Complete Python for Data Science & Machine Learning from A-Z

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Oak Academy,OAK Academy Team

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1. Installations

1. Installing Anaconda Distribution for Windows.mp4

10:35

2. Installing Anaconda Distribution for MacOs.mp4

18:11

3. Installing Anaconda Distribution for Linux.mp4

14:43

4. Reviewing The Jupyter Notebook.mp4

12:54

5. Reviewing The Jupyter Lab.mp4

11:36

2. First Step to Coding

1. Python Introduction.mp4

05:31

2. Project Files.html

3. First Step to Coding.mp4

07:05

4. Using Quotation Marks in Python Coding.mp4

08:20

5. How Should the Coding Form and Style Be (Pep8).mp4

09:50

6. Quiz.html

3. Basic Operations with Python

1. Introduction to Basic Data Structures in Python.mp4

08:16

2. Performing Assignment to Variables.mp4

10:10

3. Performing Complex Assignment to Variables.mp4

04:56

4. Type Conversion.mp4

09:03

5. Arithmetic Operations in Python.mp4

09:53

6. Examining the Print Function in Depth.mp4

07:29

7. Escape Sequence Operations.mp4

08:25

8. Quiz.html

4. Boolean Data Type in Python Programming Language

1. Boolean Logic Expressions.mp4

05:03

2. Order Of Operations In Boolean Operators.mp4

01:12

3. Practice with Python.mp4

11:56

4. Quiz.html

5. String Data Type in Python Programming Language

1. Examining Strings Specifically.mp4

08:31

2. Accessing Length Information (Len Method).mp4

02:41

3. Search Method In Strings Startswith(), Endswith().mp4

11:24

4. Character Change Method In Strings Replace().mp4

05:06

5. Spelling Substitution Methods in String.mp4

05:07

6. Character Clipping Methods in String.mp4

06:35

7. Indexing and Slicing Character String.mp4

08:02

8. Complex Indexing and Slicing Operations.mp4

10:48

9. String Formatting with Arithmetic Operations.mp4

06:22

10. String Formatting With % Operator.mp4

10:24

11. String Formatting With String.Format Method.mp4

08:17

12. String Formatting With f-string Method.mp4

05:51

13. Quiz.html

6. List Data Structure in Python Programming Language

1. Creation of List.mp4

11:06

2. Reaching List Elements Indexing and Slicing.mp4

08:07

3. Adding And Modifying And Deleting Elements of List.mp4

07:46

4. Adding and Deleting by Methods.mp4

05:31

5. Adding and Deleting by Index.mp4

04:59

6. Other List Methods.mp4

06:07

7. Quiz.html

7. Tuple Data Structure in Python Programming Language

1. Creation of Tuple.mp4

09:52

2. Reaching Tuple Elements Indexing And Slicing.mp4

04:24

3. Quiz.html

8. Dictionary Data Structure in Python Programming Language

1. Creation of Dictionary.mp4

06:02

2. Reaching Dictionary Elements.mp4

08:00

3. Adding And Changing And Deleting Elements in Dictionary.mp4

03:40

4. Dictionary Methods.mp4

07:46

5. Quiz.html

9. Set Data Structure in Python Programming Language

1. Creation of Set.mp4

08:08

2. Adding And Removing Elements Methods in Sets.mp4

04:44

3. Difference Operation Methods In Sets.mp4

05:18

4. Intersection And Union Methods In Sets.mp4

02:33

5. Asking Questions to Sets with Methods.mp4

06:06

6. Quiz.html

10. Conditional Expressions in Python Programming Language

1. Comparison Operators.mp4

06:17

2. Structure of if Statements.mp4

08:30

3. Structure of if-else Statements.mp4

04:36

4. Structure of if-elif-else Statements.mp4

09:21

5. Structure of Nested if-elif-else Statements.mp4

10:01

6. Coordinated Programming with IF and INPUT.mp4

07:29

7. Ternary Condition.mp4

05:14

8. Quiz.html

11. For Loop in Python Programming Language

1. For Loop in Python.mp4

07:17

2. For Loop in Python(Reinforcing the Topic).mp4

07:06

3. Using Conditional Expressions and For Loop Together.mp4

10:01

4. Continue Command.mp4

03:22

5. Break Command.mp4

04:39

6. List Comprehension.mp4

07:48

7. Quiz.html

12. While Loop in Python Programming Language

1. While Loop in Python.mp4

05:38

2. While Loops in Python Reinforcing the Topic.mp4

14:19

3. Quiz.html

13. Functions in Python Programming Language

1. Getting know to the Functions.mp4

08:32

2. How to Write Function.mp4

06:59

3. Return Expression in Functions.mp4

05:11

4. Writing Functions with Multiple Argument.mp4

05:02

5. Writing Docstring in Functions.mp4

05:02

