AI and Machine Learning Model Deployment Internship Program
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AI & Machine Learning Model Deployment Internship Program: 6-Week Structured Learning and Experience
Introduction
Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries by enabling systems to learn, adapt, and make intelligent decisions. This internship program offers a hands-on journey through AI/ML fundamentals, model building, and real-world deployment. Participants will gain exposure to key tools such as Python, Scikit-learn, TensorFlow, PyTorch, Flask, and Streamlit, developing practical skills in data preprocessing, model training, evaluation, and deployment.
Designed for aspiring data scientists and ML engineers, the program bridges theoretical understanding with practical application. Each week culminates in tangible outcomes, including deployed models and real-time AI applications, preparing participants for real-world challenges in the AI/ML landscape.
Program Highlights
Week 1: Foundations of AI & ML
· Introduction to AI & ML: Understand core concepts, ML types (supervised, unsupervised, reinforcement), and use cases.
· Environment Setup: Install Python, Jupyter, TensorFlow, PyTorch, Scikit-learn for development.
· Deliverables:
1. 500-word report on AI/ML basics with 3 real-world applications.
2. Screenshots of environment setup with a short guide.
Week 2: Data Handling & Analysis
· Data Collection & Preprocessing: Load datasets (e.g., Iris/Titanic), perform cleaning, normalization, encoding.
· Exploratory Data Analysis (EDA): Use visualizations to understand feature relationships and data patterns.
· Deliverables:
- Jupyter Notebook with preprocessing code and EDA visuals.
- 200-word summary of insights from EDA.
Week 3: Model Development in Scikit-learn
· Model Building: Create classification or regression models (e.g., Decision Tree, Logistic Regression).
· Model Evaluation: Use accuracy metrics and confusion matrix for performance assessment.
· Deliverables:
1. Notebook with model training and evaluation results.
Week 4: Neural Networks with TensorFlow & PyTorch
· TensorFlow Model: Build and train a neural network (e.g., MNIST dataset) using TensorFlow/Keras.
· PyTorch Model: Implement the same model in PyTorch and compare both frameworks.
· Deliverables:
1. Notebooks for TensorFlow and PyTorch models.
2. Accuracy/loss plots and 100-word comparison.
Week 5: Model Optimization & Deployment Introduction
· Hyperparameter Tuning: Apply GridSearchCV and cross-validation for model optimization.
· Deployment Methods: Research and compare Flask, FastAPI, Streamlit, etc.
· Model Serialization: Learn to save/load models using joblib, pickle, or model.save().
· Deliverables:
1. Notebook demonstrating tuning and serialization.
2. 400-word report on deployment methods.
Week 6: Real-Time Model Deployment & Projects
· Flask Deployment: Deploy a Scikit-learn model with a simple web interface.
· Streamlit Deployment: Deploy a TensorFlow model with an interactive UI.
· Real-Time Applications:
1. Chatbot: Build and deploy a rule-based or ML-powered chatbot.
2. Recommendation System: Develop and demonstrate a movie/product recommender.
3. Predictive Analytics: Build and deploy a model for sales prediction or stock trend analysis.
· Deliverables:
1. Deployed apps (Flask/Streamlit), source code, screenshots/demos.
2. Short explanations (200-300 words) of approach and implementation.
Expected Outcomes
By the end of this internship, participants will:
· Understand AI/ML fundamentals and real-world applications.
· Gain hands-on experience with tools like Scikit-learn, TensorFlow, and PyTorch.
· Master data preprocessing, EDA, and model evaluation techniques.
· Build, tune, and serialize models for varied ML tasks.
· Explore and implement model deployment using Flask and Streamlit.
· Develop real-time AI applications such as chatbots, recommendation systems, and predictive analytics tools.
· Create a project portfolio showcasing model building, deployment, and real-time implementation.
Requirements
Laptop
Internet Connection
Anaconda navigator
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To understand the foundational concepts of Artificial Intelligence and Machine Learning and explore their applications in real-world scenarios.
To install and configure essential tools for developing AI/ML models.
To learn how to load, clean, and preprocess data for ML models.
To explore datasets visually and statistically to uncover insights.
To build and evaluate a machine learning model using Scikit-learn.
To implement and train a neural network using TensorFlow/Keras.
To build the same model in PyTorch and compare with TensorFlow.
To enhance model performance through hyperparameter tuning.
To explore ways of deploying ML models for end-user access.
To deploy a trained ML model using Flask as a web app.
To deploy an ML model using Streamlit with interactive UI.
To learn how to save and reuse trained models.
To develop a basic chatbot using rule-based or ML techniques.
To create a system that suggests items based on user input.
To build a predictive model for forecasting future values.
