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Machine Learning Course for Absolute Beginners
Unlock the power of Machine Learning! Learn supervised, unsupervised and reinforcement learning with hands-on examples

This Course Includes
udemy
4.1 (2 reviews )
9h 24m
english
Online - Self Paced
professional certificate
Udemy
About Machine Learning Course for Absolute Beginners
Are you curious about Machine Learning but have no prior experience? This course is perfect for you! Designed specifically for beginners, we break down the complexities of Machine Learning into simple, easy-to-understand concepts. Through real-world examples and practical exercises, you’ll explore the foundations of
supervised learning
,
unsupervised learning
, and
reinforcement learning
. Whether you're a student, a professional looking to upskill, or simply a tech enthusiast, this course will provide you with the skills to kickstart your Machine Learning journey. Learn how to perform Exploratory Data Analysis with Python - Pandas, Seaborn, Matplotlib etc. after performing EDA learn how to apply ML algorithms on the datasets, create models and evaluate them. 1.
Supervised Learning
:Understand how algorithms learn from labeled data to make predictions.
Explore linear regression, logistic regression, decision trees, and more.
Hands-on example: Predicting house prices, Titanic Survival prediction, etc.. 2.
Unsupervised Learning
:Learn to uncover hidden patterns in data without predefined labels.
Topics include clustering.
Hands-on example: Customer segmentation for marketing. 3.
Reinforcement Learning
:Discover how agents learn to make decisions through rewards and penalties.
Key concepts: Q-learning.
Hands-on example.
Key Features
Beginner-friendly, no ML knowledge required.
Step by step tutorials on installing required IDEs and libraries.
Step-by-step coding demonstrations in Python.
Downloadable resources and cheat sheets for quick reference.
What You Will Learn?
- Supervised Machine Learning Algorithms and examples .
- Unsupervised Machine Learning Algorithms and examples .
- Reinforcement Algorithms and examples.