Learn Core Machine Learning for FREE | Ultimate Course for Beginners

🚀 Welcome to the CORE Machine Learning course for beginners! This FREE course is designed to help you build a solid foundation in machine learning, focusing on core concepts and in-depth explanations of regression analysis, which is often overlooked in other courses. Whether you're a beginner or someone looking to strengthen your understanding of machine learning, this course is for you!

📌 Timestamps:
Introduction - 0:00 - 2:58
ML Foundation - 2:59 - 1:04:03
Regression Foundation : 1:04:04 - 2:51:09
Regression intermediate - 2:51:10 - 3:48:04
MLR Intermediate - 3:48:05 - 4:51:49
Regression Advance - 4:51:50 - 6:30:20
Regression Project 1 - 6:30:21 - 7:03:31
Regression Project 2 - 7:03:32 - 9:32:46

✨ Credits for Editing the course lectures ✨

✨ MY FREE COURSES ✨

💥 By the end of this course, you'll have a strong understanding of core machine learning concepts, regression analysis, and practical applications, setting you apart from others in the field. Dive into this exciting world of machine learning and unlock your potential today!

🔎 Keywords: Machine learning for beginners, machine learning course, core machine learning, regression analysis, free course

[YouMeverse]

How can a person who is just 15 years old be so productive and intelligent? When I was in 10th grade at that age, I was still learning basic science. In my school, they taught us how to use paint and Microsoft Word in computer class. And I must say, your English is far better than mine.

[kishantomar]

As an indian you did a great contribution to our society for our youngsters
We are proud on you❤️.

[deepaksai]

I just watched 30min and I can say u worked hard to deliver this content....

You earned yourself a subscriber....
My request is to start a series from scratch to become a Data
Or something where anyone can start and learn machine learning and AI....

[itz_pitcode]

Personally I was looking for a machine learning course for a long time .. and most of them were too short with not such notes .. this course is absolutely amazing .. and providing the material for free is just awesome

[dantedt]

I was taught Maths and Physics by Indian teachers. There's just something great about how Indian people teach.
Some of the nicest and best teachers in the world. Awesome course👍
Subscribed.

[prakashsiva]

00:00 Get a comprehensive machine learning course with detailed explanation and personalized feedback for free
05:48 Learning mechanisms involve past experiences and memorization approach.
17:09 Generalization helps rats and machines to make decisions based on similar things they have experienced before
22:36 Pigeons build an association between food delivery and certain actions, leading to meaningless learning conclusions.
34:30 Machine learning extracts patterns from data to make intelligent predictions
39:58 Scoping is the first step in building a machine learning system.
50:25 Supervised learning is like studying for an exam with the help of a supervisor
55:50 Supervised learning involves learning from labeled training data.
1:06:17 Machine learning is used to establish relationships between variables
1:11:42 Understanding linear vs. nonlinear data relationships
1:22:12 Best fit line is used to approximate values based on patterns in data.
1:27:21 Finding the best fit line depends on slope and y-intercept
1:37:46 Finding the best fit line for data using optimization algorithm
1:42:51 Numerical representation is necessary for accurate predictions in machine learning
1:53:16 Evaluate model performance by matching predicted values with correct values.
1:58:21 Mean squared error measures the performance of a model.
00:20 Algorithm in machine learning to study and summarize data
2:14:22 Latitude and skin cancer mortality rate have a negative relationship
2:25:15 Minimize J of M and B in hypothesis for cost reduction.
2:30:58 Beta0 represents the price of a house when size is zero.
2:42:04 For every additional hour of study, exam score increases by 5
2:47:49 For every increase in advertising expense, revenue increases by 0.8 dollars
2:58:03 Automatically try different beta values to get low error
3:03:22 Plotting x squared function gives a parabolic graph
3:14:08 Learn about learning rate and update rule for beta values
3:19:01 Learning rate variations and sample data for building function f
3:29:40 Calculate derivative of the given function
3:35:04 Using the update rule, calculate the MSE and updated beta values for beta0 and beta1 in each iteration
3:45:28 Understanding derivatives and gradient descent for parameter optimization
3:50:45 Design Matrix and Prediction in Supervised Learning
4:01:22 Learned about multiple linear regression and vectorized hypothesis function.
4:06:16 Multiple linear regression explained with an example.
4:17:03 Vectorization helps in faster computation of hypothesis and error calculation in multiple linear regression
4:22:20 Gradient descent algorithm for multiple linear regression
4:32:20 Evaluate machine learning model using mean square error and mean absolute error
4:37:50 Mean absolute error and root mean squared error are important in regression analysis.
4:48:02 R squared value of 0.9 indicates 90% of variation in dependent variable can be explained by independent feature X.
4:53:22 Hypothesis testing validates statements based on available data
5:03:39 There is a relationship between input variables and output variable
5:08:46 Understanding hypothesis testing in linear regression
02:28 Hypothesized value is assumed under null hypothesis and disprove it with t statistics
5:24:40 T-test helps to assess the significance of the coefficient relative to the uncertainty of our estimates.
00:15 The P value threshold of 0.05 is used to reject the null hypothesis.
5:40:41 Understanding degrees of freedom in regression
00:19 Visual inspection is a common way to test model assumptions
5:57:02 Linear regression assumptions and remedies
6:07:17 Residuals should not have patterns or trends, violating the independence assumption.
6:12:11 Homo scarcity assumption states that the variance of the residuals in a regression model is constant.
6:22:21 Normality assumption states that residuals should be normally distributed.
6:27:41 Linear regression and normality assumption
6:38:21 Data ingestion and processing before building linear regression model.
6:43:23 Fit method trains the linear regression model using gradient descent algorithm
6:53:26 Understanding Standard Error and Covariance in Regression
6:58:22 The statistical tests performed indicate that the model assumptions are valid.
7:08:45 Build a multivariate regression model to predict death rate for US countries with cancer diseases.
7:14:23 Virtual environment is important for package management and isolation.
00:32 Setting up the project and required libraries
7:30:37 Covered Exploratory Data Analysis (EDA) and pre-processing techniques
7:41:39 Divided data into four equal parts and found quartile numbers.
7:47:09 Identifying and removing outliers using Box Plot
7:57:11 Data exploration reveals missing values and categorical variables.
8:02:15 Techniques for dealing with missing values and analyzing internal variables
8:12:26 Learned about feature engineering and splitting and how to split columns in a data frame
8:17:25 The text explains a process of splitting intervals and converting them into lower and upper bounds to find the median.
8:28:08 One hot encoding is a technique to convert categorical variables into numeric for machine learning.
8:33:12 Prepare and analyze data set using one-hot encoding, drop and fill missing values, and deal with outliers
00:33 Data set is positively skewed with outliers present
8:49:21 Use Z score for outlier detection on normally distributed numerical columns.
8:59:07 Handling outliers in a data set
9:04:30 We use outlier detection techniques on columns based on their type of distribution.
9:14:35 Trimming and capping techniques used to remove outliers from data
9:19:31 Identifying highly correlated features among numeric columns in data using correlation matrix.
9:29:27 Add constant columns instead of removing them

