In this video on Machine Learning with Python, you will understand the basics of machine learning from a short animated video and know the essential applications of machine learning. You will understand machine learning concepts and understand why mathematics, statistics, and linear algebra are crucial. We'll then focus on some vital machine learning algorithms. We'll also learn about regularization, dimensionality reduction, PCA.

Below topics are explained in this Machine Learning with Python full course:
Machine Learning Basics
Machine Learning Applications
Machine Learning Concepts Part -1
Machine Learning Concepts Part -2
Mathematics for Machine Learning
Machine Learning Algorithms
K-Nearest Neighbour Algorithm
Understanding SVM
Regularization in Machine Learning
Dimensionality Reduction
CoronaVirus Outbreak Prediction using Machine Learning
Machine Learning Roadmap 2021

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What Exactly is Machine Learning?
A good start at a Machine Learning definition is that it is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (well data) like humans without direct programming. When exposed to new data, these applications learn, grow, change, and develop by themselves.

What is Supervised Learning?
In supervised learning, we use known or labeled data for the training data. Since the data is known, the learning is, therefore, supervised, i.e., directed into successful execution. The input data goes through the Machine Learning algorithm and is used to train the model.

What is Unsupervised Learning?
In unsupervised learning, the training data is unknown and unlabeled - meaning that no one has looked at the data before. Without the aspect of known data, the input cannot be guided to the algorithm, which is where the unsupervised term originates from. This data is fed to the Machine Learning algorithm and is used to train the model. The trained model tries to search for a pattern and give the desired response.

What is Reinforcement Learning?
Like traditional types of data analysis, here, the algorithm discovers data through a process of trial and error and then decides what action results in higher rewards. Three major components make up reinforcement learning: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent does.

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