6. Using Functions and Conditional Expressions Together.mp4

10:57

7. Quiz.html

14. Arguments And Parameters in Python Programming Language

1. Arguments and Parameters.mp4

11:15

2. High Level Operations with Arguments.mp4

12:54

3. Quiz.html

15. Most Used Functions in Python Programming Language

1. all(), any() Functions.mp4

05:52

2. map() Function.mp4

04:58

3. filter() Function.mp4

04:42

4. zip() Function.mp4

04:22

5. enumerate() Function.mp4

03:30

6. max(), min() Functions.mp4

02:08

7. sum() Function.mp4

01:44

8. round() Function.mp4

04:14

9. Lambda Function.mp4

11:46

10. Quiz.html

16. Class Structure in Python Programming Language

1. Local and Global Variables.mp4

04:08

2. Features of Class.mp4

08:09

3. Instantiation of Class.mp4

06:58

4. Attribute of Instantiation.mp4

09:31

5. Write Function in the Class.mp4

07:09

6. Inheritance Structure.mp4

11:34

17. NumPy Library Introduction

1. Introduction to NumPy Library.mp4

06:24

2. Notebook Project Files Link regarding NumPy Python Programming Language Library.html

3. The Power of NumPy.mp4

16:04

4. Quiz.html

18. Creating NumPy Array in Python

1. Creating NumPy Array with The Array() Function.mp4

08:16

2. Creating NumPy Array with Zeros() Function.mp4

05:05

3. Creating NumPy Array with Ones() Function.mp4

03:06

4. Creating NumPy Array with Full() Function.mp4

02:49

5. Creating NumPy Array with Arange() Function.mp4

02:55

6. Creating NumPy Array with Eye() Function.mp4

03:08

7. Creating NumPy Array with Linspace() Function.mp4

01:31

8. Creating NumPy Array with Random() Function.mp4

08:29

9. Properties of NumPy Array.mp4

05:24

10. Quiz.html

19. Functions in the NumPy Library

1. Reshaping a NumPy Array Reshape() Function.mp4

05:56

2. Identifying the Largest Element of a Numpy Array.mp4

03:45

3. Detecting Least Element of Numpy Array Min(), Ar.mp4

02:35

4. Concatenating Numpy Arrays Concatenate() Functio.mp4

09:40

5. Splitting One-Dimensional Numpy Arrays The Split.mp4

05:45

6. Splitting Two-Dimensional Numpy Arrays Split(),.mp4

09:33

7. Sorting Numpy Arrays Sort() Function.mp4

04:16

8. Quiz.html

20. Indexing, Slicing, and Assigning NumPy Arrays

1. Indexing Numpy Arrays.mp4

07:39

2. Slicing One-Dimensional Numpy Arrays.mp4

06:08

3. Slicing Two-Dimensional Numpy Arrays.mp4

09:30

4. Assigning Value to One-Dimensional Arrays.mp4

05:02

5. Assigning Value to Two-Dimensional Array.mp4

09:57

6. Fancy Indexing of One-Dimensional Arrrays.mp4

06:09

7. Fancy Indexing of Two-Dimensional Arrrays.mp4

12:32

8. Combining Fancy Index with Normal Indexing.mp4

03:25

9. Combining Fancy Index with Normal Slicing.mp4

04:36

21. Operations in Numpy Library

1. Operations with Comparison Operators.mp4

06:09

2. Arithmetic Operations in Numpy.mp4

15:10

3. Statistical Operations in Numpy.mp4

06:35

4. Solving Second-Degree Equations with NumPy.mp4

07:00

22. Pandas Library Introduction

1. Introduction to Pandas Library.mp4

06:38

2. Pandas Project Files Link.html

23. Series Structures in the Pandas Library

1. Creating a Pandas Series with a List.mp4

10:21

2. Creating a Pandas Series with a Dictionary.mp4

04:53

3. Creating Pandas Series with NumPy Array.mp4

03:10

4. Object Types in Series.mp4

05:14

5. Examining the Primary Features of the Pandas Seri.mp4

04:55

6. Most Applied Methods on Pandas Series.mp4

12:53

7. Indexing and Slicing Pandas Series.mp4

07:12

24. DataFrame Structures in Pandas Library

1. Creating Pandas DataFrame with List.mp4

05:33

2. Creating Pandas DataFrame with NumPy Array.mp4

03:03

3. Creating Pandas DataFrame with Dictionary.mp4

04:01

4. Examining the Properties of Pandas DataFrames.mp4

06:32

25. Element Selection Operations in DataFrame Structures

1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4

07:41

2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4

06:04

3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4

08:42

4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4

07:33

5. Top Level Element Selection in Pandas DataFramesLesson 3.mp4

05:35

6. Element Selection with Conditional Operations in.mp4

11:23

26. Structural Operations on Pandas DataFrame

1. Adding Columns to Pandas Data Frames.mp4

08:16

