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.

  • 4 Hours 45 Hours of Learning
  • Learners 2.6K+ Learners
  • GST Derivatives 35,000+ GST

What You Will Learn

Supervised ML techniques Unsupervised ML methods Regression modeling Classification modeling Clustering analysis Ensemble learning methods Time series forecasting ARIMA model implementation LSTM model application Model evaluation metrics

Skills You Will Learn

Skills
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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:
  • Time

    Self-paced
    Modules

  • Time

    Practical assignments

  • Time

    Quizzes & assessments

  • Time

    45 Hours of Learning

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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.

Infra

Build, evaluate, and deploy ML models with advanced forecasting and ensemble techniques.

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