In this post I mainly talked about my first day in Machine Learning primarily working with Linear Regression and analyzing your data for getting ready to fit it.
After messing around with really getting to know the in’s and outs of data frame management and other sides of data science in Python I have been reluctant to get into Machine Learning with the worry of not having the time I would like to commit to it and get as good as I would like. Like everything sometimes you just gotta do it. So here we go. Starting Point Where I am starting is Supervised learning, which basically means there is known input and outputs and you are just modifying the parameters of your model to predict future outcomes. -An example of this would be Positive vs. Negative movie reviews I am doing doing this work in Jupyter with the library scikit-learn in Python which has algorithms already in it, which makes it much easier to fit models, split test and training data etc.
🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...
In this article, I will take you through Linear Regression with PyTorch. I will simply use the PyTorch package to build a Linear Regression
PySpark in Machine Learning | Data Science | Machine Learning | Python. PySpark is the API of Python to support the framework of Apache Spark. Apache Spark is the component of Hadoop Ecosystem, which is now getting very popular with the big data frameworks.
A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start?
Applied Data Analysis in Python Machine learning and Data science, we will investigate the use of scikit-learn for machine learning to discover things about whatever data may come across your desk.