AI & Machine Learning Internship ProgramDataWithCSDataWithCS

Name: AI & Machine Learning Internship Program
Availability: InStock

The AI & Machine Learning Internship Program at DataWithCS is designed to provide hands-on experience in data preparation, exploratory data analysis, machine learning algorithms, model evaluation, and AI application development. Interns will work on practical industry-oriented projects using Python, Pandas, NumPy, Scikit-learn, and visualization tools while developing skills in data preprocessing, feature engineering, predictive modeling, and performance evaluation. The program emphasizes practical implementation, problem-solving, project documentation, and portfolio development to help learners understand how AI and Machine Learning are applied to real-world business problems.

Highlights

Hands-on experience with AI and Machine Learning workflows.
Real-world dataset analysis and exploratory data analysis (EDA).
Data cleaning, preprocessing, and feature engineering.
Implementation of regression, classification, and clustering algorithms.
Machine learning model training, evaluation, and optimization.
Introduction to Deep Learning and Generative AI.
Industry-oriented case studies and practical assignments.
Guided projects with mentor feedback.
GitHub portfolio development and project documentation.
Final capstone project with practical implementation and presentation.

Learning Outcomes

Python for AI and Machine Learning
NumPy, Pandas, and Data Manipulation
Exploratory Data Analysis and Data Visualization
Data Cleaning and Preprocessing
Statistics and Mathematics for Machine Learning
Supervised Learning — Regression and Classification
Unsupervised Learning — Clustering and Dimensionality Reduction
Feature Engineering and Feature Selection
Model Training, Evaluation, and Hyperparameter Tuning
Introduction to Neural Networks and Deep Learning
Introduction to Generative AI and LLM Applications
End-to-End AI/ML Projects and Model Deployment

AI, ML, and Deep Learning fundamentals
industry applications
ML workflow

Python revision, functions, loops, data structures, and working with libraries

Arrays, DataFrames, data loading, filtering, aggregation, and transformation

Understanding datasets, descriptive statistics, data visualization, and insights

Missing values, duplicates, outliers, encoding, and feature scaling

Mean, median, variance, distributions, correlation, and basic probability

Features, labels, train-test split, regression, and classification concepts

Build regression models, interpret coefficients, and predict numerical values

Logistic regression, decision trees, and practical classification

MAE, MSE, RMSE, accuracy, precision, recall, F1-score, and confusion matrix

Clustering concepts and introduction to K-Means

Feature creation, categorical encoding, feature selection, and data leakage

Cross-validation, hyperparameter tuning, overfitting, and underfitting

Define a business problem, select a dataset, and perform EDA and preprocessing

Train models, evaluate results, compare performance, and select a model

Neural network fundamentals, layers, activation functions, and deep learning use cases

LLM fundamentals, prompting, AI applications, and responsible AI basics

Introduction to Streamlit, creating a simple ML application, and GitHub project publishing

Implement an AI/ML solution, document the workflow, and prepare project presentation

Capstone demonstration, mentor feedback, project explanation, and interview preparation

Aman Raj

Senior Data Science and Gen AI Trainer

Data Science
Generative AI
Machine Learning
Deep Learning
Python
SQL
Natural Language Processing (NLP)
Large Language Models (LLMs)

5+ years experience

Aman Raj is a Senior Data Science & Generative AI Trainer with 5+ years of experience in the AI domain. An IIIT Bangalore alumnus and former Navy Tech Mentor, he brings a strong blend of technical expertise and practical industry-oriented learning. His areas of expertise include Data Science, Machine Learning, Generative AI, Large Language Models, Python, and AI-driven applications. He focuses on helping learners build practical skills and develop a strong foundation for careers in AI and Data Science.

It is a practical, project-based internship designed to help learners understand AI/ML concepts and apply them to real-world datasets and business problems.

Students, fresh graduates, aspiring Data Analysts, Python developers, and working professionals interested in Artificial Intelligence and Machine Learning can apply.

Basic Python knowledge is recommended. Learners should be comfortable with fundamental programming concepts to get the most out of the practical sessions.

