machine learning with python ibm skills network
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title: getting started with machine learning using python: a tutorial with ibm skills network
introduction:
machine learning is a rapidly growing field that empowers computers to learn from data and make predictions or decisions without being explicitly programmed. python, being one of the most popular programming languages for machine learning, offers a wide range of libraries and tools for implementing various algorithms. in this tutorial, we'll explore the basics of machine learning using python and leverage resources from the ibm skills network for learning and practice.
prerequisites:
tools and libraries required:
step 1: setting up your environment
before we dive into coding, let's ensure that our environment is set up properly. follow these steps:
install python: if you haven't already, download and install python from the official website (
install jupyter notebook: install jupyter notebook using pip, a python package manager:
install required libraries: install numpy, pandas, and scikit-learn using pip:
sign up for ibm skills network: visit the ibm skills network website ( and sign up for a free account to access tutorials, courses, and exercises related to machine learning.
step 2: exploring machine learning concepts
before delving into code, it's crucial to understand some fundamental concepts of machine learning, such as supervised learning, unsupervised learning, and evaluation metrics. utilize the resources available on the ibm skills network platform to gain knowledge on these topics through interactive tutorials and exercises.
step 3: loading and preprocessing data
in this step, we'll load a dataset and preprocess it for machine learning tasks. we'll use pandas to load the data and perform basic preprocessing tasks such as handling missing values, encoding categorical variables, and splitting the dataset into training and testing sets.
step 4: building and training a model
now, let's build a machin ...
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