Machine Learning
Artificial Intelligence
(4.9 Reviews)
Starting On : 04th Feb, 2025
Internship on Data Science, ML & AI using Python
Course Description
This comprehensive internship program is designed to equip learners with foundational and advanced skills in Data Science, Machine Learning (ML), and Artificial Intelligence (AI) using Python. Participants will gain hands-on experience through practical projects, real-world case studies, and the application of industry-relevant tools such as NumPy, Pandas, Scikit-learn, TensorFlow, and more.
Key Highlights:
- Duration: 90 Hours (9 weeks, 10 hours/week)
- Mode: Online or Offline with recorded lectures (available for 7 days).
- Fee: ₹2700 (Online) | ₹4500 (Offline)
- Eligibility: Open to anyone with an interest in Data Science, ML, and AI.
Why Enroll?
- Learn essential techniques like data preprocessing, feature engineering, and model evaluation.
- Work on real-world projects, including regression, classification, clustering, and deployment of ML models.
- Gain insights into advanced topics like neural networks, deep learning, and AI-powered applications.
- Receive expert guidance and industry-recognized certification upon successful completion.
What you'll learn in this course?
- Data Science Workflow: Structure and manage data analysis projects effectively.
- Python for Data Science: Master libraries like NumPy, Pandas, Matplotlib, and Seaborn for data manipulation and visualization.
- Machine Learning Models: Implement and evaluate regression, classification, and clustering models using Scikit-learn.
- Deep Learning Basics: Build neural networks with TensorFlow/Keras and explore AI-powered applications.
- Data Preprocessing: Handle missing values, perform feature engineering, and optimize datasets for analysis.
- Model Deployment: Deploy ML models using Flask or Streamlit to create interactive web applications.
- Hands-On Projects: Solve real-world problems like loan prediction, customer segmentation, and image classification.
- Industry Relevance: Apply cutting-edge techniques and gain insights from industry experts to stay ahead in the tech field.
Week-Wise Course Modules:
Module 1: Introduction to Data Science and Python for Data Analysis
✔ Data Science Concepts Introduction
✔ Python Libraries Overview (Pandas, NumPy, Matplotlib, Seaborn)
✔ Basic Python Fundamentals (syntax, loops, conditionals)
✔ Data Structures Practice (lists, dictionaries, sets)
✔ Pandas Operations (loading CSV, DataFrame manipulation)
✔ NumPy Basics (arrays, broadcasting, matrix operations)
✔ Project: Dataset Exploration using Titanic or Iris dataset
Module 2: Data Exploration and Visualization
✔ Exploratory Data Analysis (EDA) Process
✔ Descriptive Statistics (mean, median, variance)
✔ Visualization Techniques using Matplotlib and Seaborn
✔ Data Exploration using Pandas
✔ Creating Various Plots (histograms, box plots, scatter plots)
✔ Project: Real-world dataset analysis (admission/sales data)
Module 3: Data Cleaning and Preprocessing
✔ Handling Missing Values and Outliers
✔ Data Transformations Techniques
✔ Missing Data Management with Pandas
✔ Data Scaling Methods (MinMaxScaler, StandardScaler)
✔ Categorical Variable Encoding
✔ Project: Building a Preprocessing Pipeline
Module 4: Introduction to Machine Learning
✔ Supervised Learning Concepts
✔ Machine Learning Workflow
✔ Linear Regression Implementation
✔ Cross-validation Techniques
✔ Train-test Split Methodology
✔ Model Evaluation using MSE
✔ Project: House Price Prediction using Linear Regression
Module 5: Regression Models
✔ Linear vs Polynomial Regression
✔ Ridge and Lasso Regularization
✔ Implementation of Polynomial Regression
✔ Regularization Techniques Practice
✔ Model Comparison and Evaluation
✔ Project: House Price Prediction with Regularization
Module 6: Classification Models
✔ Logistic Regression Implementation
✔ Decision Trees Concepts and Implementation
✔ Random Forests
✔ Binary Classification Practice
✔ Model Performance Evaluation
✔ Project: Loan Prediction using Multiple Classifiers
Module 7: Unsupervised Learning and Clustering
✔ K-means Clustering Concepts
✔ Hierarchical Clustering
✔ Principal Component Analysis (PCA)
✔ Customer Behavior Clustering
✔ Dimensionality Reduction Techniques
✔ Project: Customer Segmentation Implementation
Module 8: Advanced Machine Learning Techniques
✔ Support Vector Machines (SVM)
✔ Gradient Boosting Methods
✔ Ensemble Learning Techniques
✔ XGBoost Implementation
✔ Image Classification with SVM
✔ Project: Ensemble Models Comparison
Module 9: Introduction to Deep Learning
✔ Neural Networks Fundamentals
✔ Backpropagation Concepts
✔ TensorFlow/Keras Implementation
✔ Basic Neural Network Creation
✔ Hyperparameter Optimization
✔ Project: Image Classification using Neural Networks
Module 10: Model Evaluation and Tuning
✔ Cross-validation Methods
✔ Grid Search Implementation
✔ Hyperparameter Tuning Techniques
✔ K-fold Cross-validation Practice
✔ Model Performance Optimization
✔ Project: Classification Model Tuning
Module 11: Model Deployment and Making an AI Product
✔ Flask/Streamlit Deployment
✔ Web App Development
✔ Model Monitoring Implementation
✔ Performance Tracking
✔ Interactive Visualization
✔ Project: Deploying ML Model as Web Service
Module 12: Capstone Project
✔ End-to-End Machine Learning Project
✔ Real-world Problem Solving
✔ Complete Data Science Pipeline Implementation
✔ Solution Presentation
✔ Final Report Submission
Eligibility Criteria
Any one with interest in Data Science, Machine Learning and AI is eligible for this course”
This means:
✔ No specific educational background required
✔ No prior coding experience needed
✔ No age restrictions
✔ No professional experience required
✔ Only requirement is interest in the field of Data Science, ML, and AI
This makes the course accessible to:
- Students
- Working professionals
- Career changers
- Enthusiasts
- Beginners in the field
Python Libraries
✔ NumPy (for numerical operations)
✔ Pandas (for data manipulation)
✔ Matplotlib (for visualization)
✔ Seaborn (for advanced visualization)
✔ Scikit-learn (for machine learning)
✔ TensorFlow/Keras (for deep learning)
✔ Flask/Streamlit (for deployment)
✔ XGBoost (for gradient boosting)
✔ Jupyter Notebook or Google Colab (for interactive coding)
CoE in Chip Design, NIELIT Noida
Course Fee:
2,700
Course Includes:
- Level Intermediate
- Duration 90hr
- Sessions 60
- Mode Online
- Certification Yes
Frequently Asked Questions
Have questions? Find quick and clear answers to the most common queries in our FAQ section. Whether it’s about our programs, services, or initiatives, we’re here to provide the information you need to stay informed.”
Yes, we offer a 10% group discount for a minimum of 5 students or working professionals from the same university, institute, or company.
✔ Data Handling: Pandas, NumPy
✔ Visualization: Matplotlib, Seaborn
✔ Machine Learning: Scikit-learn
✔ Deep Learning: TensorFlow, Keras
✔ NLP: NLTK, SpaCy
✔ Deployment: Flask
Hands-on projects & real-world case studies included! 🚀
Yes, we offer a two-part installment option for the course fees. For more details, please refer to the registration page.
