Introduction to Flask

• Overview of Flask
• What is Flask? Understanding its microframework structure.
• Setting up a Flask environment: Installation and basic Configuration
• First Flask Application
• Creating a simple app: Routing and view functions.
• Templating with Jinja2: Basic templates to render data.

Flask Routing and Forms

• Advanced Routing
• Dynamic routing and URL building.
• Handling different HTTP methods: GET and POST requests.
• Working with Forms
• Flask-WTF for form handling: Validations and rendering forms.
• CSRF protection in Flask applications.

Flask and Data Handling

• Integrating Flask with SQL Databases
• Using Flask-SQLAlchemy: Basic ORM concepts, creating models, and querying data.
• API Development with Flask
• Creating RESTful APIs to interact with machine learning models.
• Using Flask-RESTful extension for resource-based routes.FastAPI: Basics to Intermediate

Introduction to FastAPI

• Why FastAPI?
• Advantages of FastAPI over other Python web frameworks, especially for async features.
• Setting up a FastAPI project: Installation and first application.
• FastAPI Routing and Models Path operations: GET, POST, DELETE, and PUT. Request body and path parameters: Using Pydantic models for data validation.

Building APIs with FastAPI

• API Operations Advanced model validation techniques and serialization.
• Dependency injection: Using FastAPI’s dependency injection system for better code organization.
• Asynchronous Features Understanding async and await keywords.
• Asynchronous SQL database interactions using databases like SQLAlchemy async.

Serving Machine Learning Models

• Integrating ML Models
• Building endpoints to serve predictions from pre-trained machine learning models.
• Handling asynchronous tasks within FastAPI to manage long-running ML predictions.
• Security and Production Adding authentication and authorization layers to secure APIs.
• Tips for deploying Flask and FastAPI applications to production environments.

Introduction to Machine Learning

• Overview of Machine Learning

• Definitions and Significance*:

  Students will explore the fundamental concepts and various definitions of machine learning, understanding its crucial role in leveraging big data in numerous industries such as finance, healthcare, and more.

• Types of Machine Learning:

  The course will differentiate between the three main types of machine learning: supervised learning (where the model is trained on labeled data), unsupervised learning (where the model finds patterns in unlabeled data), and reinforcement learning (where an agent learns to behave in an environment by performing actions and receiving rewards).

Supervised Learning Algorithms

Regression Algorithms

• • Linear Regression*: Focuses on predicting a continuous variable using a linear relationship formed from the input variables.
  • Polynomial Regression: Extends linear regression to model non-linear relationships between the independent and dependent variables.
  • Decision Tree Regression: Uses decision trees to model the regression, helpful in capturing non-linear patterns with a tree structure.

Classification Algorithms

• Logistic Regression: Used for binary classification tasks; extends to multiclass classification under certain methods like one-vs-rest (OvR).
• K-Nearest Neighbors (KNN): A non-parametric method used for classification and regression; in classification, the output is a class membership.
• Support Vector Machines (SVM): Effective in high-dimensional spaces and ideal for complex datasets with clear margin of separation.
• Decision Trees and Random Forest*: Decision Trees are a non-linear predictive model, and Random Forest is an ensemble method of Decision Trees.
• Naive Bayes: Based on Bayes’ Theorem, it assumes independence between predictors and is particularly suited for large datasets.

Ensemble Methods and Handling Imbalanced Data

• Ensemble Techniques – Detailed techniques such as Bagging (Bootstrap Aggregating), Boosting, AdaBoost (an adaptive boosting method), and Gradient Boosting will be covered, emphasizing how they reduce variance and bias, and improve predictions.
• Strategies for Imbalanced Data* – Techniques such as Oversampling, Undersampling, and Synthetic Minority Over-sampling Technique (SMOTE) are discussed to handle imbalanced datasets effectively, ensuring that the minority class in a dataset is well-represented and not overlooked.

Unsupervised Learning Algorithms

Clustering Techniques

• K-Means Clustering: A method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters.
• Hierarchical Clustering*: Builds a tree of clusters and is particularly useful for hierarchical data, such as taxonomies.
• DBSCAN: Density-Based Spatial Clustering of Applications with Noise finds core samples of high density and expands clusters from them.
• Association Rule Learning – Apriori and Eclat algorithms*: Techniques for mining frequent itemsets and learning association rules. Commonly used in market basket analysis.

