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Python Masterclass 2023: Machine Learning and Data Science

Discover the intricacies of Python-based Machine Learning and data science, delving into essential libraries such as Numpy, Pandas, Matplotlib, Scikit-Learn, and other key tools.

Created by- Jose Portilla

Throughout the course, you will acquire the following knowledge and skills:

  1. Gain a thorough understanding of machine learning and data science using Python.
  2. Create efficient data pipeline workflows to analyze, visualize, and extract insights from data.
  3. Develop a portfolio of data science projects using real-world datasets.
  4. Acquire the skills to analyze and gain insights from your own datasets through data science techniques.
  5. Master critical data science skills essential for effective analysis and modeling.
  6. Gain a comprehensive understanding of machine learning from the ground up.
  7. Replicate real-world scenarios and generate data reports based on your analyses.
  8. Learn NumPy, a powerful library for numerical processing with Python.
  9. Perform feature engineering on practical case studies to enhance machine learning models.
  10. Utilize Pandas, a versatile library for data manipulation in Python.
  11. Create supervised machine learning algorithms for accurate classification tasks.
  12. Learn Matplotlib for creating customized data visualizations.
  13. Develop regression machine learning models to predict continuous values.
  14. Explore Seaborn, a library for creating visually appealing statistical plots in Python.
  15. Build a modern portfolio of data science and machine learning projects for your resume.
  16. Understand how to use Scikit-learn to apply powerful machine learning algorithms.
  17. Quickly set up the Anaconda data science stack environment for efficient development.
  18. Learn best practices for handling and analyzing real-world datasets.
  19. Gain insights into the complete workflow of the machine learning lifecycle.
  20. Discover techniques for deploying your machine learning models as interactive APIs.

By covering these topics, you’ll acquire a comprehensive skill set in data science and machine learning with Python.

machine learning and data science

Course content-

  1. Introduction to Course
  2. OPTIONAL: Python Crash Course
  3. Machine Learning Pathway Overview
  4. NumPy
  5. Pandas
  6. Matplotlib
  7. Seaborn Data Visualizations
  8. Data Analysis and Visualization Capstone Project Exercise
  9. Machine Learning Concepts Overview
  10. Linear Regression
  11. Feature Engineering and Data Preparation
  12. Cross Validation , Grid Search, and the Linear Regression Project
  13. Logistic Regression
  14. KNN – K Nearest Neighbors
  15. Support Vector Machines
  16. Tree Based Methods: Decision Tree Learning
  17. Random Forests
  18. Boosting Methods
  19. Supervised Learning Capstone Project – Cohort Analysis and Tree Based Methods
  20. Naive Bayes classification and explore its application in Natural Language Processing, a supervised learning technique.
  21. Unsupervised Learning
  22. K-Means Clustering
  23. Hierarchical Clustering
  24. DBSCAN – Density-based spatial clustering of applications with noise
  25. PCA (Principal Component Analysis) and manifold learning techniques to reduce dimensionality and extract meaningful features from high-dimensional datasets.
  26. Model Deployment


  • A foundational understanding of Python, including the ability to work with functions, is a prerequisite for this course.


Discover the ultimate online course that offers unparalleled depth in Python, Data Science, and Machine Learning. Join a thriving community of over 2 million students led by esteemed instructor Jose Portilla and embark on a journey to embrace the future today!

This comprehensive course stands out as the definitive resource for learning about Python, Data Science, and Machine Learning in the online realm. It provides an unrivaled breadth of knowledge, equipping you with the skills necessary to excel in these dynamic fields. With the job market constantly evolving, this course positions you to seize lucrative opportunities and thrive in the ever-changing landscape.

Designed with meticulous attention to detail, the course covers the entire spectrum of the data science and machine learning tech stack used by leading companies worldwide. Graduates of this course have gone on to secure prestigious positions at renowned organizations such as McKinsey, Facebook, Amazon, Google, Apple, Asana, and more. The curriculum strikes the perfect balance between hands-on practical case studies and the fundamental mathematical theory that underpins machine learning algorithms.

Immerse yourself in advanced machine learning algorithms, including cutting-edge unsupervised learning techniques like DBSCAN, setting yourself apart from the competition. Gain the knowledge and expertise needed to navigate the future with confidence in the realms of Python, Data Science, and Machine Learning.

Join this transformative learning experience today, become part of a vibrant community of learners, and unlock a world of opportunities in Python, Data Science, and Machine Learning.

Experience a comprehensive course that rivals high-cost bootcamps, covering a wide range of topics essential to your data science journey. This course provides an extensive curriculum that encompasses the following key areas:

  1. Programming with Python
  2. NumPy for efficient numerical computing with Python
  3. In-depth exploration of Pandas for data analysis
  4. Complete understanding of the Matplotlib programming library
  5. Extensive coverage of seaborn for impactful data visualizations
  6. Machine Learning with SciKit Learn, including essential techniques such as:
    • Linear Regression
    • Regularization
    • Lasso Regression
    • Ridge Regression
    • Elastic Net
    • K Nearest Neighbors
    • K Means Clustering
    • Decision Trees
    • Random Forests
    • Natural Language Processing
    • Support Vector Machines
    • Hierarchical Clustering
    • DBSCAN
    • PCA
    • Model Deployment

And there’s much more to discover!

This course has been thoughtfully designed to provide the same level of quality and depth as high-priced bootcamps. We believe in making data science, machine learning, and Python accessible to all learners, offering a transformative opportunity to enhance your skillset. Join us inside the course and unlock the potential to excel in these fields!

-Jose and Pierian Data Inc. Team

You can also check other courses as well: Data Science Bootcamp: The Complete Course

This course is suitable for:

  • Python developers who are at the beginner level and have a keen interest in exploring the realms of Machine Learning and Data Science with Python.

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Frequently Asked Questions (FAQs)

Here are some frequently asked questions (FAQs) related to the Python-based Data Science and Machine Learning course:

Q: What topics will be covered in the course?

A: The course covers a range of topics, including Python programming, NumPy, Pandas, Matplotlib, Seaborn, data analysis, visualization, machine learning concepts, linear regression, feature engineering, logistic regression, K-nearest neighbors, support vector machines, decision tree learning, random forests, boosting methods, Naive Bayes classification, natural language processing, unsupervised learning, clustering algorithms, principal component analysis (PCA), model deployment, and more.

Q: What skills and knowledge will I gain from this course?

A: By taking this course, you will gain a thorough understanding of data science and machine learning using Python. You will learn to create efficient data pipeline workflows, analyze and visualize data, develop machine learning models, perform feature engineering, and gain insights from real-world datasets. Additionally, you will acquire essential skills in Python programming, data manipulation, and data visualization.

Q: Are there any prerequisites for this course?

A: A foundational understanding of Python, including the ability to work with functions, is required as a prerequisite for this course.

Q: Is this course suitable for beginners?

A: Yes, this course is suitable for Python developers who are at the beginner level and have a keen interest in exploring the realms of Machine Learning and Data Science with Python.

Q: What can I expect from the course content?

A: The course content includes video lectures, practical case studies, exercises, and projects. It provides a comprehensive and in-depth understanding of the covered topics, combining practical application with fundamental theory.

Q: Can I interact with other learners?

A: Yes, by joining this course, you become part of a vibrant community of learners, allowing you to interact, collaborate, and learn from others.

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