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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:
00:00:00 Machine Learning Basics
00:08:51 Machine Learning Applications
00:13:46 Machine Learning Concepts Part -1
01:15:16 Machine Learning Concepts Part -2
02:10:42 Mathematics for Machine Learning
04:00:55 Machine Learning Algorithms
05:50:24 K-Nearest Neighbour Algorithm
06:16:51 Understanding SVM
06:35:52 Regularization in Machine Learning
07:04:58 Dimensionality Reduction
07:35:00 CoronaVirus Outbreak Prediction using Machine Learning
08:21:55 Machine Learning Roadmap 2021

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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.

➡️ About Post Graduate Program In AI And Machine Learning
This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

✅ Key Features
- Post Graduate Program certificate and Alumni Association membership
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✅ Skills Covered
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- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
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