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Artificial Intelligence & Machine Learning

The AI & ML Internship Program is designed to give you practical, hands-on experience in developing intelligent systems using machine learning and deep learning. You’ll learn how to work with data, build predictive models, train neural networks, and apply industry tools like Scikit-learn, TensorFlow/Keras, Pandas, and more. The program also introduces version control (Git) and model deployment using Flask or Streamlit.

By the end of the internship, you'll be able to:

  • Build machine learning models for real-world datasets
  • Apply deep learning techniques for image or text data
  • Understand end-to-end AI/ML project workflows
  • Collaborate using Git and deploy models using simple web frameworks

 

Duration: 6 Weeks (6-8 hrs/week)                         Learning Format: Live/Online/Interactive  

Course Overview:

This internship provides a beginner-friendly, project-based learning experience in AI & ML. Interns gain hands-on exposure to supervised and unsupervised learning, neural networks, and model evaluation using Python based tools. The curriculum is designed to help interns transition into real-world data science and ML engineering roles, with a strong focus on practical skills and deployment-readiness.

Skills Developed:

  • Python for data analysis and machine learning
  • Data cleaning, preprocessing, and visualization
  • Supervised learning: regression, classification
  • Unsupervised learning: clustering, dimensionality reduction
  • Neural networks and CNNs using TensorFlow/Keras
  • Model evaluation and tuning (cross-validation, confusion matrix)
  • Version control using Git and GitHub
  • Model deployment with Flask/Streamlit
  • Resume building, mock interviews, and LinkedIn optimization

Course Curriculum

Duration: 6 Weeks

Level: Beginner to Intermediate

Goal: Equip interns with practical AI/ML skills for entry-level roles in data science or machine learning.

Python & Data Analysis for AI/ML

  • Python basics, data types, loops, functions
  • Libraries: NumPy, Pandas, Matplotlib, Seaborn
  • Exploratory Data Analysis (EDA)
  • Introduction to Jupyter and Google Colab

Outcome: Develop basic programming and data analysis skills.

 

Statistics & Supervised Learning

  • Probability, mean/median/mode, standard deviation
  • Linear & Logistic Regression
  • Train-test split, cross-validation
  • Model evaluation metrices

Outcome: Build your first regression/classification models and analyse performance.

 

Classification Models & Unsupervised Learning

  • Decision Trees, KNN, SVM
  • Clustering: K-Means, Hierarchical
  • Dimensionality Reduction: PCA
  • Use-case: Customer Segmentation

Outcome: Apply ML to classification and clustering problems.

 

Neural Networks & Deep Learning

  • Basics of Neural Networks
  • Activation functions, backpropagation
  • Convolutional Neural Networks (CNNs)
  • Frameworks: TensorFlow and Kera’s

Outcome: Train deep learning models for image or text data.

Capstone Project & Deployment

  • Full-cycle ML project: data? model? evaluation
  • Model serialization and saving (Pickle/ joblib)
  • Simple model deployment with Flask or Streamlet
  • Project presentation preparation 

Outcome: Build, evaluate, and deploy a complete AI/ML solution.

 

HR & Career Skills Week

  • Professional Communication & Email Etiquette
  • Interview Prep & Mock Interviews
  • LinkedIn & Resume Optimization
  • Workplace Ethics & Time Management
  • HR Session: Understanding Roles in Tech Teams

Outcome: Build soft skills and career readiness for entry-level Salesforce roles.

 

₹6999   ₹10000 (30% off) Add to Cart

Course FAQs

A: No prior experience is required. The course starts with Python basics.

A: Yes. A certificate is awarded upon successful completion of the internship.

A: Yes. Interns complete a capstone project and multiple hands-on exercises.

A: Yes. Week 6 includes resume reviews, mock interviews, and career guidance.
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