2. Removing Rows and Columns from Pandas Data frames.mp4

04:00

3. Null Values in Pandas Dataframes.mp4

14:42

4. Dropping Null Values Dropna() Function.mp4

07:14

5. Filling Null Values Fillna() Function.mp4

11:36

6. Setting Index in Pandas DataFrames.mp4

07:03

27. Multi-Indexed DataFrame Structures

1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp4

09:16

2. Element Selection in Multi-Indexed DataFrames.mp4

05:12

3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4

07:03

28. Structural Concatenation Operations in Pandas DataFrame

1. Concatenating Pandas Dataframes Concat Function.mp4

12:40

2. Merge Pandas Dataframes Merge() Function Lesson 1.mp4

10:44

3. Merge Pandas Dataframes Merge() Function Lesson 2.mp4

05:37

4. Merge Pandas Dataframes Merge() Function Lesson 3.mp4

09:44

5. Merge Pandas Dataframes Merge() Function Lesson 4.mp4

07:34

6. Joining Pandas Dataframes Join() Function.mp4

11:41

29. Functions That Can Be Applied on a DataFrame

1. Loading a Dataset from the Seaborn Library.mp4

06:41

2. Examining the Data Set 1.mp4

07:29

3. Aggregation Functions in Pandas DataFrames.mp4

21:45

4. Examining the Data Set 2.mp4

10:38

5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp4

18:14

6. Advanced Aggregation Functions Aggregate() Function.mp4

07:40

7. Advanced Aggregation Functions Filter() Function.mp4

06:30

8. Advanced Aggregation Functions Transform() Function.mp4

11:38

9. Advanced Aggregation Functions Apply() Function.mp4

10:06

30. Pivot Tables in Pandas Library

1. Examining the Data Set 3.mp4

08:14

2. Pivot Tables in Pandas Library.mp4

10:35

31. File Operations in Pandas Library

1. Accessing and Making Files Available.mp4

05:11

2. Data Entry with Csv and Txt Files.mp4

13:35

3. Data Entry with Excel Files.mp4

04:24

4. Outputting as an CSV Extension.mp4

07:09

5. Outputting as an Excel File.mp4

03:43

32. Matplotlib

1. What is Matplotlib.mp4

03:02

2. Using Pyplot.mp4

07:29

3. Pyplot Pylab - Matplotlib.mp4

07:19

4. Figure, Subplot and Axes.mp4

17:28

5. Figure Customization.mp4

14:47

6. Plot Customization.mp4

06:44

7. Grid, Spines, Ticks.mp4

07:05

8. Basic Plots in Matplotlib I.mp4

26:47

9. Basic Plots in Matplotlib II.mp4

13:28

33. Seaborn

1. What is Seaborn.mp4

04:09

2. Controlling Figure Aesthetics in Seaborn.mp4

10:21

3. Example in Seaborn.mp4

09:07

4. Color Palettes in Seaborn.mp4

13:00

5. Basic Plots in Seaborn.mp4

19:57

6. Multi-Plots in Seaborn.mp4

09:19

7. Regression Plots and Squarify in Seaborn.mp4

14:22

34. Geoplotlib

1. What is Geoplotlib.mp4

08:43

2. Example - 1.mp4

08:16

3. Example - 2.mp4

16:08

4. Example - 3.mp4

09:39

35. Intro to Machine Learning with Python

1. What is Machine Learning.mp4

03:52

2. Machine Learning Terminology.mp4

02:31

3. Machine Learning Project Files.html

4. Quiz.html

36. Evaluation Metrics in Machine Learning

1. Classification vs Regression in Machine Learning.mp4

03:23

2. Machine Learning Model Performance Evaluation Classification Error Metrics.mp4

18:01

3. Evaluating Performance Regression Error Metrics in Python.mp4

09:51

4. Machine Learning With Python.mp4

18:13

5. Quiz.html

37. Supervised Learning with Machine Learning

1. What is Supervised Learning in Machine Learning.mp4

05:06

2. Quiz.html

38. Linear Regression Algorithm in Machine Learning A-Z

1. Linear Regression Algorithm Theory in Machine Learning A-Z.mp4

07:47

2. Linear Regression Algorithm With Python Part 1.mp4

14:57

3. Linear Regression Algorithm With Python Part 2.mp4

23:39

4. Linear Regression Algorithm With Python Part 3.mp4

15:46

5. Linear Regression Algorithm With Python Part 4.mp4

19:22

39. Bias Variance Trade-Off in Machine Learning

1. What is Bias Variance Trade-Off.mp4

10:47

2. Quiz.html

40. Logistic Regression Algorithm in Machine Learning A-Z

1. What is Logistic Regression Algorithm in Machine Learning.mp4

04:39

2. Logistic Regression Algorithm with Python Part 1.mp4

13:45

3. Logistic Regression Algorithm with Python Part 2.mp4

18:16

4. Logistic Regression Algorithm with Python Part 3.mp4

07:53

5. Logistic Regression Algorithm with Python Part 4.mp4

09:18

6. Logistic Regression Algorithm with Python Part 5.mp4

08:11

7. Quiz.html

