AI/ML Internship

Small Project: Spam Email Detection using Naive Bayes

In this project, students will develop a machine learning model to classify emails as spam or not spam using the Naive Bayes algorithm. This project will teach them how to work with textual data, clean it, and implement a simple classification model.

Tools & Technologies:

• Python for programming
• NLTK or Scikit-learn for text preprocessing
• Scikit-learn for implementing the Naive Bayes classifier
• Jupyter Notebook for running the project

Process:

• Data Collection: Download a dataset of emails, such as the SpamAssassin public dataset.
• Data Cleaning: Preprocess the email text (tokenization, lowercasing, removing stop words, etc.).
• Feature Extraction: Use techniques like Bag of Words or TF-IDF to convert the text into numerical features.
• Model Building: Train a Naive Bayes model on the processed data.
• Model Evaluation: Test the model on unseen data and measure its accuracy using precision, recall, and F1-score.

Price: ₹200

Big Project: Object Detection in Images using YOLO

In this project, students will implement an object detection system using the YOLO (You Only Look Once) deep learning algorithm, known for its speed and accuracy in detecting multiple objects within an image.

Tools & Technologies:

• Python for programming
• OpenCV for image processing and displaying results
• TensorFlow or PyTorch for deep learning
• YOLO pre-trained models (YOLOv3 or YOLOv4)

Process:

• Data Preparation: Use a pre-labeled dataset like COCO or prepare your own dataset by labeling images for object detection.
• Model Setup: Load a pre-trained YOLO model using TensorFlow or PyTorch.
• Image Preprocessing: Feed images into the YOLO model, ensuring the correct dimensions and normalization.
• Object Detection: Run the YOLO model to detect objects in images, outputting bounding boxes and class labels.
• Post-Processing: Use Non-Max Suppression (NMS) to remove redundant detections.

Price: ₹500 (With Mentorship)

Data Analyst Internship

Small Project: Sales Dashboard Using Power BI

In this project, students will create a dynamic sales dashboard using Power BI to visualize sales data for a fictional retail company. The goal is to analyze sales performance over time, identify trends, and provide insights to improve business decisions.

Tools & Technologies:

• Power BI Desktop for data visualization and dashboard creation
• Excel for data cleaning and manipulation

Process:

• Data Collection: Provided CSV file containing sales data.
• Data Cleaning: Preprocess the data in Power BI.
• Dashboard Creation: Use Power BI to create interactive visualizations, such as bar charts and line graphs.
• Insights Generation: Summarize key findings in a brief report.

Price: ₹200

Big Project: Data Analysis and Visualization for E-Commerce Business

In this comprehensive project, students will conduct a full data analysis of an e-commerce dataset, including data collection, storage, EDA, and visualization.

Tools & Technologies:

• Python (Pandas, Matplotlib, Seaborn) for EDA
• SQL for data storage
• Power BI for visualization

Process:

• Data Collection: Gather data from web scraping or Kaggle datasets.
• Data Storage: Use SQL to store the collected data.
• EDA: Use Python for descriptive statistics and visualizations.
• Visualization: Create interactive dashboards in Power BI.
• Final Report: Summarize analysis and insights in a detailed report.

Price: ₹500 (With Mentorship)

Data science Internship

Small Project: Customer Segmentation using K-Means Clustering

This project focuses on segmenting customers based on their behavior and attributes, which helps businesses target specific customer groups with personalized marketing strategies.

Tools & Technologies:

• Python (Pandas, Matplotlib, Seaborn, Scikit-learn)

Process:

• Data Collection: Use datasets like the Mall Customers dataset.
• EDA: Explore relationships between customer attributes.
• K-Means Clustering: Apply clustering algorithms and visualize the results.

Price: ₹200

Big Project: Image Classification using CNN

This project involves building an image classification model using Convolutional Neural Networks (CNNs), a popular technique in deep learning for image recognition tasks.

Tools & Technologies:

• Python (TensorFlow or Keras, Matplotlib, NumPy)
• Datasets: CIFAR-10 or Fashion MNIST

Process:

• Data Preprocessing: Normalize pixel values and split the data.
• Building the CNN: Design the model architecture with layers like convolutional and pooling layers.
• Training and Evaluation: Compile, train, and evaluate the model.

Price: ₹500 (With Mentorship)