Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.
We will walk you stepbystep into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative subfield of Data Science.
This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way:
 Part 1 – Data Preprocessing
 Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
 Part 3 – Classification: Logistic Regression, KNN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
 Part 4 – Clustering: KMeans, Hierarchical Clustering
 Part 5 – Association Rule Learning: Apriori, Eclat
 Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
 Part 7 – Natural Language Processing: Bagofwords model and algorithms for NLP
 Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
 Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
 Part 10 – Model Selection & Boosting: kfold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Moreover, the course is packed with practical exercises which are based on reallife examples. So not only will you learn the theory, but you will also get some handson practice building your own models.
And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
 Part 1: Data Preprocessing 

1Applications of Machine Learning

2Why Machine Learning is the Future

3Important notes, tips & tricks for this course

4This PDF resource will help you a lot

5Updates on Udemy Reviews

6Installing Python and Anaconda (Mac, Linux & Windows)

7Update: Recommended Anaconda Version

8Installing R and R Studio (Mac, Linux & Windows)

9BONUS: Meet your instructors
 Part 2: Regression 

10Welcome to Part 1  Data Preprocessing

11Get the dataset

12Importing the Libraries

13Importing the Dataset

14For Python learners, summary of Objectoriented programming: classes & objects

15Missing Data

16Categorical Data

17WARNING  Update

18Splitting the Dataset into the Training set and Test set

19Feature Scaling

20And here is our Data Preprocessing Template!

21Data Preprocessing
Simple Linear Regression
Multiple Linear Regression

23How to get the dataset

24Dataset + Business Problem Description

25Simple Linear Regression Intuition  Step 1

26Simple Linear Regression Intuition  Step 2

27Simple Linear Regression in Python  Step 1

28Simple Linear Regression in Python  Step 2

29Simple Linear Regression in Python  Step 3

30Simple Linear Regression in Python  Step 4

31Simple Linear Regression in R  Step 1

32Simple Linear Regression in R  Step 2

33Simple Linear Regression in R  Step 3

34Simple Linear Regression in R  Step 4

35Simple Linear Regression
Polynomial Regression

36How to get the dataset

37Dataset + Business Problem Description

38Multiple Linear Regression Intuition  Step 1

39Multiple Linear Regression Intuition  Step 2

40Multiple Linear Regression Intuition  Step 3

41Multiple Linear Regression Intuition  Step 4

42Prerequisites: What is the PValue?

43Multiple Linear Regression Intuition  Step 5

44Multiple Linear Regression in Python  Step 1

45Multiple Linear Regression in Python  Step 2

46Multiple Linear Regression in Python  Step 3

47Multiple Linear Regression in Python  Backward Elimination  Preparation

48Multiple Linear Regression in Python  Backward Elimination  HOMEWORK !

49Multiple Linear Regression in Python  Backward Elimination  Homework Solution

50Multiple Linear Regression in Python  Automatic Backward Elimination

51Multiple Linear Regression in R  Step 1

52Multiple Linear Regression in R  Step 2

53Multiple Linear Regression in R  Step 3

54Multiple Linear Regression in R  Backward Elimination  HOMEWORK !

55Multiple Linear Regression in R  Backward Elimination  Homework Solution

56Multiple Linear Regression in R  Automatic Backward Elimination

57Multiple Linear Regression
Support Vector Regression (SVR)

58Polynomial Regression Intuition

59How to get the dataset

60Polynomial Regression in Python  Step 1

61Polynomial Regression in Python  Step 2

62Polynomial Regression in Python  Step 3

63Polynomial Regression in Python  Step 4

64Python Regression Template

65Polynomial Regression in R  Step 1

66Polynomial Regression in R  Step 2

67Polynomial Regression in R  Step 3

68Polynomial Regression in R  Step 4

69R Regression Template