Industry-Ready AI & ML for R&D and Analytics

Week 1: Python, Statistics & EDA Foundations

1. Day 1: Industry ML lifecycle, CRISP-DM, MLOps overview, and setup of Anaconda, VS Code, and Git
2. Day 2: Python refresh — functions, OOP, exception handling, and file I/O
3. Day 3: NumPy — vectorization, broadcasting, and linear algebra
4. Day 4: Pandas — grouping, merging, pivot tables, and time-series indexing
5. Day 5: Exploratory data analysis, missing values, outliers, data visualization, and an R&D dataset report

Week 2: Classical Machine Learning — Supervised Learning

1. Day 1: Probability, distributions, hypothesis testing, and p-values
2. Day 2: Linear regression — assumptions, OLS, MAE, MSE, and R²
3. Day 3: Logistic regression, classification, ROC-AUC, and confusion matrices
4. Day 4: Decision trees, Random Forest, SVM, and feature importance
5. Day 5: XGBoost, LightGBM, CatBoost, and comparison of ML models using an R&D dataset

Week 3: Unsupervised Learning & Model Optimization

1. Day 1: K-Means, DBSCAN, and hierarchical clustering
2. Day 2: Dimensionality reduction — PCA, t-SNE, and UMAP
3. Day 3: Anomaly detection using Isolation Forest and Local Outlier Factor
4. Day 4: Cross-validation — K-Fold, Stratified, TimeSeriesSplit, and GroupKFold
5. Day 5: Hyperparameter tuning, Optuna, Scikit-learn pipelines, and model saving with Joblib

Week 4: Deep Learning Fundamentals

1. Day 1: Neural networks — perceptrons, MLPs, activation functions, and backpropagation
2. Day 2: PyTorch/TensorFlow — tensors, autograd, and training loops
3. Day 3: Loss functions, optimizers, and model training
4. Day 4: Regularization — dropout, batch normalization, early stopping, and L1/L2
5. Day 5: CNNs, transfer learning with ResNet/EfficientNet, and microscopy image classification

Week 5: Sequence Learning & Time-Series Deep Learning

1. Day 1: RNN, LSTM, and GRU architectures
2. Day 2: 1D CNNs for sensor data and bioreactor signals
3. Day 3: Time-series forecasting — ARIMA, Prophet, and LSTM
4. Day 4: Attention mechanisms, sequence-to-sequence models, and Transformers
5. Day 5: Hugging Face pretrained models and an LSTM-based sensor-data forecasting project

Week 6: NLP for R&D & Scientific Text

1. Day 1: NLP pipeline — tokenization, stemming, lemmatization, and spaCy
2. Day 2: Word embeddings — Word2Vec, GloVe, and FastText
3. Day 3: BERT, SciBERT, and BioBERT for scientific literature and patents
4. Day 4: Text classification and named entity recognition for genes, drugs, and diseases
5. Day 5: Topic modeling with LDA/BERTopic and a PubMed literature analysis project

Week 7: MLOps & Model Deployment

1. Day 1: FastAPI, Pydantic, and serving machine learning models through APIs
2. Day 2: Docker, Dockerfiles, and Docker Compose
3. Day 3: MLflow experiment tracking and model registry
4. Day 4: DVC data versioning, SHAP/LIME explainability, and model monitoring
5. Day 5: Data/model drift monitoring and deployment of an ML API with a Streamlit interface

Week 8: Capstone Projects & Industry Readiness

1. Day 1: GitHub Actions, CI/CD, cloud basics, and AWS/GCP deployment
2. Day 2: A/B testing, model validation, FDA SaMD, GxP, and documentation
3. Day 3: Pharma industry case studies and selection of a real-world R&D problem
4. Day 4: Capstone development — data preparation, model training, evaluation, explainability, and application deployment
5. Day 5: Final project demonstration, technical documentation, GitHub portfolio, mock interviews, and a six-month learning roadmap