๐ฅ Machine Learning Engineer Masters Program (Use Code "๐๐๐๐๐๐๐๐๐"): https://www.edureka.co/masters-program/machine-learning-engineer-training This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Tutorial for Beginners video: 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
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