Master Data Science, AI, and Machine Learning with hands-on projects in Python, Deep Learning, Big Data, and Analytics
What you will learn
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Understand Data Science Workflow: Master the end-to-end data science lifecycle, from data collection to model deployment.
Data Collection Techniques: Learn to gather data from APIs, databases, and web scraping.
Data Preprocessing: Clean and preprocess raw data for analysis and modeling.
Exploratory Data Analysis (EDA): Uncover patterns and trends in datasets using visualization tools.
Feature Engineering: Create and optimize features to improve model performance.
Machine Learning Models: Build regression, classification, and clustering models using scikit-learn.
Deep Learning Techniques: Train neural networks with TensorFlow and PyTorch.
Model Deployment: Serve AI models using Flask, FastAPI, and Docker.
Big Data Handling: Work with large datasets using tools like Hadoop and Spark.
Ethical AI Practices: Understand data privacy, bias mitigation, and AI governance.
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