Machine Learning Using Python Course in Singapore

best Machine Learning course using Python

Best Machine learning using python course in Singapore

Machine learning using python for beginners Course

Machine learning is a branch of Artificial Intelligence to introduce the Best Machine Learning Course Using Python and Scikit-Learn. It introduces learners to basic machine learning steps from data preparation to assessment of machine learning models. Learn machine learning using python will learn and create two classical machine learning models namely Linear Regression and Logistic Regression for continuous and categorical data respectively. Learners will learn how to process data using R-squared Pandas library in Python as well as to Imagination those data using Seaborn and Matplotlib. At the end, they will learn some metrics to evaluate their machine learning models.

Course Objectives:

The purpose of this course-

What you will learn:

Learn Python programming and Scikit on machine learning regression

Benefits of the Machine Learning Using Python Course:

Easy To Learn And Read:

Machine learning using python programming Python is usable by both inexpert and seasoned programmers due to its clear and legible syntax. Because of its simplicity, Developers can focus more on the machine learning algorithms’ logic and less when writing complicated code.

Extensive Libraries:

Python has many robust libraries designed specifically for machine learning applications. The best option is Scikit-learn since it offers all the tools needed for feature selection, training, evaluation, and data preprocessing. Furthermore, efficient tools for handling data and carrying out mathematical computations are offered by libraries like NumPy and Pandas.

Deep Learning Frameworks:

Machine learning is deep learning using  Python is the recommended language for a number of deep learning frameworks, such as TensorFlow, PyTorch, and Keras. between other advanced deep learning models, these frameworks include high-level concepts and training tools for transformer models, recurrent neural networks (RNNs), and convolutional neural networks (CNNs).

Large Community Support:

A sizable and high-spirited community of data scientists, researchers, and developers use Python. Along forums and online communities, this community shares information develops machine learning libraries, and provide tutorials and documentation. It is simpler to find solutions to issues and stay current with the most recent developments in the sector thanks to this strong support network.

Integration Capabilities:

Python is adjustable in machine learning, but its application goes beyond machine learning. It can easily combine with other technologies, including big data processing frameworks (like Apache Spark) for managing large data basic and web frameworks (like Django, Flask) for creating machine learning powered apps. Python using machine learning is just one of its many multiskilled applications. 

Machine Learning Using Python Course Outline

  • Know the machine learning flow and concepts
  • Understand functions within scikit-learn
  • Preface to supervised and unsupervised machine learning

  • Know unsupervised ML algorithms
  • Know clustering (k-means, SOM)
  • Know clustering (k-means, SOM)

  • Learn numerous supervised learning algorithms
  • Learn feature engineering and feature sets
  • Execute numerous Supervised ML algorithms with real use cases

  • Understand model selection and evaluation methods
  • Understand how to optimize machine learning models
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