Complete Machine Learning Course in 60 Hours - Part 2 | Full Machine Learning Course for Beginners

. This Complete Machine Learning Course video will help you understand and learn Machine Learning in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning concepts & practice.

Timestamp for the topics covered in this Machine Learning Course video:

00:00 Introduction
5:15 Mathematics for ML - Introduction
10:39 Linear Algebra - Vectors
20:40 Vector Operations - Part 1
33:35 Vector Operations in Python - Part 1
53:18 Vector Operations - Part 2
1:03:34 Vector Operations in Python - Part 2
1:22:06 Matrix - Basics
1:35:45 Working with Matrix in Python
1:54:09 Matrix Operations
2:12:36 Matrix Operations in Python
2:44:38 Statistics for ML - Intro
2:53:26 Statistics Basics & Types of Data
3:06:55 Types of Statistics
3:20:46 Statistical Study - Types
3:32:57 Population & Sample
3:56:24 Central Tendencies
4:12:45 Measure of Variability
4:25:25 Percentiles & Quantiles
4:34:10 Correlation & Causation
4:47:41 Hypothesis Testing
4:57:45 Probability for ML
5:05:55 Basics of Probability
5:15:52 Random Variables & its Types
5:25:22 Probability Distribution for Random Variables
5:35:18 Normal Distribution & Skewness
5:44:55 Poisson Distribution
5:55:19 Module 6: ML Models
6:16:07 Supervised Learning Models
6:24:05 Unsupervised Learning Models
6:30:45 Model Selection & Cross Validation
6:45:01 Overfitting
6:59:03 Underfitting
7:07:37 Bias Variance Tradeoff
7:26:12 Loss Function
7:40:24 Model Evaluation
7:55:58 Model Parameters & Hyperparameters
8:28:15 Gradient Descent
8:54:01 ML Use Case 4: Heart Disease Prediction
10:32:55 ML Use Case 6: Loan Approval Prediction

Note: Audio is not proper for "ML Use Case 5: House Price Prediction"

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[PrashantSingh-su]

Other YouTube channels were just wasting my time but your teaching, explaining, ... is worth it. Keep it up, keep growing

[extremexplorer]

Literally this is the Only Channel On Youtube where we can Learn About AI/ML.. Other are Just There for Views

[rishabhkushwaha]

Best serise for ML and Deep learning . If any one want to learn then watch it.

[lifecodes]

I can't find words to thank! This is priceless!! A true life changer for those who want

[praveenchinnareddy]

Seems like lot of hard effort being kept in preparing the PPT. Thank you for your help :)

[mohamedthasneem]

Thank you very much for the video, there is no sound region start ater 9.36.48 - House price prediction section...

[sumedhmane]

Absolute best tutorial till way ahead sir

[its_me_vk_jd]

Great
Keep It Up Sir....
It's Really Amazing....
Hats Off For Your Work

[isaacetungu]

This is an awesome!
In the last of the video, you said "You can now Make predictions" kindly provide me with a link to that video on how to do that.

Thanks..

[commonguy]

in the last project, the training accuracy varies from device to device but test accuracy is same

[angrahraina]

Really appreciate the effort you've put in this video👏

[omarabdelaziz]

Great Work..but this shall be part 1 not 2 !

[KararaJawaab]

Great Sir. Many Many Many Thanks To You.

[eclipse-eclipse-eclipse]

9:26:38 I got better accuracy just because i standarized the data (0.87 accuracy)

[aysham]

sir can u share this ppt with us please.. it will be more helpful for us to study easily..✴✴✴✴

[cirimalasreenath]

can anyone tell that Bias and loss function is same or not by his said that bias is diff between predicted and correct value where as loss function is how far from estimated value and true value both giving same meaning i think please correct my doubt

[gauravshukla]

Thankew sir your support
After completing this course can b crack any interview for machine learning or data science

[omarabdelaziz]

boston data set has ethical problem, what shall be done to load these data?

[idontknowiknow]

When we run describe on the heart_data and see when STD is more across the columns, why we have not standardized the data using Standard Scaler?

[darshitpatel]

Why we use derivative here in gradient descent ??