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Data Science

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Data Science

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  1. Prepared By ITSOLERA AI Department

  2. Week 1-2: Introduction to Data Science Day 1-2: Overview of AI What is AI? History and applications Subfields of AI: ML, Deep Learning, NLP, and Computer Vision Day 3-4: Ethical AI & Real-World Use Cases Bias and fairness in AI Applications in healthcare, finance, and more Day 5-6: Python Basics Variables, loops, and functions Libraries: NumPy, Pandas, and Matplotlib Day 7-8: Data Handling & Visualization Exploratory Data Analysis (EDA) Hands-on exercises with datasets

  3. Week 3: Supervised Machine Learning Day 1: Machine Learning Foundations Types of ML: Supervised and Unsupervised ML pipeline overview Day 2: Supervised Learning Techniques Regression: Linear and Logistic Regression Classification: Decision Trees Day 3: Model Evaluation Metrics Accuracy, Precision, Recall, F1 Score Introduction to cross-validation Day 4: Hands-On Practice Build regression and classification models on real datasets

  4. Week 4: Unsupervised Machine Learning Day 1: Clustering Techniques Introduction to K-Means and KNN Applications of clustering Day 2: Dimensionality Reduction PCA and its applications in data compression Day 3: Advanced Topics in Unsupervised Learning Evaluation metrics: Silhouette Score, Davies-Bouldin Index Day 4: Hands-On Practice Perform clustering and PCA on datasets

  5. Week 5-6: Deep Learning Basics Day 1-2: Neural Networks Fundamentals Understanding networks Activation and loss functions perceptrons and multi-layer Day 3-4: Convolutional Neural Networks (CNNs) • Architecture and applications in image classification Day 5-6: Hyperparameter Tuning Learning rate, epochs, and batch size optimization Day 7-8: Hands-On Practice Build a basic image classification model

  6. Week 7: Advanced Deep Learning Day 1: Recurrent Neural Networks (RNNs) Sequence modeling basics and applications Day 2: Transfer Learning Pre-trained models: VGG, ResNet, and their applications Day 3: Fine-Tuning Models Practical learning implementation of transfer Day 4: Hands-On Practice Build and fine-tune a deep learning model

  7. Week 8: Natural Language Processing (NLP) Day 1: Introduction to NLP Text preprocessing: Tokenization, stemming, and lemmatization Day 2: Feature Engineering for NLP Bag of Words, TF-IDF, and word embeddings (Word2Vec) Day 3: Transformers in NLP Introduction to BERT and GPT-based models Day 4: Hands-On Practice Build a sentiment analysis model

  8. Week 9: AI Deployment Basics Day 1: Model Deployment Overview Introduction to Flask and FastAPI for deployment Day 2: API Development Building APIs for ML models Day 3: Containerization with Docker Basics of Docker and creating containerized applications Day 4: Hands-On Practice Deploy an ML model locally using Flask

  9. Week 10: Advanced AI Deployment and MLOps Day 1: Cloud Deployment Basics Introduction to Google Cloud, AWS, or Azure Day 2: MLOps Fundamentals Continuous Integration and Deployment (CI/CD) pipelines Day 3: Monitoring and Scaling Models Use of monitoring tools for deployed applications Day 4: Capstone Project Kickoff Plan a real-world problem for the capstone project

  10. Week 10: Advanced AI Deployment and MLOps Day 1: Cloud Deployment Basics Introduction to Google Cloud, AWS, or Azure Day 2: MLOps Fundamentals Continuous Integration and Deployment (CI/CD) pipelines Day 3: Monitoring and Scaling Models Use of monitoring tools for deployed applications Day 4: Capstone Project Kickoff Plan a real-world problem for the capstone project

  11. THANK YOU Address G13 Islamabad Pakistan Telephone +923345336745 +923334471066 Website www.itsolera.com

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