You will learn data preprocessing, exploratory data analysis, feature engineering, regression, classification, clustering, model training, evaluation, and introductory Deep Learning and Generative AI.

The internship may cover Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, SQL, Jupyter Notebook, Streamlit, Git, and GitHub.

Yes. The program focuses on practical assignments and projects, such as customer churn prediction, sales forecasting, and customer segmentation, using relevant datasets.

The recommended duration is 4-8 weeks, depending on the program structure, learner experience, and project requirements. Confirm the current batch schedule with DataWithCS.

Certificate eligibility depends on completing the internship requirements established by DataWithCS, including applicable assignments and project submissions.

Yes. Project documentation, code, and GitHub repositories can help you demonstrate your practical skills to prospective employers, subject to project guidelines and data permissions.

Visit the DataWithCS website or contact the team to learn about upcoming batches, fees, eligibility, schedules, and enrollment procedures.

Ready to start AI & Machine Learning Internship Program?

Talk to a counselor and find out which batch fits your schedule and goals.

Enroll Now Back to Courses

Highlights

Hands-on experience with AI and Machine Learning workflows.
Real-world dataset analysis and exploratory data analysis (EDA).
Data cleaning, preprocessing, and feature engineering.
Implementation of regression, classification, and clustering algorithms.
Machine learning model training, evaluation, and optimization.
Introduction to Deep Learning and Generative AI.
Industry-oriented case studies and practical assignments.
Guided projects with mentor feedback.
GitHub portfolio development and project documentation.
Final capstone project with practical implementation and presentation.

Learning Outcomes

Python for AI and Machine Learning
NumPy, Pandas, and Data Manipulation
Exploratory Data Analysis and Data Visualization
Data Cleaning and Preprocessing
Statistics and Mathematics for Machine Learning
Supervised Learning — Regression and Classification
Unsupervised Learning — Clustering and Dimensionality Reduction
Feature Engineering and Feature Selection
Model Training, Evaluation, and Hyperparameter Tuning
Introduction to Neural Networks and Deep Learning
Introduction to Generative AI and LLM Applications
End-to-End AI/ML Projects and Model Deployment

AI, ML, and Deep Learning fundamentals
industry applications
ML workflow

Python revision, functions, loops, data structures, and working with libraries

Arrays, DataFrames, data loading, filtering, aggregation, and transformation

Understanding datasets, descriptive statistics, data visualization, and insights

Missing values, duplicates, outliers, encoding, and feature scaling

Mean, median, variance, distributions, correlation, and basic probability

Features, labels, train-test split, regression, and classification concepts

Build regression models, interpret coefficients, and predict numerical values

Logistic regression, decision trees, and practical classification

MAE, MSE, RMSE, accuracy, precision, recall, F1-score, and confusion matrix

Clustering concepts and introduction to K-Means

Feature creation, categorical encoding, feature selection, and data leakage

Cross-validation, hyperparameter tuning, overfitting, and underfitting

Define a business problem, select a dataset, and perform EDA and preprocessing

Train models, evaluate results, compare performance, and select a model

Neural network fundamentals, layers, activation functions, and deep learning use cases

LLM fundamentals, prompting, AI applications, and responsible AI basics

Introduction to Streamlit, creating a simple ML application, and GitHub project publishing

Implement an AI/ML solution, document the workflow, and prepare project presentation

Capstone demonstration, mentor feedback, project explanation, and interview preparation

Aman Raj

Senior Data Science and Gen AI Trainer

Data Science
Generative AI
Machine Learning
Deep Learning
Python
SQL
Natural Language Processing (NLP)
Large Language Models (LLMs)

5+ years experience

AI & Machine Learning Internship Program

Course Overview

Highlights

Learning Outcomes

Course Curriculum

Meet your instructor

Aman Raj

Frequently Asked Questions

Ready to start AI & Machine Learning Internship Program?

AI & Machine Learning Internship Program

Course Overview

Highlights

Learning Outcomes

Course Curriculum

Meet your instructor

Aman Raj

Frequently Asked Questions

Ready to start AI & Machine Learning Internship Program?