Career Development

Artificial Intelligence – Full Stack Internship Program

📆 Duration: 6 Months | 🌐 Mode: Online / Offline / Hybrid | 🎓 Level: Beginner to Intermediate

👥 Eligibility: UG/PG Students, Freshers, IT Professionals

6-Month Internship Program Modules

Module 1: Python Programming & Math for AI

Week 1: Python Basics & Programming Foundations

Day 1: Python, IDE setup
Day 2: Variables, Data Types
Day 3: Conditions (if/else)
Day 4: Loops (for/while)
Day 5: Functions, Lambda

Week 2: Advanced Python Concepts

Day 6: Lists, Tuples, Sets, Dict
Day 7: Strings & Functions
Day 8: File & Exception Handling
Day 9: Classes & Objects
Day 10: Inheritance, OOP Principles

Week 3: Data Handling with Python

Day 11: NumPy Basics
Day 12: Pandas – DataFrames
Day 13: Cleaning Data
Day 14: Matplotlib
Day 15: Seaborn Visuals

Week 4: Math for AI

Day 16: Vectors, Matrices
Day 17: Matrix Operations
Day 18: Stats – Mean, Median
Day 19: Distributions
Day 20: Hypothesis Testing

Module 2: Data Science & EDA

Week 1:Data Science Fundamentals & Data Handling

Day 1:Introduction to Data Science Lifecycle & Roles
Day 2: Data Types, Sources, and Importing Data (CSV, Excel, APIs)
Day 3: Data Cleaning – Handling Missing Data, Nulls
Day 4: Data Transformation – Type Conversion, Scaling
Day 5:Outlier Detection and Handling (Z-score, IQR)

Week 2: Pandas, NumPy & EDA

Day 6: Pandas – DataFrames, Filtering, Sorting
Day 7:GroupBy, Aggregation, Merging & Joins
Day 8: NumPy Operations – Statistical Functions, Broadcasting
Day 9: Data Encoding – Label Encoding, One-Hot Encoding
Day 10: Feature Engineering – Creating New Features

Week 3: Data Visualization

Day 11: Introduction to Data Visualization Principles
Day 12: Matplotlib Line, Bar, Scatter, Pie Charts
Day 13: Seaborn Histograms, Boxplots, Violin plots
Day 14: Correlation Heatmaps&Pairplots
Day 15: Plotly Basics – Interactive Charts

Week 4: Exploratory Data Analysis (EDA) Projects

Day 16: Exploratory Data Analysis – Strategy & Checklist
Day 17: Mini Project – EDA on Real Dataset (e.g., Titanic, Iris)
Day 18: Visualization Storytelling – Explaining insights
Day 19: Report Building – Summary, Recommendations
Day 20: Project Submission + Feedback Session

Module 3: Machine Learning (ML)

Week 1:Introduction to Machine Learning

Day 1: Introduction to ML – Types (Supervised, Unsupervised, Reinforcement)
Day 2: ML Workflow – Data Splitting, Preprocessing, Model Training
Day 3: Supervised Learning – Linear Regression (Theory + Code)
Day 4: Polynomial Regression & Model Evaluation Metrics (R², MAE, MSE)
Day 5: : Logistic Regression – Classification Basics

Week 2: Supervised Learning Algorithms

Day 6: Decision Trees – Gini, Entropy, Overfitting Control
Day 7: Random Forest – Ensemble Learning
Day 8: Support Vector Machines (SVM)
Day 9: K-Nearest Neighbors (KNN)
Day 10: Hands-on Mini Project: Classification Use Case (e.g., Loan Prediction)

Week 3: Unsupervised Learning

Day 11: Introduction to Clustering – Use Cases
Day 12: K-Means Clustering – Theory + Implementation
Day 13: Hierarchical Clustering – Dendrograms
Day 14: Dimensionality Reduction – PCA
Day 15: : Anomaly Detection Techniques

Week 4:Model Tuning & Project

Day 16: Cross-Validation & Train-Test Split Techniques
Day 17: Hyperparameter Tuning – Grid Search, Random Search
Day 18: Introduction to Scikit-learn Pipeline
Day 19: Final ML Project – End-to-End Pipeline (EDA → Model → Evaluation)
Day 20: Project Demo, Peer Review, and Feedback

Module 4: Deep Learning & Neural Networks

Week 1: Fundamentals of Deep Learning

Day 1: What is Deep Learning? Difference from ML, Real-world applications
Day 2: Neural Network Architecture – Neurons, Weights, Bias, Activation
Day 3: Feedforward&Backpropagation Algorithms
Day 4: Loss Functions & Optimizers (MSE, CrossEntropy, SGD, Adam)
Day 5: Introduction to TensorFlow&Keras – Setup & First Neural Net

Week 2:Artificial Neural Networks (ANN)

Day 6: Building ANN with Keras – Input, Hidden, Output Layers
Day 7: Training & Evaluating ANN Models
Day 8: Overfitting, Underfitting – Dropout, Regularization
Day 9: Hands-on: Classification with ANN (e.g., MNIST Digits)
Day 10: Mini Project 1: Binary Classification (e.g., Customer Churn)

