Convolutional Neural Networks (CNN)
4.4
Introduction to Recurrent Neural Networks (RNN)
Module 5: Model Deployment & Big Data
3
5.1
Deploying models with Flask and FastAPI
5.2
Working with cloud platforms (AWS, GCP, Azure)
5.3
Introduction to Big Data technologies (Hadoop, Spark)
Data Science – Advanced
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