@PrimeContent01
00:00 Introduction 2:47 What is Machine Learning? 4:08 AI vs ML vs Deep Learning 5:43 How does Machine Learning works? 6:18 Types of Machine Learning 6:43 Supervised Learning 8:38 Supervised Learning Examples 11:49 Unsupervised Learning 13:54 Unsupervised Learning Examples 16:09 Reinforcement Learning 18:39 Reinforcement Learning Examples 19:34 AI vs Machine Learning vs Deep Learning 22:09 Examples of AI 23:39 Examples of Machine Learning 25:04 What is Deep Learning? 25:54 Example of Deep Learning 27:29 Machine Learning vs Deep Learning 33:49 Jupyter Notebook Tutorial 34:49 Installation 50:24 Machine Learning Tutorial 51:04 Classification Algorithm 51:39 Anomaly Detection Algorithm 52:14 Clustering Algorithm 53:34 Regression Algorithm 54:14 Demo: Iris Dataset 1:12:11 Stats & Probability for Machine Learning 1:16:16 Categories of Data 1:16:36 Qualitative Data 1:17:51 Quantitative Data 1:20:55 What is Statistics? 1:23:25 Statistics Terminologies 1:24:30 Sampling Techniques 1:27:15 Random Sampling 1:28:05 Systematic Sampling 1:28:35 Stratified Sampling 1:29:35 Types of Statistics 1:32:21 Descriptive Statistics 1:37:36 Measures of Spread 1:44:01 Information Gain & Entropy 1:56:08 Confusion Matrix 2:00:53 Probability 2:03:19 Probability Terminologies 2:04:55 Types of Events 2:05:35 Probability of Distribution 2:10:45 Types of Probability 2:11:10 Marginal Probability 2:11:40 Joint Probability 2:12:35 Conditional Probability 2:13:30 Use-Case 2:17:25 Bayes Theorem 2:23:40 Inferential Statistics 2:24:00 Point Estimation 2:26:50 Interval Estimate 2:30:10 Margin of Error 2:34:20 Hypothesis Testing 2:41:25 Supervised Learning Algorithms 2:42:40 Regression 2:44:05 Linear vs Logistic Regression 2:49:55 Understanding Linear Regression Algorithm 3:11:10 Logistic Regression Curve 3:18:34 Titanic Data Analysis 3:58:39 Decision Tree 3:58:59 what is Classification? 4:01:24 Types of Classification 4:08:35 Decision Tree 4:14:20 Decision Tree Terminologies 4:18:05 Entropy 4:44:05 Credit Risk Detection Use-case 4:51:45 Random Forest 5:00:40 Random Forest Use-Cases 5:04:29 Random Forest Algorithm 5:16:44 KNN Algorithm 5:20:09 KNN Algorithm Working 5:27:24 KNN Demo 5:35:05 Naive Bayes 5:40:55 Naive Bayes Working 5:44:25Industrial Use of Naive Bayes 5:50:25 Types of Naive Bayes 5:51:25 Steps involved in Naive Bayes 5:52:05 PIMA Diabetic Test Use Case 6:04:55 Support Vector Machine 6:10:20 Non-Linear SVM 6:12:05 SVM Use-case 6:13:30 k Means Clustering & Association Rule Mining 6:16:33 Types of Clustering 6:17:34 K-Means Clustering 6:17:59 K-Means Working 6:21:54 Pros & Cons of K-Means Clustering 6:23:44 K-Means Demo 6:28:44 Hierarchical Clustering 6:31:14 Association Rule Mining 6:34:04 Apriori Algorithm 6:39:19 Apriori Algorithm Demo 6:43:29 Reinforcement Learning 6:46:39 Reinforcement Learning: Counter-Strike Example 6:53:59 Markov's Decision Process 6:58:04 Q-Learning 7:02:39 The Bellman Equation 7:12:14 Transitioning to Q-Learning 7:17:29 Implementing Q-Learning 7:23:33 Machine Learning Projects 7:38:53 Who is a ML Engineer? 7:39:28 ML Engineer Job Trends 7:40:43 ML Engineer Salary Trends 7:42:33 ML Engineer Skills 7:44:08 ML Engineer Job Description 7:45:53 ML Engineer Resume 7:54:48 Machine Learning Interview Questions
@combardus9309
2 semesters worth of classes in one vid. Thanks
@sanjeev9073
this is literally a piece of god, 10 hr video and not a single add. God bless you
@ganeshmankarwtxitu8228
The best training I had seen till now on youtube, no adds, no waste of time, very detail!
@madarauchiha550
This channel is doing more good than government.
@HARDIKSHARMA100
Oh my god! I wanted this thing so badly. Thanks .Love from 🇱🇰 SriLanka.
@sandeepvy1347
Successfully completed 9 hour 40 minutes course in 3 days 😧. Thanks a lot😘
@smobamnsoor
This is Amazing video. I am a PhD students in CS and I feel I benefited from this tutorial more than all of m study :)
@sengonzi2010
Wow . This is is one of the best ML youtube courses i have ever attended. Its a job well done , its informative , it teaches you everything u wanna know about ML in one short. Thanks guys a job well done!
@shabbirahmedosmani6126
Thanks a lot! It's really a treasurer for all in programming.
@gaitondegaoni1696
You know the video will be great when you see an indian guy in it
@righteousness8606
I'm up to 5 hours into the video so far.. excellent teaching, looking forward to the last parts..thank you.
@pranavbhardwaj8352
Dear Edureka! This is one of the finest videos I have seen that has really helped me prepare well for my campus placement process
@greatideagreatinformation4094
Please make more courses on NLP : Crash Course, Interview Questions, Resume building for NLP freshers.
@smarttechproject9876
Thanks for the awesome tutorial, very informative.
When I run the code in "54:14 Demo: Iris Dataset ", everything went well until the part that i was supposed to run the algorithms and test the accuracy. The code gave me error. Kindly find below the code, which part is wrong?
# Evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
When I run the above code, it gives this error
ValueError: Setting a random_state has no effect since shuffle is False. You should leave random_state to its default (None), or set shuffle=True.
@mohammadshahbandepaxdljln9883
Ohh! Thanks a lot. Finally i got this.
@Aditi2495
edureka team thanks a ton for taking these sections that too for free..
@tharunramesh9310
machine learning has been my nightmare for yrs. Not anymore ; All thanks to the faculty invlolved in this video and Edureka!!!!!!!
@cygmoid
Really resourceful. This course helped get up to speed on the subject. Thank you Edureka
@edurekaIN