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.

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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

📌 Courses:

• IBM Data Science Professional Certificate (Coursera)

• Introduction to Machine Learning (Udacity)

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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)

📌 Courses:

• Python for Data Science (DataCamp)

• Version Control with Git (Udacity)

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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)

📌 Courses:

• Python for Data Science and Machine Learning Bootcamp (Udemy)

• Data Manipulation with Pandas (DataCamp)

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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

📌 Courses:

• Feature Engineering for Machine Learning (Coursera)

• Data Cleaning in Python (DataCamp)

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5. Exploratory Data Analysis (EDA)

• Understanding Data Distributions

• Visualization Techniques (Matplotlib, Seaborn, Plotly)

• Correlation Analysis

• Hypothesis Testing

📌 Courses:

• Data Visualization with Python (Coursera)

• Exploratory Data Analysis in Python (DataCamp)

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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

📌 Courses:

• SQL for Data Science (Coursera)

• Advanced SQL for Data Analysis (Mode Analytics)

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7. Machine Learning Basics

• Understanding ML Workflow

• Supervised vs. Unsupervised Learning

• Model Selection and Evaluation Metrics

• Bias-Variance Tradeoff

📌 Courses:

• Machine Learning by Andrew Ng (Coursera)

• Supervised Machine Learning: Regression and Classification (Coursera)

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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)

📌 Courses:

• Supervised Learning with scikit-learn (DataCamp)

• Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (Book)

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9. Unsupervised Learning

• Clustering (K-Means, DBSCAN, Hierarchical)

• Dimensionality Reduction (PCA, t-SNE)

📌 Courses:

• Unsupervised Learning in Python (DataCamp)

• Applied Unsupervised Learning (Coursera)

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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

📌 Courses:

• Deep Learning Specialization by Andrew Ng (Coursera)

• TensorFlow for Deep Learning (Udacity)

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11. Natural Language Processing (NLP)

• Text Preprocessing (Tokenization, Lemmatization, Stopwords)

• Sentiment Analysis & Named Entity Recognition

• Using Pretrained Models (BERT, GPT)

📌 Courses:

• Natural Language Processing with Deep Learning (Stanford)

• NLP with Python (DataCamp)

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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)

📌 Courses:

• Machine Learning Model Deployment (Udemy)

• Serverless Machine Learning with AWS Lambda (DataCamp)

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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

📌 Courses:

• End-to-End Machine Learning Projects (Udemy)

• Practical Data Science Specialization (Coursera)

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14. Best Practices in Data Science

• Model Interpretability (SHAP, LIME)

• Data Science Ethics & Bias in AI

• Writing Reproducible Code & Documentation

📌 Courses:

• Interpretable Machine Learning (Book)

• Data Science Ethics (Coursera)

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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

📌 Courses:

• Data Science Career Guide (Udacity)

• Cracking the Data Science Interview (Book)

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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. 🚀