41. K-fold Cross-Validation in Machine Learning A-Z

1. K-Fold Cross-Validation Theory.mp4

04:17

2. K-Fold Cross-Validation with Python.mp4

06:33

42. K Nearest Neighbors Algorithm in Machine Learning A-Z

1. K Nearest Neighbors Algorithm Theory.mp4

06:33

2. K Nearest Neighbors Algorithm with Python Part 1.mp4

07:22

3. K Nearest Neighbors Algorithm with Python Part 2.mp4

12:06

4. K Nearest Neighbors Algorithm with Python Part 3.mp4

07:46

5. Quiz.html

43. Hyperparameter Optimization

1. Hyperparameter Optimization Theory.mp4

06:24

2. Hyperparameter Optimization with Python.mp4

09:56

44. Decision Tree Algorithm in Machine Learning A-Z

1. Decision Tree Algorithm Theory.mp4

09:18

2. Decision Tree Algorithm with Python Part 1.mp4

07:06

3. Decision Tree Algorithm with Python Part 2.mp4

08:35

4. Decision Tree Algorithm with Python Part 3.mp4

03:27

5. Decision Tree Algorithm with Python Part 4.mp4

09:08

6. Decision Tree Algorithm with Python Part 5.mp4

05:58

7. Quiz.html

45. Random Forest Algorithm in Machine Learning A-Z

1. Random Forest Algorithm Theory.mp4

05:46

2. Random Forest Algorithm with Pyhon Part 1.mp4

05:54

3. Random Forest Algorithm with Pyhon Part 2.mp4

08:15

46. Support Vector Machine Algorithm in Machine Learning A-Z

1. Support Vector Machine Algorithm Theory.mp4

05:08

2. Support Vector Machine Algorithm with Python Part 1.mp4

05:30

3. Support Vector Machine Algorithm with Python Part 2.mp4

08:15

4. Support Vector Machine Algorithm with Python Part 3.mp4

10:43

5. Support Vector Machine Algorithm with Python Part 4.mp4

08:42

6. Quiz.html

47. Unsupervised Learning with Machine Learning

1. Unsupervised Learning Overview.mp4

03:30

2. Quiz.html

48. K Means Clustering Algorithm in Machine Learning A-Z

1. K Means Clustering Algorithm Theory.mp4

04:10

2. K Means Clustering Algorithm with Python Part 1.mp4

07:06

3. K Means Clustering Algorithm with Python Part 2.mp4

06:50

4. K Means Clustering Algorithm with Python Part 3.mp4

06:51

5. K Means Clustering Algorithm with Python Part 4.mp4

07:08

6. Quiz.html

49. Hierarchical Clustering Algorithm in machine learning data science

1. Hierarchical Clustering Algorithm Theory.mp4

04:39

2. Hierarchical Clustering Algorithm with Python Part 1.mp4

07:50

3. Hierarchical Clustering Algorithm with Python Part 2.mp4

05:54

4. Quiz.html

50. Principal Component Analysis (PCA) in Machine Learning A-Z

1. Principal Component Analysis (PCA) Theory.mp4

08:47

2. Principal Component Analysis (PCA) with Python Part 1.mp4

05:17

3. Principal Component Analysis (PCA) with Python Part 2.mp4

01:55

4. Principal Component Analysis (PCA) with Python Part 3.mp4

07:30

51. Recommender System Algorithm in Machine Learning A-Z

1. What is the Recommender System Part 1.mp4

04:57

2. What is the Recommender System Part 2.mp4

04:23

3. Quiz.html

52. First Contact with Kaggle

1. What is Kaggle.mp4

15:57

2. FAQ about Kaggle.html

3. Registering on Kaggle and Member Login Procedures.mp4

06:06

4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html

5. Getting to Know the Kaggle Homepage.mp4

17:45

6. Quiz.html

53. Competition Section on Kaggle

1. Competitions on Kaggle Lesson 1.mp4

22:44

2. Competitions on Kaggle Lesson 2.mp4

21:25

3. Quiz.html

54. Dataset Section on Kaggle

1. Datasets on Kaggle.mp4

15:59

2. Quiz.html

55. Code Section on Kaggle

1. Examining the Code Section in Kaggle Lesson 1.mp4

12:39

2. Examining the Code Section in Kaggle Lesson 2.mp4

14:49

3. Examining the Code Section in Kaggle Lesson 3.mp4

19:54

4. Quiz.html

56. Discussion Section on Kaggle

1. What is Discussion on Kaggle.mp4

05:39

2. Quiz.html

57. Other Most Used Options on Kaggle

1. Courses in Kaggle.mp4

06:47

2. Ranking Among Users on Kaggle.mp4

15:33

3. Blog and Documentation Sections.mp4

04:48

4. Quiz.html

58. Details on Kaggle

1. User Page Review on Kaggle.mp4

10:37

2. Treasure in The Kaggle.mp4

07:41

3. Publishing Notebooks on Kaggle.mp4

05:10

4. What Should Be Done to Achieve Success in Kaggle.mp4

08:23

5. Quiz.html

59. Introduction to Machine Learning with Real Hearth Attack Prediction Project

1. First Step to the Hearth Attack Prediction Project.mp4

15:15

2. FAQ about Machine Learning, Data Science.html