[KnowlwdgeofTrueSaint]

I hope this message finds you well. I'm a big fan of your YouTube channel and have learned a lot from your videos. I was wondering if you would consider creating a video on providing a video channel or playlist that has made videos of math for machine learning from scratch.

As someone who is interested in machine learning, I have found that having a strong foundation in math is essential. However, finding good resources that teach math from scratch specifically for machine learning can be challenging. I believe that your expertise and teaching style would make for an excellent resource on this topic.

In particular, I would love to see videos that cover the basics of math like 2 + 2 = ?, all the way to more advanced topics such as linear algebra, calculus, and probability.

I understand that creating high-quality content takes time and effort, but I truly believe that a video series on this topic would be extremely beneficial to the machine learning community. Thank you for considering my request, and I look forward to watching your future videos.

[DeepaakashGupta]

I really applicate the hard work you put into this. Keep doing it. Everyone should take inspiration from you.

[syncpoint]

I knew python, numpy and pandas now I was planning to dive in machine learning, at the age of 40 it's very difficult to get time from routine life, I will try to go through this tutorial.

[aliiinawaz]

Notes visuals are soo goood. Really appreciated. Thank you so much BRO.This is really great

[successfulvictorypublisher]

Deepest thanks Mr. Ayush Singh for your excellent training session! Keep up the great work...and more training please. With best regards!

[jaymahakaal]

Subscribed not in AI/ML but thanks for contributing to tech community... I want more 15year old kids of India like you

[JohnS-erjh]

Excellent Course, great job with the graphics and breaking things down! I really liked where you covered Regression, breaking down the Best Fit Line, slope, etc.. Some videos they just rush right through this stuff (assuming everyone already had exposure to this stuff in previous classes).

[HeartOfTech-cwkz]

33:06
Machine Learning is a technique that enables machines to learn from existing data, identify patterns, and make predictions or decisions without being explicitly programmed for each task. It allows machines to improve their performance over time as they are exposed to more data.

[saqlainsajid]

I was watching Andrew Ng's videos but he skips a lot of basic stuff and assumes the student knows those simple basic things but I don't lol but this video is so very thorough and easy to understand thanks a ton man you saved me a lot of time and effort

[arpansart]

🔥🔥🔥 finally wait is over for your teaching material

[KumManish]

You are doing an awesome here for everyone Ayush Brother ✨ Thank you so much!

[AmanKumar-jkqu]

bhai can i just say i love your passion and you are a good human being serving the community

[mekbibbeyene]

Even his personality and articulation are inviting to learn. Wonderful!

[PrinceKumar-hhyn]

Clicked to refresh my recommendations, will watch later. The contents seems fabulous