Advanced Bootcamp - Classification Analysis By Spotle

What you'll learn
Supervised Learning
Semi-supervised learning
Classification Analysis
Decision Tree
Discriminant Analysis
Naïve Bayes Classifier
Logistic Regression
k-Nearest Neighbor
Overview of machine learning
Overview of data science
Measuring and preparing data
Missing data imputation
Overview of R
Requirements
You will need to have a computer or a mobile handset with an internet connection
Description
Data science has become key industry drivers in the global job and opportunity market. This course with mix of lectures from industry experts and Ivy League academics will help students, recent graduates and young professionals learn classification analysis and its applications in business scenarios. In this course you will learn1. Machine learning and data science overview2. Supervised, unsupervised and semi-supervised learning3. The difference between supervised and unsupervised learning4. Preparing and measuring data5. Missing data imputation6. Discriminant Analysis7. Decision Tree8. Logistic Regression8. Naïve Bayes Classifier9. k-Nearest Neighbor10. Overview of RSo, what is supervised learning?Let's say I have labeled fruits and I kept them in separate baskets. So you have separate baskets for yellow banana, golden pineapple, black grapes and so on. Now if I give you a golden pineapple you know exactly what it is and in which basket you need to keep it. So, I am helping you classify fruits by previously labeled and classified fruits.What essentially is happening here is helping you learn about fruits which are already labeled. You know the characteristics and labels based on which they are separated into different baskets. The labeled fruits help you train your brain about their respective correct baskets. Now, for each new fruit you can put them into its respective basket. When machines learn in this way this is called supervised learning. Supervised learning is a learning in which we teach or train the machine using data which are properly or rather correctly labeled.
Overview
Section 1: Introduction
Lecture 1 Introduction To Machine Learning
Lecture 2 Introduction To Data Science
Section 2: Supervised And Unsupervised Learning
Lecture 3 Supervised Learning Vs Unsupervised Fundamentals
Lecture 4 Semi-supervised Learning
Section 3: Understanding And Preparing Data
Lecture 5 Measuring Central Tendency
Lecture 6 Measuring Skewness And Kurtosis
Lecture 7 Missing Data Imputation - Part 1
Lecture 8 Missing Data Imputation - Part 2
Section 4: Discriminant Analysis
Lecture 9 Discriminant Analysis With Case Studies
Lecture 10 Discriminant Analysis - Part 2
Section 5: Decision Tree
Lecture 11 Decision Tree Walk Through
Lecture 12 Fundamentals Of Decision Tree - Part 1
Lecture 13 Fundamentals Of Decision Tree - Part 2
Lecture 14 Decision Tree, Impurity Gain Ratio
Lecture 15 Decision Tree, Numerical Attributes - Part 1
Lecture 16 Decision Tree, Numerical Attributes - Part 2
Section 6: Logistic Regression
Lecture 17 Understanding Logistic Regression
Section 7: The Statistical Model Of Logistic Regression
Lecture 18 Logistic Regression Part 1: Introduction
Lecture 19 Logistic Regression Part 2: Likelihood Estimation
Lecture 20 Logistic Regression Part 3: Statistical Inference
Lecture 21 Logistic Regression Part 4: Measure Of Accuracy
Section 8: Naïve Bayes Classifier
Lecture 22 Naïve Bayes Classifier
Section 9: k-Nearest Neighbor
Lecture 23 k-Nearest Neighbor
Lecture 24 How To Calculate Euclidean Distance
Section 10: Overview Of R For Data Science
Lecture 25 Introduction To R - Part 1
Lecture 26 Introduction To R - Part 2
Lecture 27 Data Visualization With R
Anyone with an interest in a rewarding career in Data Science


Homepage

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