3. Notebook Design to be Used in the Project.mp4

14:16

4. Project Link File - Hearth Attack Prediction Project, Machine Learning.html

5. Examining the Project Topic.mp4

10:00

6. Recognizing Variables In Dataset.mp4

17:02

7. Quiz.html

60. First Organization

1. Required Python Libraries.mp4

08:40

2. Loading the Statistics Dataset in Data Science.mp4

01:47

3. Initial analysis on the dataset.mp4

12:21

4. Quiz.html

61. Preparation For Exploratory Data Analysis (EDA) in Data Science

1. Examining Missing Values.mp4

10:04

2. Examining Unique Values.mp4

09:10

3. Separating variables (Numeric or Categorical).mp4

03:12

4. Examining Statistics of Variables.mp4

18:12

5. Quiz.html

62. Exploratory Data Analysis (EDA) - Uni-variate Analysis

1. Numeric Variables (Analysis with Distplot) Lesson 1.mp4

14:29

2. Numeric Variables (Analysis with Distplot) Lesson 2.mp4

03:57

3. Categoric Variables (Analysis with Pie Chart) Lesson 1.mp4

13:54

4. Categoric Variables (Analysis with Pie Chart) Lesson 2.mp4

15:39

5. Examining the Missing Data According to the Analysis Result.mp4

10:09

6. Quiz.html

63. Exploratory Data Analysis (EDA) - Bi-variate Analysis

1. Numeric Variables Target Variable (Analysis with FacetGrid) Lesson 1.mp4

08:32

2. Numeric Variables Target Variable (Analysis with FacetGrid) Lesson 2.mp4

07:30

3. Categoric Variables Target Variable (Analysis with Count Plot) Lesson 1.mp4

03:57

4. Categoric Variables Target Variable (Analysis with Count Plot) Lesson 2.mp4

12:56

5. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1.mp4

04:56

6. Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2.mp4

06:54

7. Feature Scaling with the Robust Scaler Method.mp4

09:00

8. Creating a New DataFrame with the Melt() Function.mp4

11:22

9. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 1.mp4

06:25

10. Numerical - Categorical Variables (Analysis with Swarm Plot) Lesson 2.mp4

11:10

11. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 1.mp4

07:19

12. Numerical - Categorical Variables (Analysis with Box Plot) Lesson 2.mp4

07:44

13. Relationships between variables (Analysis with Heatmap) Lesson 1.mp4

06:04

14. Relationships between variables (Analysis with Heatmap) Lesson 2.mp4

12:31

15. Quiz.html

64. Preparation for Modelling in Machine Learning

1. Dropping Columns with Low Correlation.mp4

03:46

2. Visualizing Outliers.mp4

08:31

3. Dealing with Outliers Trtbps Variable Lesson 1.mp4

09:57

4. Dealing with Outliers Trtbps Variable Lesson 2.mp4

10:53

5. Dealing with Outliers Thalach Variable.mp4

08:21

6. Dealing with Outliers Oldpeak Variable.mp4

07:50

7. Determining Distributions of Numeric Variables.mp4

05:02

8. Transformation Operations on Unsymmetrical Data.mp4

04:55

9. Applying One Hot Encoding Method to Categorical Variables.mp4

05:24

10. Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms.mp4

02:28

11. Separating Data into Test and Training Set.mp4

07:04

12. Quiz.html

65. Modelling for Machine Learning

1. Logistic Regression.mp4

06:53

2. Cross Validation.mp4

05:40

3. Roc Curve and Area Under Curve (AUC).mp4

08:16

4. Hyperparameter Optimization (with GridSearchCV).mp4

12:53

5. Decision Tree Algorithm.mp4

05:05

6. Support Vector Machine Algorithm.mp4

05:02

7. Random Forest Algorithm.mp4

06:17

8. Hyperparameter Optimization (with GridSearchCV).mp4

10:53

9. Quiz.html

66. Conclusion

1. Project Conclusion and Sharing.mp4

03:31

2. Quiz.html

67. Extra

1. Complete Python for Data Science And Machine Learning from A-Z.html

Description

Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z

What You'll Learn?

Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks.
Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames.
Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
Pandas Pyhon aims to be the fundamental high-level building block for doing practical, real world data analysis in Python
Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices.
NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.
NumPy brings the computational power of languages like C and Fortran to Python.
Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries.
Learn Machine Learning with Hands-On Examples
What is Machine Learning?
Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective.
Python is a general-purpose, object-oriented, high-level programming language.
Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles
Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis.
Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python is one of the most important skills
Its simple syntax and readability makes Python perfect for Flask, Django, data science, and machine learning.
Installing Anaconda Distribution for Windows
Installing Anaconda Distribution for MacOs
Installing Anaconda Distribution for Linux
Reviewing The Jupyter Notebook
Reviewing The Jupyter Lab
Python Introduction
First Step to Coding
Using Quotation Marks in Python Coding
How Should the Coding Form and Style Be (Pep8)
Introduction to Basic Data Structures in Python
Performing Assignment to Variables
Performing Complex Assignment to Variables
Type Conversion
Arithmetic Operations in Python
Examining the Print Function in Depth
Escape Sequence Operations
Boolean Logic Expressions
Order Of Operations In Boolean Operators
Practice with Python
Examining Strings Specifically
Accessing Length Information (Len Method)
Search Method In Strings Startswith(), Endswith()
Character Change Method In Strings Replace()
Spelling Substitution Methods in String
Character Clipping Methods in String
Indexing and Slicing Character String
Complex Indexing and Slicing Operations
String Formatting with Arithmetic Operations
String Formatting With % Operator
String Formatting With String Format Method
String Formatting With f-string Method
Creation of List
Reaching List Elements – Indexing and Slicing
Adding & Modifying & Deleting Elements of List
Adding and Deleting by Methods
Adding and Deleting by Index
Other List Methods
Creation of Tuple
Reaching Tuple Elements Indexing And Slicing
Creation of Dictionary
Reaching Dictionary Elements
Adding & Changing & Deleting Elements in Dictionary
Dictionary Methods
Creation of Set
Adding & Removing Elements Methods in Sets
Difference Operation Methods In Sets
Intersection & Union Methods In Sets
Asking Questions to Sets with Methods
Comparison Operators
Structure of “if” Statements
Structure of “if-else” Statements
Structure of “if-elif-else” Statements
Structure of Nested “if-elif-else” Statements
Coordinated Programming with “IF” and “INPUT”
Ternary Condition
For Loop in Python
For Loop in Python(Reinforcing the Topic)
Using Conditional Expressions and For Loop Together
Continue Command
Break Command
List Comprehension
While Loop in Python
While Loops in Python Reinforcing the Topic
Getting know to the Functions
How to Write Function
Return Expression in Functions
Writing Functions with Multiple Argument
Writing Docstring in Functions
Using Functions and Conditional Expressions Together
Arguments and Parameters
High Level Operations with Arguments
all(), any() Functions
map() Function
filter() Function
zip() Function
enumerate() Function
max(), min() Functions
sum() Function
round() Function
Lambda Function
Local and Global Variables
Features of Class
Instantiation of Class
Attribute of Instantiation
Write Function in the Class
Inheritance Structure

Who is this for?

Anyone who wants to start learning Python bootcamp

Anyone who plans a career as Python developer

Anyone who needs a complete guide on how to start and continue their career with Python in data analysis

And also, who want to learn how to develop ptyhon coding

People who want to learn python

People who want to learn python programming

People who want to learn python programming, python examples

What You Need to Know?

A working computer (Windows, Mac, or Linux)

No prior knowledge of Python for beginners is required

Motivation to learn the the second largest number of job postings relative program language among all others

Desire to learn machine learning python

Curiosity for python programming

Desire to learn python programming, pycharm, python pycharm

Nothing else! It’s just you, your computer and your ambition to get started today

More details

Description
Welcome to my " Complete Python for Data Science & Machine Learning from A-Z " course.
Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z

Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn't specialized for any specific problems.