Model Evaluation and Hyperparameter Tuning

• • Evaluation Metrics* – Comprehensive exploration of metrics such as Accuracy, Precision, Recall, F1 Score, and ROC-AUC for classification; and MSE, RMSE, and MAE for regression.
  • Hyperparameter Tuning – Techniques such as Grid Search, Random Search, and Bayesian Optimization with tools like Optuna are explained. These methods help in finding the most optimal parameters for machine learning models to improve performance.

Introduction to Natural Language Toolkit (NLTK)

• Getting Started with NLTK
• Installation and setup of NLTK
• Overview of NLTK’s features and capabilities for processing text.
• Basic Text Processing with NLTK*
• Tokenization: Splitting text into sentences and words.
• Text normalization: Converting text to a standard format (case normalization, removing punctuation).
• Stopwords removal: Filtering out common words that may not add much meaning to the text.

Basics of OpenCV

• Introduction to OpenCV
• Installing and setting up OpenCV.
• Understanding how OpenCV handles images.
• Basic Image Processing Techniques
• Reading, displaying, and writing images.
• Basic operations on images: resizing, cropping, and rotating.
• Image transformations: Applying filters and color space conversions.

Basics of Convolutional Neural Networks (CNN)

• Understanding CNNs
• The architecture of CNNs: Layers involved (Convolutional layers, Pooling layers, Fully connected layers).
• The role of Convolutional layers: Feature detection through filters/kernels.
• Implementing a Simple CNN
• Building a basic CNN model for image classification.
• Training a CNN with a small dataset: Understanding the training process, including forward propagation and backpropagation.

Basics of Recurrent Neural Networks (RNN)

• Introduction to RNNs
• Why RNNs?
• Understanding their importance in modeling sequences.
• Architecture of RNNs: Feedback loops and their role.
• Challenges with Basic RNNs
• Exploring issues like vanishing and exploding gradients.
• Introduction to LSTMs
• How Long Short-Term Memory (LSTM) networks overcome the challenges of traditional RNNs.
• Building a simple LSTM for a sequence modeling task such as time series prediction or text generation.

Capstone Project

• Applying Deep Learning Skills
• Choose between a natural language processing task using NLTK, an image processing task using OpenCV, or a sequence prediction task using RNN/LSTM.
• Implement the project using the techniques learned over the course.
• Presentation of Results
• Summarize the methodology, challenges faced, and the insights gained.
• Demonstrate the practical application of deep learning models in solving real-world problems.

Basic Docker Curriculum for Data Science and Machine Learning

Introduction to Docker

• Overview of Docker*
• Understanding what Docker is and the core concepts behind containers.
• Differences between Docker and traditional virtualization.
• Setting up Docker
• Installing Docker on various operating systems (Windows, macOS, Linux).
• Navigating Docker interfaces (Docker Desktop, Docker CLI).

Docker Basics

• Docker Images and Containers
• Understanding images vs. containers.
• Managing Docker images—pulling from Docker Hub, exploring Dockerfile basics.
• Running Containers
• Starting, stopping, and managing containers.
• Exposing ports, mounting volumes, and linking containers.

Docker Compose and Container Orchestration

• Introduction to Docker Compose
• Benefits of using Docker Compose.
• Writing a docker-compose.yml file for multi-container applications.
• Basic Orchestration* – Understanding the need for orchestration.
• Overview of Docker Swarm mode for managing a cluster of Docker Engines.

Docker for Data Science

• Creating a Data Science Work Environment
• Building a custom Docker image for data science environments.
• Including tools like Jupyter Notebook, RStudio, and popular data science libraries (Pandas, NumPy, Scikit-learn).
• Data Persistence in Containers
• Strategies for managing data in Docker, focusing on non-volatile data storage.

Advanced Docker Applications in Data Science

• • Deploying Machine Learning Models
  • Containerizing machine learning models for consistent deployment.
  • Using Docker containers to deploy a model to a production environment. – *Best Practices and Security
  • Understanding Docker security best practices. – Maintaining and updating data science Docker environments.