Week 3: Convolutional Neural Networks (CNN)

Day 11: CNN Basics – Kernels, Filters, Pooling, Flattening
Day 12: Building CNNs for Image Recognition using Keras
Day 13: Data Augmentation & Image Preprocessing
Day 14:Hands-on: Image Classification with CNN (e.g., CIFAR-10)
Day 15: Mini Project 2: Custom Image Classification

Week 4: Recurrent Neural Networks (RNN) + Project

Day 16: RNN Basics – Sequence Modeling, Time Series Data
Day 17: LSTM Networks – Architecture & Use Cases
Day 18: NLP with RNN/LSTM (e.g., Sentiment Analysis)
Day 19: Final Deep Learning Capstone Project (Choose from CV/NLP)
Day 20: Project Presentation + Peer Review + Feedback

Module 5: NLP & AI Tools

Week 1: Basics of NLP

Day 1: Introduction to NLP – Applications & Workflow
Day 2: Text Preprocessing – Tokenization, Lowercasing, Stopwords
Day 3: Stemming vs Lemmatization (NLTK &SpaCy)
Day 4: POS Tagging, Named Entity Recognition (NER)
Day 5: Hands-on: Text Preprocessing with NLTK &SpaCy

Week 2: Advanced Python Concepts

Day 6: Feature Extraction & Traditional NLP Models
Day 7: Text Classification using Naive Bayes
Day 8: Logistic Regression & SVM for NLP
Day 9: Sentiment Analysis Project (e.g., IMDB/YouTube Reviews)
Day 10: Mini Project: Spam Detection / News Categorization

Week 3: Deep Learning for NLP

Day 11: Word Embeddings – Word2Vec, GloVe Concepts
Day 12: Intro to Transformers & BERT
Day 13: Text Classification using LSTM
Day 14: Chatbot Basics – Rule-based vs ML-based
Day 15: Build a Simple Chatbot with Python & NLP libraries

Week 4: Math for AI

Day 16: Introduction to Hugging Face & Transformers
Day 17: Using Pretrained Models for Summarization, Translation
Day 18: AI Tools: LangChain, AutoGPT, Prompt Engineering Basics
Day 19: AI Use Case Project – Choose from Resume Parser, FAQ Bot, etc.
Day 20: Capstone NLP Project Presentation + Evaluation

Module 6: Project + Deployment + Placement & Career Prep

Week 1: Capstone Project Planning & Development

Day 1: Introduction to Capstone Projects – Real-World Problem Scenarios
Day 2: Dataset Selection & Problem Statement Finalization
Day 3: Data Preprocessing & EDA Review
Day 4:Model Building (ML or DL-based depending on use case)
Day 5:Performance Tuning, Metrics, Final Model Selection

Week 2:Deployment & Tools

Day 6: Introduction to Model Deployment – Concepts & Workflow
Day 7: Building a Flask/FastAPI Web App for Model Serving
Day 8: Frontend Integration – Streamlit / HTML Template Basics
Day 9: Hosting – Using Render / Railway / GitHub + Heroku (or AWS basics)
Day 10: Project Finalization, Demo & Debugging

Week 3: Placement & Career Preparation

Day 11:Resume Building – ATS Friendly Resume with Keywords
Day 12:LinkedIn Optimization & Branding (Live profile review)
Day 13:GitHub Portfolio Setup – Showcasing Projects Professionally
Day 14:Mock Interviews (Tech + HR Round)
Day 15: Common Interview Questions – Data Science & AI Roles

Week 4:Soft Skills + NLP-Based Personality Training

Day 16: Communication & Confidence Building Techniques
Day 17: Group Discussion Practice & Feedback
Day 18: Personality Analysis using NLP Tools – Self Report & Correction
Day 19: Final Project Presentation (Industry Panel / Mentor Review)
Day 20: Internship Completion Ceremony + Certificate & Placement Drive Info

🧠 Add-ons & Exclusive Benefits

🗣️

Personality Development

Enhance communication, confidence, and mindset with NLP techniques.

💻

Dev Tools Training

Learn Git, GitHub & VS Code for effective code versioning and collaboration.

🌐

Portfolio Website

Get a professionally designed personal portfolio hosted online.

📝

Experience Letter

Receive an internship letter + strong recommendation from mentors.

🔁

Lifetime Mentorship

Ongoing support and guidance even after the internship ends.

🎯

Placement Drives

Get priority access to InfinityMind’s exclusive hiring drives & job referrals.

Artificial Intelligence – Full Stack Internship Program

Personality Development

Dev Tools Training

Portfolio Website

Experience Letter

Lifetime Mentorship

Placement Drives

🏆 Certificate Milestones

✅ Internship Certificate

📁 Project Completion Certificate

🌟 Letter of Recommendation (LOR)

🚀 Ready to Start Your Artificial Intelligence Journey?