Python NumPy Machine Learning and Data Science
Whether you're delving into machine learning, finance, web development, or data science, mastering Python is paramount. Python's straightforward syntax makes it ideal for desktop, web, and business applications. Emphasizing readability and usability, Python was designed to offer a singular, preferably obvious method for tasks, resulting in stringent code standardization. Despite its compact core programming language, Python boasts an extensive standard library, providing many tools tailored to diverse programming needs.
At the heart of NumPy lies the NumPy array, which is crucial for numerical computation and data analysis. Pandas and other machine learning or artificial intelligence tools rely on tabular or array-like data for optimal performance. Leveraging NumPy within Pandas and machine learning packages significantly enhances data computation speed—NumPy-based arrays surpass Python Lists in performance by 10 to over 100 times. Aspiring Data Analysts, Data Scientists, and Big Data Engineers working with Python must acquaint themselves with NumPy, which offers a more efficient approach to handling matrix-like objects like Nd-arrays.
This course elucidates the advantages of utilizing NumPy's vectorized operations over standard Python lists and delves into matrix and array creation. You'll embark on a step-by-step journey, delving into Python fundamentals alongside its robust library, NumPy, through practical examples. Given its limitless capabilities, proficiency in effectively harnessing the NumPy library is crucial in the realm of Data Science. Throughout the course, you'll learn to apply Python in Linear Algebra and grasp Neural Network concepts, leveraging powerful machine learning algorithms. Various exercises will reinforce your understanding of Machine Learning with NumPy and Python Data Science concepts.
What you'll learn:
- Fundamentals of the NumPy Library and beyond
- Anaconda installation and usage
- Utilizing Jupyter notebook
- Mastering Python fundamentals for efficient use of the NumPy Library
- NumPy arrays and functions
- Linear Algebra applications
- Python integration with NumPy
- Machine learning concepts in data science
- Python-based machine learning applications
- Data analysis with NumPy and Python, including Pandas
Course Components:
Course:
- Python NumPy in Machine Learning & Data Science Training Course
PDF Guides:
- Data Science for Beginners: 4 Books in 1, Park
- Machine Learning Applications Data Science Fundamentals with Python, Paper
- Python for Data Science and Machine Learning, Chan
- Python Programming: 3 Books In 1, Test