Course Overview
This Data Science course teaches the fundamentals of data analysis, machine learning, and statistical modeling using programming languages like Python and R. It covers key concepts such as data cleaning, visualization, predictive analytics, and deep learning to prepare students for real-world applications.
What You'll Learn in This Course:
- Learn Python, R, and SQL for data analysis and manipulation.
- Process raw data and create insightful visualizations using Pandas, Matplotlib, and Seaborn.
- Develop predictive models and deep learning applications with Scikit-Learn and TensorFlow.
- Work with large-scale data using Hadoop, Spark, and cloud platforms.
- Solve industry problems in business, healthcare, and finance using data-driven approaches.
This course will help you master data analysis, machine learning, and AI techniques to extract insights and build predictive models. By applying these skills to real-world projects, you'll be prepared for a career in data science, AI, and analytics.
Course Content
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1.1 Python Fundamentals
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1.2 NumPy for Numerical Computing
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1.3 Pandas for Data Analysis
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1.4 Exploratory Data Analysis (EDA)
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2.1 Data Visualization with Matplotlib & Seaborn
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2.2 Advanced Pandas & Feature Engineering
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2.3 Probability & Statistics
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2.4 Time Series Analysis
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3.1 Introduction to Machine Learning
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3.2 Regression Models
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3.3 Classification Models
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3.4 Model Performance Evaluation
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4.1 Ensemble Models
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4.2 Support Vector Machines & Hyperparameter Tuning
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4.3 Clustering & Dimensionality Reduction
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4.4 Handling Imbalanced Data & Synthetic Data Generation
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5.1 Introduction to Deep Learning
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5.2 Neural Network Optimization
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5.3 Convolutional Neural Networks (CNN)
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5.4 Mini-Project
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6.1 NLP Fundamentals
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6.2 Text Processing & Sentiment Analysis
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6.3 Sequence Models
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6.4 Mini-Project
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7.1 Model Deployment Basics
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7.2 Web Apps for ML Models
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7.3 Deployment on Cloud Platforms
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7.4 Mini-Project
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8.1 End-to-End Data Science Project Setup
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8.2 Model Selection & Fine-tuning
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8.3 Career Preparation
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8.4 Final Capstone Project
