Curriculum

  1. 1

    Book Preview

    1. Ultimate Machine Learning with Scikit-Learn Free preview
  2. 2

    Introduction

    1. (Included in full purchase)
  3. 3

    Chapter 1 : Data Preprocessing with Linear Regression

    1. (Included in full purchase)
  4. 4

    Chapter 2 : Structured Data and Logistic Regression

    1. (Included in full purchase)
  5. 5

    Chapter 3 : Time-Series Data and Decision Trees

    1. (Included in full purchase)
  6. 6

    Chapter 4 : Unstructured Data Handling and Naive Bayes

    1. (Included in full purchase)
  7. 7

    Chapter 5 : Real-time Data Streams and K-Nearest Neighbors

    1. (Included in full purchase)
  8. 8

    Chapter 6 : Sparse Distributed Data and Support Vector Machines

    1. (Included in full purchase)
  9. 9

    Chapter 7 : Anomaly Detection and Isolation Forests

    1. (Included in full purchase)
  10. 10

    Chapter 8 : Stock Market Data and Ensemble Methods

    1. (Included in full purchase)
  11. 11

    Chapter 9 : Data Engineering and ML Pipelines for Advanced Analytics

    1. (Included in full purchase)
  12. 12

    INDEX

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About the course

“Ultimate Machine Learning with Scikit-Learn” is a definitive resource that offers an in-depth exploration of data preparation, modeling techniques, and the theoretical foundations behind powerful machine learning algorithms using Python and Scikit-Learn. Beginning with foundational techniques, you'll dive into essential skills for effective data preprocessing, setting the stage for robust analysis. Next, logistic regression and decision trees equip you with the tools to delve deeper into predictive modeling, ensuring a solid understanding of fundamental methodologies. You will master time series data analysis, followed by effective strategies for handling unstructured data using techniques like Naive Bayes. Transitioning into real-time data streams, you'll discover dynamic approaches with K-nearest neighbors for high-dimensional data analysis with Support Vector Machines(SVMs). Alongside, you will learn to safeguard your analyses against anomalies with isolation forests and harness the predictive power of ensemble methods, in the domain of stock market data analysis. By the end of the book you will master the art of data engineering and ML pipelines, ensuring you're equipped to tackle even the most complex analytics tasks with confidence.

About the Author

Parag Saxena, a seasoned AI ML Data Scientist, embodies a unique blend of academic excellence and industry expertise. With a master’s degree in Data Science and Analytics, his career spans vital sectors like banking, retail, and power generation. Parag is a visionary, having deployed sophisticated machine learning models, authored research papers, and shared his expertise on prestigious platforms.