🎓 Advanced Certification in Data Science & AI

Dive deep into machine learning, statistical modeling, and AI algorithms. Learn to extract valuable insights from complex datasets.

Program Curriculum

🔹 Module 1: Foundations of Data Science

  • Introduction to Data Science & AI
  • Business Problem Understanding & Data Science Life Cycle
  • Overview of Python for Data Science
  • Exploratory Data Analysis (EDA) Techniques

🔹 Module 2: Mathematics & Statistics for Data Science

  • Linear Algebra Basics (Vectors, Matrices)
  • Probability Theory and Distributions
  • Descriptive & Inferential Statistics
  • Hypothesis Testing & p-values

🔹 Module 3: Data Handling & Visualization

  • Data Collection (APIs, Web Scraping) & Preprocessing
  • Handling Missing Values, Outliers
  • Feature Engineering & Transformation
  • Data Visualization Tools: Matplotlib, Seaborn, Plotly

🔹 Module 4: Machine Learning - Supervised Learning

  • Regression Algorithms: Linear, Ridge, Lasso
  • Classification Algorithms: Logistic Regression, Decision Trees, Random Forest, KNN, SVM
  • Model Evaluation: Accuracy, Precision, Recall, AUC-ROC

🔹 Module 5: Machine Learning - Unsupervised Learning

  • Clustering Techniques: K-Means, DBSCAN, Hierarchical
  • Dimensionality Reduction: PCA, t-SNE
  • Association Rules: Apriori, FP-Growth

🔹 Module 6: Deep Learning & Neural Networks

  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN), LSTM, and GRU
  • Transfer Learning with Pre-trained Models (ResNet, VGG)

🔹 Module 7: Natural Language Processing (NLP)

  • Text Cleaning, Tokenization, Stemming, Lemmatization
  • TF-IDF, Word2Vec, BERT
  • Sentiment Analysis & Text Classification

🔹 Module 8: Advanced AI Topics

  • Reinforcement Learning (Q-Learning)
  • Recommendation Systems (Collaborative & Content-Based)
  • Time Series Forecasting (ARIMA, Prophet)
  • Generative AI (GANs) & Prompt Engineering

🔹 Module 9: Data Science in Production

  • Model Deployment with Flask/FastAPI
  • Model Monitoring & Retraining with MLflow
  • Dockerizing ML Models
  • Cloud Deployment (AWS/GCP/Azure Basics)

🔹 Module 10: Capstone Projects & Case Studies

  • E-commerce Product Recommendation System
  • Customer Churn Prediction
  • Image Classification App with CNN
  • Credit Risk Scoring Model

🛠️ Tools & Technologies Covered

Languages:

Python, SQL

ML & DL:

Scikit-learn, TensorFlow, Keras, PyTorch

NLP:

NLTK, spaCy, Transformers (HuggingFace)

Data Handling:

Pandas, NumPy

Visualization:

Matplotlib, Seaborn, Plotly

Deployment:

Flask, FastAPI, Docker, MLflow

Cloud:

AWS Sagemaker, GCP AI Platform (Intro)