Artificial Intelligence with Machine Learning Internship

AI fundamentals, applications and industry use cases.

Core ML concepts, workflows and learning approaches.

Python programming for AI and machine learning.

Arrays, numerical operations and mathematical computing.

Data loading, cleaning, transformation and analysis.

Charts, plots and communicating data insights.

Probability, distributions, correlation and statistical thinking.

Discover patterns, outliers and useful relationships in datasets.

Missing values, duplicates, inconsistent formats and data quality.

Create useful variables for better model performance.

Understand labelled data and predictive learning workflows.

Explore clustering, dimensionality reduction and pattern discovery.

Build and evaluate regression models.

Classification fundamentals and probability-based predictions.

Tree-based modelling and interpretability.

Ensemble learning and robust classification or regression.

Margins, kernels and classification concepts.

Similarity-based prediction and classification.

Segment data using unsupervised learning.

Dimensionality reduction and feature compression.

Accuracy, precision, recall, F1-score, MAE and RMSE.

Assess model reliability and reduce evaluation bias.

Improve models with systematic parameter search.

Sampling strategies and suitable evaluation metrics.

Organize preprocessing and modelling into repeatable workflows.

Understand feature importance and explain predictions.

Neurons, layers, activations and backpropagation.

Build and train introductory deep learning models.

Model building, training, validation and callbacks.

Tensors, datasets, models and training loops.

Image features and visual pattern recognition.

Develop a basic image classification workflow.

Image processing, augmentation and vision applications.

Understand bounding boxes and detection pipelines.

Text preprocessing, tokenization and language data.

Build a text classification project.

Represent language as vectors for semantic tasks.

Understand attention and modern language models.

LLM concepts, capabilities, limitations and use cases.

Design clear prompts for reliable AI-assisted workflows.

Explore text, image and assistant-based applications.

Connect language models with relevant external knowledge.

Plan conversational flows and practical chatbot features.

Connect applications with model inference services.

Build similarity-based or collaborative recommendation concepts.

Explore trends, forecasting and sequential data.

Identify unusual patterns in data.

Serve models through practical application interfaces.

Create simple interactive AI and data applications.

Understand API-based model serving.

Version control, repositories and project documentation.

Explore reproducibility, monitoring and model lifecycle concepts.

Bias, privacy, safety and responsible development.

Define objectives, datasets, milestones and deliverables.

Explain methods, results, limitations and future improvements.

Prepare project evidence, documentation and interview discussion points.