#ml #machinelearning #ai #artificialintelligence #datascience #regression #classification
🔥 In this video, we explain every major Machine Learning algorithm.
Regression models: Linear Regression, Polynomial Regression.
Classification models: Logistic Regression, Naive Bayes.
Models used for both: Decision Tree, Random Forest, Support Vector Machines, K-Nearest Neighbors.
Ensembles: Bagging, Boosting, Voting and Stacking.
Deep Learning: Fully Connected (Dense) Neural Networks.
Unsupervised learning: K-Means clustering and Principal Component Analysis (PCA) dimensionality reduction technique.
Heads up! You can't learn Machine Learning in just 22 minutes, a day, a week or even in a month! It needs a continuous dedication, patience, and consistent effort. I’m here to guide you every step of the way with clear explanations, tips, and resources to make your learning experience easier! Don't worry if there were concepts that were hard to understand!
Keep at it, and you’ll get there. Subscribe and like the video if you found it helpful!
Starting with this video, we’ll be posting a quick quiz on our Instagram page to help you review the material and test your understanding! It’s a great way to reinforce what you’ve learned and see how well you’re understanding the concepts. Be sure to follow us on Instagram, keep track of your progress and challenge yourself!
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🔍 Key points covered:
0:00 - Introduction.
0:22 - Linear Regression.
2:00 - Logistic Regression.
3:12 - Naive Bayes.
4:15 - Decision Trees.
6:25 - Random Forests.
7:55 - Support Vector Machines.
10:05 - K-Nearest Neighbors.
12:23 - Ensembles.
12:49 - Ensembles (Bagging).
13:18 - Ensembles (Boosting).
13:55 - Ensembles (Voting).
14:48 - Ensembles (Stacking).
15:55 - Neural Networks.
18:59 - K-Means.
20:58 - Principal Component Analysis.
22:05 - Subscribe to us!
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🤖 Note that we use synthetic generations, such as AI-generated images and voices, to enhance the appeal and engagement of our content.
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