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2021 Python for Data Science & Machine Learning from A-Z

Begin Your Python Journey: Explore Data Science & Machine Learning Foundations with Hands-On Learning

Embark on a transformative journey into the world of Python, data science, and machine learning, where you will uncover the intricacies of these dynamic fields from the ground up. This comprehensive course is meticulously designed to guide you through the foundational aspects of Python programming, tailored specifically for data science and machine learning applications. Whether you are a newcomer to the field or seeking to deepen your existing knowledge, you will find this course to be an invaluable resource.

Venture into the realm of data science and machine learning concepts, understanding why Python is the language of choice for data professionals. Delve into the essentials of Python programming, exploring its powerful capabilities for data analysis and visualization. You will gain insights into the statistical methods that underpin data science, including probability and hypothesis testing, equipping you with the analytical tools necessary to interpret complex datasets.

The course further enriches your understanding with detailed explorations of NumPy and Pandas, two essential libraries for data manipulation and processing. These modules will enhance your ability to manage and analyze data efficiently, laying a solid foundation for advancing into more sophisticated topics. As you progress, you will discover the art of data visualization, learning to craft compelling narratives from data through various visualization techniques.

Transitioning to machine learning, the course offers a deep dive into key algorithms and models. You will explore linear and logistic regression, K Nearest Neighbors, decision trees, and ensemble methods like random forests and support vector machines. Each topic is presented with clarity, demystifying complex concepts and providing a thorough understanding of how these models function and their applications in real-world scenarios.

An exploration of unsupervised learning techniques such as K-Means and Principal Component Analysis (PCA) adds another layer to your expertise, allowing you to discern patterns and structures within data without predefined labels. These sections are designed to broaden your perspective on data analysis and open up new avenues for exploration.

Beyond technical knowledge, the course addresses the professional landscape of data science, offering guidance on crafting resumes and cover letters tailored for data science roles. Insights into freelancing and networking strategies are provided to help you establish a strong professional presence in the industry.

As you navigate the intricacies of Python for data science and machine learning, this course will empower you to harness the full potential of these technologies. With a focus on theoretical understanding, you will be well-prepared to tackle the challenges of the field and make informed decisions in your data-driven endeavors. Join us in this educational experience and unlock new opportunities for personal and professional growth.

Introduction to Python for Data Science & Machine Learning from A-Z +
Data Science & Machine Learning Concepts +
Python For Data Science +
Statistics for Data Science +
Probability and Hypothesis Testing +
NumPy Data Analysis +
Pandas Data Analysis +
Python Data Visualization +
Introduction to Machine Learning +
Data Loading & Exploration +
Data Cleaning +
Feature Selecting and Engineering +
Linear and Logistic Regression +
K Nearest Neighbors +
Decision Trees +
Ensemble Learning and Random Forests +
Support Vector Machines +
K-Means +
PCA +
Data Science Career +
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About the Instructor

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Juan Galvan