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Machine Learning With Python Full Course 2023 | Machine Learning Tutorial for Beginners| Simplilearn

Machine Learning With Python Full Course 2023 | Machine Learning Tutorial for Beginners| Simplilearn

Machine Learning With Python Full Course 2023 | Machine Learning Tutorial for Beginners| Simplilearn



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In this video on Machine Learning with Python full course, you will understand the basics of machine learning and Python. In this Machine Learning tutorial for beginners, we will cover essential machine learning topics like applications of machine learning and machine learning concepts and understand why mathematics, statistics, and linear algebra are crucial. We’ll also learn about regularization, dimensionality reduction, and PCA. We will perform a prediction analysis on the recently held US Elections. Finally, you will study the Machine Learning roadmap. Below are the topics covered in this video:

00:00:00 Machine Learning With Python Full Course 2023
00:08:36 Introduction to Machine Learning
00:16:14 Top 10 Applications of Machine Learning
00:32:38 Types of Machine Learning
00:37:46 Machine Learning Algorithms
00:38:14 Linear Regression
00:46:52 Decision Tree
01:23:25 Clustering
01:26:11 K-Means Clustering
02:18:03 Data and its types
03:29:22 Probability
04:07:53 Multiple Linear Regression
04:45:55 Confusion Matrices
05:59:54 KNN
06:23:40 Support Vector Machine
07:14:40 Principle Component Analysis(PCA)
07:53:01 Corona Virus Analysis

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Dataset Link -https://drive.google.com/drive/folders/15lSrc4176J9z9_3WZo_b91BaNfItc2s0

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Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME’s excellence and IBM’s industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions.

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– Capstone projects in 3 domains
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✅ Skills Covered

– Statistics
– Python
– Supervised Learning
– Unsupervised Learning
– Recommendation Systems
– NLP
– Neural Networks
– GANs
– Deep Learning
– Reinforcement Learning
– Speech Recognition
– Ensemble Learning
– Computer Vision

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Comments (25)

  1. for K-means clusterin i should be in range of 1,10 not 0,10

  2. Thanks for your great course. Is the slides of this course available to download?

  3. Very great job you have done to this tutorials, I must say and it's very understandable than my lecture's teachings in the class room… Thank you so much am not taking it for granted as you have rescued my career with this great favor….

    Please can I get the link to ALL the SLIDES USED IN THIS LECTURE?

  4. X = X.convert_objects(convert_numeric = True), i tried using this syntax to covert my object types to numerics yet i am getting errors in the car.csv dataset

  5. Thanks a. Lot

  6. How do I learn artificial intelligence?
    I am currently studying as an information engineer? I am from Syrian

  7. Thank you.

  8. Thanks ❤❤❤

  9. Many many thanks

  10. thanks for this great effort.

  11. When and how I can get the certificate of completion, do I need to complete the course on Simplilearn platform? or the you tube videos by Simplilearn? Please suggest.

  12. Do i need to learn scikit learn for this

  13. Could you drop answers to the quiz at 14:20? In my opinion its:
    1. Supervised learning
    2. Unsupervised learning
    3. Unsupervised learning
    I really want to know if I am right 😀

  14. Great job. Pls I am requesting for the dataset used for linear regression.

  15. 1. reinforcement
    2. supervised
    3. unsupervised

  16. Really useful. Altough didnt find the csv document called "heart.csv"

  17. In put who to reaching

  18. is this beginner friendly?

  19. It's actually only 8.5 hrs though

  20. Superb Course

  21. Thanks for your knowledge sharing sir. Required reinforcement learning. I hv subscribed just now.

  22. This is great. please make advance course of ML and make some good projects like spam etc.

  23. Thanks for doing this…

  24. Hi, i want to request for the datasets used in the course

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