Applied Data Science and Machine Learning in R for Beginners

Focused View

Asmi Ariv

8:50:50

96 View

1. Introduction

1. Introduction.html

2. R Programming

1. R Programming 1.mp4

07:42

2. R Programming 2.mp4

20:53

3. R Programming 3.mp4

16:12

4. R Programming 4.mp4

10:52

5. R Programming 5.mp4

10:23

6.1 Lab_R_1_RM.pdf

6.2 patient.csv

6. Lab R 1.html

7.1 Lab_R_2_RM.pdf

7. Lab R 2.html

8. Quiz on R Programming.html

3. Introduction To Data Science

1. What is Data Science.mp4

03:08

2. Jargons of Data Science.mp4

09:59

4. Data Understanding and Preprocessing

1. Understanding Of Data 1.mp4

17:00

2. Understanding of Data 2.mp4

17:04

3. Probability Distributions.mp4

15:49

4. Data Cleansing and Processing.mp4

23:50

5.1 data_missing.csv

5.2 Lab_D_1_RM.pdf

5. Lab D 1.html

6.1 clinicalData.csv

6.2 countries.zip

6.3 Lab_D_2_RM.pdf

6.4 patient.csv

6.5 score.zip

6.6 Wages.csv

6. Lab D 2.html

7.1 Lab_D_3_RM.pdf

7. Lab D 3 (Optional).html

8. Quiz on data understanding and preprocessing.html

5. Linear Regression

1. Linear Regression 1.mp4

16:23

2. Linear Regression 2.mp4

17:50

3. Linear Regression 3.mp4

27:25

4. Statistical Approach (Hypothesis Tests) vs Machine Learning.mp4

23:01

5. Train, Validation and Test Sets.mp4

05:36

6. Bias-Variance Trade Off.mp4

08:30

7.1 Lab_LR_1_RM.pdf

7. Lab1.html

8.1 experiment_3.csv

8.2 Lab_LR_2_RM.pdf

8. Lab2.html

9.1 Lab_LR_3_RM.pdf

9. Lab3.html

10. Quiz on Linear Regression.html

6. Logistic Regression

1. Logistic Regression 1.mp4

07:17

2. Logistic Regression 2.mp4

14:37

3. Logistic Regression 3.mp4

12:01

4.1 Lab_LRC_1_RM.pdf

4. Lab1.html

5.1 Lab_LRC_2_RM.pdf

5. Lab2.html

6. Quiz on Logistic Regression.html

7. Artificial Neural Network

1. Artificial Neural Network 1.mp4

11:49

2. Artificial Neural Network 2.mp4

13:15

3. Artificial Neural Network 3.mp4

26:05

4. Artificial Neural Network 4.mp4

13:22

5.1 Lab_NN_1_RM.pdf

5. Lab1.html

6.1 Lab_NN_2_RM.pdf

6. Lab2.html

7. Quiz on Artificial Neural Network.html

8. Support Vector Machine

1. Support Vector Machine 1.mp4

19:32

2. Support Vector Machine 2.mp4

20:12

3. Support Vector Machine 3.mp4

16:48

4.1 Lab_SVM_1_RM.pdf

4. Lab1.html

5.1 Lab_SVM_2_RM.pdf

5. Lab2.html

6.1 Lab_SVM_3_RM.pdf

6. Lab3.html

7. Quiz on SVM.html

9. Principal Component Analysis (PCA)

1. Principal Component Analysis 1.mp4

06:22

2. Principal Component Analysis 2.mp4

21:40

3.1 Lab_PCA_1_RM.pdf

3. Lab1.html

4.1 Lab_PCA_2_RM.pdf

4. Lab2.html

5. Quiz on PCA.html

10. Clustering

1. Clustering 1.mp4

04:16

2. Clustering 2.mp4

18:22

3. Clustering 3.mp4

04:45

4.1 Lab_CLUS_1_RM.pdf

4. Lab1.html

5.1 flower_small.zip

5.2 Lab_CLUS_2_RM.pdf

5. Lab2.html

6. Quiz on Clustering.html

11. Text Analytics

1. Text Analytics 1.mp4

05:50

2. Text Analytics 2.mp4

17:38

3.1 decep_op.csv

3.2 Lab_NLP_1_RM.pdf

3. Lab1.html

4. Quiz on Text Analytics.html

12. Time Series Forecasting

1. Time Series Forecasting 1.mp4

05:06

2. Time Series Forecasting 2.mp4

17:41

3.1 Lab_TS_1_RM.pdf

3. Lab1.html

4. Quiz on Time Series.html

13. Market Basket Analysis

1. Market Basket Analysis 1.mp4

06:32

2. Market Basket Analysis 2.mp4

16:03

3.1 Lab_MBA_1_RM.pdf

3. Lab1.html

4. Quiz on Market Basket.html

Description

Learn Data Science Methods and Techniques in R. Build Machine Learning Algorithms from Scratch. Gain Insight into Data.

What You'll Learn?

Data Science Concept
R Programming Basics
Machine Learning Basics
Predictive Model Building Basics
Basics of text analytics
Basics of Time Series and Forecasting

Who is this for?

Beginners who have interest in learning about data science and machine learning

Basic understanding of how programming works and can grasp R programming will be an added advantage, but not mandatory

More details

Description
In this course, we have provided the basic understanding of data science methods and techniques and machine learning, beginning with the basics of R programming. Having gone through this course, a new beginner, who has interest in data science and machine learning, will be able to sail through on their own. What it means is that students will be able to build the advanced knowledge based on what they learn here.

We will not only use the predefined functions in R and different packages, but we will also learn how to build machine learning algorithms from scratch defining our own functions.

In this course we will cover topics such as, R programming, introduction to data science and machine learning, understanding of data, preprocessing of data, cleansing of data and how to apply data science tools to preprocess and analyze data (structured as well as unstructured) and build predictive models, perform clustering, PCA, etc.

The machine learning topics would include, supervised learning and unsupervised learning. In supervised learning we will cover topics such as linear regression, logistics regression, time series and forecasting, text analytics (part of natural language processing), neural network, support vector machine, market basket analysis (association rules).

As part of unsupervised learning, we will go through clustering, such as hierarchical clustering and non-hierarchical clustering.Â We will focus on topics such as K-means, soft and hard clustering, similarity functions, agglomerative methods (bottom up), divisive methods (top-down). We will also cover topics such as principal component analysis (PCA), and how to use both in real life.

We will look at different examples of various topics. At the end, we will go through various lab sessions on each topic, using different datasets, with all the codes and operations.

Who this course is for:
Beginners who have interest in learning about data science and machine learning
Basic understanding of how programming works and can grasp R programming will be an added advantage, but not mandatory

User Reviews

Rating

average 0

Total votes0

Focused display

Asmi Ariv has over 15 years of experience in different domains, including Data Science, Marketing and Branding, Human Resources and Counselling. He has worked with some of the top international clients in various capacities. He loves to share his knowledge and experiences with others with a single objective to add some values to the quality of their lives

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.