Internship Onboarding 2025
Below is a structured GitBook Content Outline for your Internship Training Materials in Data Science and Machine Learning. This will help interns build the necessary skills before starting a project.
GitBook: Data Science & Machine Learning Internship Training
1. Introduction to Data Science and Machine Learning
Overview of Data Science & ML
Role of a Data Science & ML Engineer
Applications in real-world projects
Required Tools & Technologies
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2. Setting Up the Environment
Installing Python & Jupyter Notebook
Overview of Anaconda, VS Code, and Google Colab
Introduction to Git, GitHub, and Version Control
Setting up Virtual Environments (venv, conda)
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3. Python for Data Science
Python Basics (Variables, Data Types, Control Flow)
Functions, Lambda, and List Comprehensions
Working with Libraries (NumPy, Pandas, Matplotlib, Seaborn)
File Handling (CSV, JSON, Excel, SQL)
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4. Data Wrangling and Preprocessing
Handling Missing Values
Data Cleaning and Formatting
Feature Engineering and Selection
Encoding Categorical Variables
Handling Outliers and Scaling Data
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5. Exploratory Data Analysis (EDA)
Understanding Data Distributions
Visualization Techniques (Matplotlib, Seaborn, Plotly)
Correlation Analysis
Hypothesis Testing
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6. SQL for Data Science
Basics of SQL (Select, Insert, Update, Delete)
Joins, Subqueries, and Aggregations
Writing Efficient Queries for Large Datasets
Using SQL for Data Analysis
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7. Machine Learning Basics
Understanding ML Workflow
Supervised vs. Unsupervised Learning
Model Selection and Evaluation Metrics
Bias-Variance Tradeoff
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8. Supervised Learning Algorithms
Regression (Linear, Ridge, Lasso)
Classification (Logistic Regression, Decision Trees, Random Forest, XGBoost, SVM, k-NN)
Model Tuning (Hyperparameter Optimization)
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9. Unsupervised Learning
Clustering (K-Means, DBSCAN, Hierarchical)
Dimensionality Reduction (PCA, t-SNE)
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10. Deep Learning Basics
Introduction to Neural Networks
Basics of TensorFlow and PyTorch
Building a Simple Neural Network
Image Classification with CNNs
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11. Natural Language Processing (NLP)
Text Preprocessing (Tokenization, Lemmatization, Stopwords)
Sentiment Analysis & Named Entity Recognition
Using Pretrained Models (BERT, GPT)
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12. Model Deployment
Introduction to Flask & FastAPI for Model Deployment
Deploying ML Models using Streamlit
Working with Docker & Cloud Deployment (AWS, GCP)
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13. Real-World Projects & Case Studies
Sentiment Analysis on Twitter Data
Customer Segmentation for a Retail Business
Predicting Loan Default using Credit Risk Data
Demand Forecasting for an E-commerce Company
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14. Best Practices in Data Science
Model Interpretability (SHAP, LIME)
Data Science Ethics & Bias in AI
Writing Reproducible Code & Documentation
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15. Resume & Portfolio Building
Creating an Impressive LinkedIn & GitHub Profile
Writing a Strong Resume for Data Science Roles
Preparing for Technical Interviews
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This structured roadmap with courses ensures that you will gain strong theoretical knowledge and hands-on experience before working on projects. 🚀
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