Python instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.
Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn.
Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.
Do you want to learn one of the employerâ€™s most requested skills? If you think so, you are at the right place. Python, machine learning, Django, python programming, machine learning python, python Bootcamp, coding, data science, data analysis, programming languages.
We've designed for you "Complete Python for Data Science & Machine Learning from A-Zâ€ a straightforward course for the Complete Python programming language.
In the course, you will have down-to-earth way explanations of hands-on projects. With my course, you will learn Python Programming step-by-step. I made Python 3 programming simple and easy with exercises, challenges, and lots of real-life examples.
This Python course is for everyone!
My "Python: Learn Python with Real Python Hands-On Examples" is for everyone! If you donâ€™t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher).
Why Python?
Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, that it supports a lot of todayâ€™s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development.
No prior knowledge is needed!
Python doesn't need any prior knowledge to learn it and the Ptyhon code is easy to understand for beginners.
What you will learn?
In this course, we will start from the very beginning and go all the way to programming with hands-on examples . We will first learn how to set up a lab and install needed software on your machine. Then during the course, you will learn the fundamentals of Python development like
Installing Anaconda Distribution for Windows
Installing Anaconda Distribution for MacOs
Installing Anaconda Distribution for Linux
Reviewing The Jupyter Notebook
Reviewing The Jupyter Lab
Python Introduction
First Step to Coding
Using Quotation Marks in Python Coding
How Should the Coding Form and Style Be (Pep8)
Introduction to Basic Data Structures in Python
Performing Assignment to Variables
Performing Complex Assignment to Variables
Type Conversion
Arithmetic Operations in Python
Examining the Print Function in Depth
Escape Sequence Operations
Boolean Logic Expressions
Order Of Operations In Boolean Operators
Practice with Python
Examining Strings Specifically
Accessing Length Information (Len Method)
Search Method In Strings Startswith(), Endswith()
Character Change Method In Strings Replace()
Spelling Substitution Methods in String
Character Clipping Methods in String
Indexing and Slicing Character String
Complex Indexing and Slicing Operations
String Formatting with Arithmetic Operations
String Formatting With % Operator
String Formatting With String.Format Method
String Formatting With f-string Method
Creation of List
Reaching List Elements â€“ Indexing and Slicing
Adding & Modifying & Deleting Elements of List
Adding and Deleting by Methods
Adding and Deleting by Index
Other List Methods
Creation of Tuple
Reaching Tuple Elements Indexing And Slicing
Creation of Dictionary
Reaching Dictionary Elements
Adding & Changing & Deleting Elements in Dictionary
Dictionary Methods
Creation of Set
Adding & Removing Elements Methods in Sets
Difference Operation Methods In Sets
Intersection & Union Methods In Sets
Asking Questions to Sets with Methods
Comparison Operators
Structure of â€œifâ€ Statements
Structure of â€œif-elseâ€ Statements
Structure of â€œif-elif-elseâ€ Statements
Structure of Nested â€œif-elif-elseâ€ Statements
Coordinated Programming with â€œIFâ€ and â€œINPUTâ€
Ternary Condition
For Loop in Python
For Loop in Python(Reinforcing the Topic)
Using Conditional Expressions and For Loop Together
Continue Command
Break Command
List Comprehension
While Loop in Python
While Loops in Python Reinforcing the Topic
Getting know to the Functions
How to Write Function
Return Expression in Functions
Writing Functions with Multiple Argument
Writing Docstring in Functions
Using Functions and Conditional Expressions Together
Arguments and Parameters
High Level Operations with Arguments
all(), any() Functions
map() Function
filter() Function
zip() Function
enumerate() Function
max(), min() Functions
sum() Function
round() Function
Lambda Function
Local and Global Variables
Features of Class
Instantiation of Class
Attribute of Instantiation
Write Function in the Class
Inheritance Structure
With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.
Do not forget ! Python for beginners has the second largest number of job postings relative to all other languages. So it will earn you a lot of money and will bring a great change in your resume.

What is python?
Machine learning python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python bootcamp is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing different tools for programmers suited for a variety of tasks.

Python vs. R: What is the Difference?
Python and R are two of today's most popular programming tools. When deciding between Python and R in data science , you need to think about your specific needs. On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets. On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance.

What does it mean that Python is object-oriented?
Python is a multi-paradigm language, which means that it supports many data analysis programming approaches. Along with procedural and functional programming styles, Python also supports the object-oriented style of programming. In object-oriented programming, a developer completes a programming project by creating Python objects in code that represent objects in the actual world. These objects can contain both the data and functionality of the real-world object. To generate an object in Python you need a class. You can think of a class as a template. You create the template once, and then use the template to create as many objects as you need. Python classes have attributes to represent data and methods that add functionality. A class representing a car may have attributes like color, speed, and seats and methods like driving, steering, and stopping.

