Machine Learning for Beginners 2024: Theory to Practice with Python Project [Full Course]

🚀 Machine Learning for Beginners 2024: Theory to Practice with Python Project [Full Course]

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🤖 Machine Learning for Beginners 2024:

Embark on a comprehensive journey from theory to real-world application with our "Machine Learning for Beginners 2024" course. Dive deep into the core concepts of machine learning, understand its practical implications, and bring your knowledge to life through a hands-on Python project. Whether you're new to the field or looking to update your skills, this full course is tailored to guide you through the fascinating world of machine learning, preparing you for the future with the latest trends and techniques.

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👤 Meet Your Instructor: Tatev Aslanyan

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Timestamps:

00:00:00 - Intro
00:04:31 - Machine Learning Roadmap for 2024
00:48:41 - Machine Learning Basics
01:03:51 - Bias-Variance Trade-Off
01:11:00 - Overfitting and Regularization
01:26:30 - Linear Regression Basics Part 1
01:40:53 - Linear Regression Basics Part 2
02:05:11 - Case Study Linear Regression - Californian House Price Prediction

[abhisheksharma]

This is video is gold. I wanted to learn machine learning as a beginner and this video literally cleared my mind. After watching this video, I have a clear vision and now I can proceed further on my own. One suggestion, in future please start a series where you teach machine learning from basic to advance. Thanks a lot.

[GaelAlvarezcalderon]

Facing self-doubt, this course was a turning point. LunarTech's comprehensive curriculum and supportive community laid the groundwork for my transformation from a hopeful learner to a proficient, and ultimately, a standout software engineer.

[solomononuchefaruna]

You are such an amazing and patient teacher. Your presentation is wonderful. God bless you.

[Ninopssilva]

Passing by to thank you for your great quality content for the development community! Real gratitude!

[s-anandyadav]

Wonderfully explained complex topics..With due regards Ma'am.. Please allow me to point out a small correction in the slide content at time stamp 1:07:28 .. The "Reducible" error rate should be :- E ( f(x) - f^(x) ) ^2 .. It should be Expectation of that quantity.. You might have missed it by mistake Ma'am .. and please correct me if I am wrong .. I would appreciate it ! 😊

[MrCarloshazevedo]

The most beautiful math teacher I've never had during my elementary and high school times....

[SumitPal-sbuy]

Hi, ma'am my name is sumit. . I am 19years old. Currently pursuing my bachelor in science maths honours. Ma'am I also tried to get into data science and started with sql. But it so much complicated that ultimately I had to give up. Now I am thinking to get into java full stack. Please make complete guidance video on java full stack and dsa.

[akhenatontubba]

Tüyo: videolarınıza manuel olarak ingilizce altyazı eklediğiniz takdirde otomatik çeviri ile başka dillerde ingilizce bilmeyenler kendi dillerinde altyazılı olarak anlayarak izleyebilirler

[okolojohn]

A big thank you to LunarTech for this Video. However, i have a question, i tried using the pd.get_dummy to convert the categorical variable into the numerical variable but what i am getting is a boolean variable(True/False) instead of the 0/1. please any way out.

[LuisLopes-pbyk]

Great explanation, really appreciate this!

[karenbobo-yilw]

Thanks for slime and very helpful explanation. Excellent work.

[arokyajenson]

Hi mam thank you for the wonderful explanation video and also upload all other algorithms mam

[vlad_the_player]

Great explanation 😁 You're a really nice teacher !

[Data-curious.]

If i want to be a data scientist should I become data analyst first...?

[ggggg]

What I don't understand about these ML projects is so what if i get the accuracy and all those, how to I use it. It's not like wbe development where you get a website or chatbot or whatever. So what if you get these results from python, how to use it?

[Fercho]

This is really well explained, thank you so much!