Data Scientist
Machine Learning for Data Science
Overview
Master core machine learning algorithms, model evaluation, and time series forecasting to build predictive solutions for real-world data challenges.
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45 Hours of Learning
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2.6K+ Learners
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35,000+ GST
Skills You Will Learn
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Data preprocessing
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Feature engineering
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Model training & tuning
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Performance evaluation
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Handling missing data
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Managing imbalanced datasets
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Forecast modeling
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TensorFlow model building
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Scikit-learn implementation
Course Key Highlights
- Learn regression, classification, and clustering from scratch
- Master Random Forest and XGBoost ensemble techniques
- Build ARIMA and LSTM forecasting models
- Apply ROC and precision-recall for evaluation
- Handle missing and imbalanced datasets effectively
- Develop models with TensorFlow and Scikit-learn
- Practice both supervised and unsupervised learning methods
Course Time
Includes:
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Self-paced
Modules -
Practical assignments
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Quizzes & assessments
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45 Hours of Learning
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Learn how to process, manage, and analyze massive datasets using cutting-edge big data tools and cloud platforms like AWS, Azure, and Google Cloud.
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Tools: Scikit-Learn, TensorFlow
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INR 55,000 + GST
FAQs
What is the focus of this course?
This course covers supervised and unsupervised machine learning algorithms, ensemble methods, and time series forecasting with practical implementation.
Which tools are taught in the course?
You will work with TensorFlow and Scikit-learn for developing and evaluating machine learning models.
Will I learn both classification and regression?
Yes, the course includes regression, classification, clustering, and other ML techniques.
Is time series forecasting included?
Yes, you will learn ARIMA and LSTM-based forecasting models.
How is model performance evaluated in the course?
The course covers ROC curves, precision-recall, and other evaluation metrics.