What are the limitations of Python?
Python is a widely used, general-purpose programming language, but it has some limitations. Because Python in machine learning is an interpreted, dynamically typed language, it is slow compared to a compiled, statically typed language like C. Therefore, Python is useful when speed is not that important. Python's dynamic type system also makes it use more memory than some other programming languages, so it is not suited to memory-intensive applications. The Python virtual engine that runs Python code runs single-threaded, making concurrency another limitation of the programming language. Though Python is popular for some types of game development, its higher memory and CPU usage limits its usage for high-quality 3D game development. That being said, computer hardware is getting better and better, and the speed and memory limitations of Python are getting less and less relevant.

How is Python used?
Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks in the background. Many of the scripts that ship with Linux operating systems are Python scripts. Python is also a popular language for machine learning, data analytics, data visualization, and data science because its simple syntax makes it easy to quickly build real applications. You can use Python to create desktop applications. Many developers use it to write Linux desktop applications, and it is also an excellent choice for web and game development. Python web frameworks like Flask and Django are a popular choice for developing web applications. Recently, Python is also being used as a language for mobile development via the Kivy third-party library.

What jobs use Python?
Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website and server deployments. Web developers use Python to build web applications, usually with one of Python's popular web frameworks like Flask or Django. Data scientists and data analysts use Python to build machine learning models, generate data visualizations, and analyze big data. Financial advisors and quants (quantitative analysts) use Python to predict the market and manage money. Data journalists use Python to sort through information and create stories. Machine learning engineers use Python to develop neural networks and artificial intelligent systems.

How do I learn Python on my own?
Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar with the syntax. But you only need to know a little bit about Python syntax to get started writing real code; you will pick up the rest as you go. Depending on the purpose of using it, you can then find a good Python tutorial, book, or course that will teach you the programming language by building a complete application that fits your goals. If you want to develop games, then learn Python game development. If you're going to build web applications, you can find many courses that can teach you that, too. Udemyâ€™s online courses are a great place to start if you want to learn Python on your own.

Why would you want to take this course?
Our answer is simple: The quality of teaching.
OAK Academy based in London is an online education company. OAK Academy gives education in the field of IT, Software, Design, development in English, Portuguese, Spanish, Turkish, and a lot of different languages on the Udemy platform where it has over 2000 hours of video education lessons. OAK Academy both increases its education series number by publishing new courses, and it makes students aware of all the innovations of already published courses by upgrading.
When you enroll, you will feel the OAK Academy`s seasoned developers' expertise.Â Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest.

Video and Audio Production Quality
All our videos are created/produced as high-quality video and audio to provide you the best learning experience.
You will be,
Seeing clearly
Hearing clearly
Moving through the course without distractions
You'll also get:
Lifetime Access to The Course
Fast & Friendly Support in the Q&A section
Udemy Certificate of Completion Ready for Download
Dive in now!

We offer full support, answering any questions.
See you in theÂ " Complete Python for Data Science & Machine Learning from A-Z " course.
Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z
Who this course is for:
Anyone who wants to start learning Python bootcamp
Anyone who plans a career as Python developer
Anyone who needs a complete guide on how to start and continue their career with Python in data analysis
And also, who want to learn how to develop ptyhon coding
People who want to learn python
People who want to learn python programming
People who want to learn python programming, python examples

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Hi there,By 2024, there will be more than 1 million unfilled computing jobs and the skills gap is a global problem. This was our starting point.At OAK Academy, we are the tech experts who have been in the sector for years and years. We are deeply rooted in the tech world. We know the tech industry. And we know the tech industry's biggest problem is the “tech skills gap” and here is our solution.OAK Academy will be the bridge between the tech industry and people who-are planning a new career-are thinking career transformation-want career shift or reinvention,-have the desire to learn new hobbies at their own paceBecause we know we can help this generation gain the skill to fill these jobs and enjoy happier, more fulfilling careers. And this is what motivates us every day.We specialize in critical areas like cybersecurity, coding, IT, game development, app monetization, and mobile. Thanks to our practical alignment we are able to constantly translate industry insights into the most in-demand and up-to-date courses,OAK Academy will provide you the information and support you need to move through your journey with confidence and ease.Our courses are for everyone. Whether you are someone who has never programmed before, or an existing programmer seeking to learn another language, or even someone looking to switch careers we are here.OAK Academy here to transforms passionate, enthusiastic people to reach their dream job positions.If you need help or if you have any questions, please do not hesitate to contact our team.

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