Machine Learning with Python | Machine Learning Tutorial for Beginners | Machine Learning Tutorial

Welcome to our comprehensive tutorial on Machine Learning with Python, designed specifically for beginners! Whether you're new to the world of machine learning or looking to solidify your understanding of core concepts, this video is the perfect starting point. This tutorial covers Machine Learning basics, its significance, and applications.

The video talks about why Python is ideal for Machine Learning and teaches you how to set up your Python environment. It also introduces essential libraries like NumPy, Pandas, Matplotlib, and Scikit-learn, and discusses data preprocessing techniques, including cleaning, normalization, and feature engineering. It explores supervised learning algorithms like Linear and Logistic Regression, Decision Trees, and unsupervised methods like K-Means Clustering and PCA. Additionally, it explains model evaluation with metrics like accuracy and F1 score and applies knowledge to real-world projects such as housing price prediction and image classification.

This tutorial is packed with practical examples and hands-on coding sessions to ensure you gain a thorough understanding of each topic. By the end of this video, you'll have a solid foundation in machine learning with Python and be well-equipped to tackle more advanced concepts and projects.

Topics Covered:
00:00:00 - Agenda
00:03:58 - Introduction to Python and Anaconda
01:07:05 - Introduction to Pandas and Data Manipulation
04:42:32 - Introduction to Numpy and Numerical Computing
05:10:58 - Data Visualization
06:06:12 - Statistics vs Machine Learning
06:12:44 - Types of Statistics
07:54:39 - Understanding Data
07:58:19 - What is Reinforcement Learning?
08:53:46 - Reinforcement Learning Framework
09:24:58 - Q-Learning
09:51:08 - Case Study on Smart Taxi

🔥Check Our Free Courses on with free certificate:

Here is a list of our other full course videos you can check out:
✔ Data Science Tutorial: • Data Science Full Course for Beginner...
✔ Python for Data Science: • Python for Data Science | Data Scienc...
✔ Machine Learning with Python: • Machine Learning with Python | Machin...
✔ Statistics for Data Science: • Statistics for Data Science | Probabi...
✔ Tableau Training for Beginners: • Tableau Tutorial | Tableau Full Cours...
✔ Reinforcement Learning Tutorial: • Reinforcement Learning Tutorial | Rei...

⚡ About Great Learning:
With more than 5.4 Million+ learners in 170+ countries, Great Learning, a part of the BYJU'S group, is a leading global edtech company for professional and higher education offering industry-relevant programs in the blended, classroom, and purely online modes across technology, data and business domains. These programs are developed in collaboration with the top institutions like Stanford Executive Education, MIT Professional Education, The University of Texas at Austin, NUS, IIT Madras, IIT Bombay & more.

SOCIAL MEDIA LINKS:

🔹 For more updates on courses and tips follow us on:

[greatlearning]

🔹 Topics Covered:
00:00:00 - Agenda -
00:03:58 - Introduction to Python and Anaconda
01:07:05 - Introduction to Pandas and Data Manipulation
04:42:32 - Introduction to Numpy and Numerical Computing
05:10:58 - Data Visualization
06:06:12 - Statistics vs Machine Learning
06:12:44 - Types of Statistics
07:54:39 - Understanding Data
07:58:19 - What is Reinforcement Learning?
08:53:46 - Reinforcement Learning Framework
09:24:58 - Q-Learning
09:51:08 - Case Study on Smart Taxi

[rutvijgajjar]

Started this video casually. And got stuck to it for 2+ hours. Eventually gonna finish the whole.
Important course, great information, smoothly explained.
Raghu Raman👍

[TheSnekkerShow]

This is awesome. There's absolutely no way I'm going to watch this right now, but it's great to see this quantity and quality of information posted for public benefit. I'm subscribing and sharing.

[sunilroy]

Great introduction. Just one point at 2:02:30 Tuple for single column is passed in wrong way and thats why it returned data series. It should be df.loc[:, ("START*", ) instead of df.loc[:, ("START*") to get the dataframe. As in Pyhton () brackets are used for expressions or for functions. (2+3) will be evaluated into 5 and ("START*") will be evaluated into string "START*" and thus when there is single element in a TUPLE it must be represented with comma at the end.

[dandamohanrao]

Great session !!!! One Suggestion!!!
3:43:15 : As you said, we can't remove index from DataFrame. No, we can remove index from DataFrame. Try below solution.
res = res.reset_index(inplace = True, index = False)
3:44:98 :
df.to_csv("op.csv", index=False)

[manishverma-vuhs]

2:01:30 In python: ("string") is not a tuple, ("string", ) is a tuple. Comma is must for tuple with single item

[abhishekpathak]

Actually i start learning this course right now.. it's amazing 😍.

[sony]

Thank you Great Learning, this course has created my interest in data science and made me confident to choose data science as a new career after a big gap of 15 years. Thank you very much for providing the resources as well. For me I will say Great Learning is a trusted source and it has saved a lot of money i was planning to spend on the course from other institute.

[shankar]

Thanks Mr RaghuRaman. You really covered the Basics as they should be. This is the way to cover the PG course on Python. Thanks a lot.

[Techno]

Tysm Great Learning this is a golden opportunity for those who are unable to enroll

[issack]

ha!!!! finally completed 10hrs course in two days with picture clarity on machine learning thank you @Greatlearning for this course video

[vijayprabhakaran]

Big Kudos to making all this available, Raghu Raman you are a good teacher. I would however like to point out that the interpretation of boxplots that you describe is incorrect : (6:02:19-6:05:30), the vertical dimensions of the boxes says nothing about the amount of data, only says something about the shape of the distribution and the inter quartile spread.

[mophez]

only 10 mins into the video, and really loving this. Thanks for sharing!!!!

[roshannsharma]

2:03:50, I think tuples do not allow manipulation as in the case of list, and that might be the reason.

[manpreetsingh]

5:27:47 things which you are:
1. Aware and know
2. Aware and dont know yet
3. Not aware but you know
4. Not aware and dont know

[jasimwaqar]

Assignment 3 :

conditions = [
(df["MILES*"] > 10),
((df["MILES*"] >=5 ) & (df["MILES*"]<10)),
(df["MILES*"] < 5)]

choices = ['LONG TRIP ', 'MID TRIP', 'SHORT TIP']

df["MILES_caatt"]=np.select(conditions, choices, default='No trip')

[hillfordh]

This prof is fantastic. Perfect pace and well articulated for a beginner like me!

[lakshmin]

Excellent Teaching. I truly learnt a lot and used it to teach some of my students also. They managed to go back and forth to view difficult sections of the video several times and learn better.
Good on

[xSidx]

@2:01:30 ('Start*' ) is not a tuple
Single element tuple is ('Start*', )
df.loc(:, ('Start*', )) will return a df as it is only one element tuple.

[sorenbabu]

Thank you sir.for supported advance level to improve learning skill.