Data Science : Begineer to Advance

Chapters

1Data Science Foundations and Workflow

2Python Programming Essentials for Data Science

3Working with Data Sources and SQL

4Data Wrangling with NumPy and Pandas

5Data Cleaning and Preprocessing

6Exploratory Data Analysis and Visualization

7Probability and Statistics for Data Science

8Machine Learning Foundations

9Supervised Learning Algorithms

10Unsupervised Learning and Dimensionality Reduction

11Model Evaluation, Validation, and Tuning

12Feature Engineering and ML Pipelines

13Time Series Analysis and Forecasting

14Natural Language Processing

15Deep Learning, Deployment, and MLOps

Neural Network Basics Activation Functions and Initialization Backpropagation and Optimizers Regularization Dropout and BatchNorm Convolutional Neural Networks Recurrent and Sequence Models Frameworks PyTorch and TensorFlow Transfer Learning and Fine Tuning Experiment Tracking with MLflow Model Serving APIs and Batch Containerization with Docker CI CD for ML Monitoring Drift and Performance Data and Model Versioning Orchestration and Pipelines

Courses/Data Science : Begineer to Advance/Deep Learning, Deployment, and MLOps

Deep Learning, Deployment, and MLOps

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Learn neural network fundamentals and apply practical MLOps to ship, monitor, and maintain production grade AI systems.

Content

11 of 15

Containerization with Docker

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