Machine Learning & Data Science with Python, Kaggle & Pandas

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Oak Academy,Ali̇ CAVDAR,OAK Academy Team

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1. Installations

1. Installing Anaconda Distribution for Windows.mp4

10:35

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

3. Installing Anaconda Distribution for MacOs.mp4

06:17

4. 6 Article Advice And Links about Numpy, Numpy Pyhon.html

5. Installing Anaconda Distribution for Linux.mp4

14:43

2. NumPy Library Introduction

1. Introduction to NumPy Library.mp4

06:24

2. The Power of NumPy.mp4

16:04

3. Quiz.html

3. 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

4. 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() Function.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

5. 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

6. 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

7. Pandas Library Introduction

1. Introduction to Pandas Library.mp4

06:38

2. Pandas Project Files Link.html

3. Quiz.html

8. 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

8. Quiz.html

9. 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

5. Quiz.html

10. 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

7. Quiz.html

11. 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

7. Quiz.html

12. 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

4. Quiz.html

13. 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

7. Quiz.html

14. 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

10. Quiz.html

15. Pivot Tables in Pandas Library

1. Examining the Data Set 3.mp4

08:14

2. Pivot Tables in Pandas Library.mp4

10:35

3. Quiz.html

16. 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

6. Quiz.html

17. First Contact with Machine Learning

1. What is Machine Learning.mp4

03:52

2. Machine Learning Terminology.mp4

02:31

3. Machine Learning Project Files.html

4. FAQ regarding Python.html

5. FAQ regarding Machine Learning.html

18. 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

19. Supervised Learning with Machine Learning

1. What is Supervised Learning in Machine Learning.mp4

05:06

2. Quiz.html

20. 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

21. Bias Variance Trade-Off in Machine Learning

1. What is Bias Variance Trade-Off.mp4

10:47

2. Quiz.html

22. 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

23. 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

24. 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

25. Hyperparameter Optimization

1. Hyperparameter Optimization Theory.mp4

06:24

2. Hyperparameter Optimization with Python.mp4

09:56

26. 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

27. 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

28. 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

29. Unsupervised Learning with Machine Learning

1. Unsupervised Learning Overview.mp4

03:30

2. Quiz.html

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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

36. Dataset Section on Kaggle

1. Datasets on Kaggle.mp4

15:59

2. Quiz.html

37. 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

38. Discussion Section on Kaggle

1. What is Discussion on Kaggle.mp4

05:39

2. Quiz.html

39. 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

40. 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

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

1. First Step to the 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

42. First Organization

1. Required Python Libraries.mp4

08:40

2. Loading the Dataset.mp4

01:47

3. Initial analysis on the dataset.mp4

12:21

4. Quiz.html

43. Preparation For Exploratory Data Analysis (EDA)

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

44. 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

45. 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

46. 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

47. 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

48. Conclusion

1. Project Conclusion and Sharing.mp4

03:31

2. Quiz.html

49. Extra

1. Machine Learning And Data Science with Kaggle, Pandas , Numpy.html

Description

Machine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examples

What You'll Learn?

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?
Machine Learning Terminology
Evaluation Metrics
What are Classification vs Regression?
Evaluating Performance-Classification Error Metrics
Evaluating Performance-Regression Error Metrics
Supervised Learning
Cross Validation and Bias Variance Trade-Off
Use matplotlib and seaborn for data visualizations
Machine Learning with SciKit Learn
Linear Regression Algorithm
Logistic Regresion Algorithm
K Nearest Neighbors Algorithm
Decision Trees And Random Forest Algorithm
Support Vector Machine Algorithm
Unsupervised Learning
K Means Clustering Algorithm
Hierarchical Clustering Algorithm
Principal Component Analysis (PCA)
Recommender System Algorithm
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 widely used, general-purpose programming language, but it has some limitations. Because Python is an interpreted, dynamically typed language
Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks.
Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website.
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
Machine learning describes systems that make predictions using a model trained on real-world data.
Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing.
It's possible to use machine learning without coding, but building new systems generally requires code.
Python is the most used language in machine learning. Engineers writing machine learning systems often use Jupyter Notebooks and Python together.
Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning.
Machine learning is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving.
Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine"
A machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science.
Python machine learning, complete machine learning, machine learning a-z

Who is this for?

Anyone who wants to start learning "Machine Learning"

Anyone who needs a complete guide on how to start and continue their career with machine learning

Students Interested in Beginning Data Science Applications in Python Environment

People Wanting to Specialize in Anaconda Python Environment for Data Science and Scientific Computing

Students Wanting to Learn the Application of Supervised Learning (Classification) on Real Data Using Python

Anyone eager to learn python for data science and machine learning bootcamp with no coding background

Anyone who plans a career in data scientist,

Software developer whom want to learn python,

Anyone interested in machine learning a-z

People who want to become data scientist

Poeple who want tp learn complete machine learning

What You Need to Know?

Basic knowledge of Python Programming Language

Be Able To Operate & Install Software On A Computer

Free software and tools used during the machine learning a-z course

Determination to learn machine learning and patience.

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

Data visualization libraries in python such as seaborn, matplotlib

Curiosity for machine learning python

Desire to learn Python

Desire to learn matplotlib

Desire to learn pandas and numpy

Desire to learn machine learning a-z, complete machine learning

Any device you can watch the course, such as a mobile phone, computer or tablet.

Watching the lecture videos completely, to the end and in order.

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

LIFETIME ACCESS, course updates, new content, anytime, anywhere, on any device.

More details

Description
Hello there,
Welcome to the " Machine Learning & Data Science with Python, Kaggle & Pandas " Course
Machine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examples

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.
Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.
Itâ€™s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazonâ€™s Alexa and the iPhoneâ€™s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data science
Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. Pandas is built on top of another package named Numpy, which provides support for multi-dimensional arrays.
Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. data analysis, pandas, numpy, numpy stack, numpy python, python data analysis, python, Python numpy, data visualization, pandas python, python pandas, python for data analysis, python data, data visualization.

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.
Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels.
Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover, Numpy forms the foundation of the Machine Learning stack.
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 know data science needs will create 11.5 million job openings by 2026?
Do you know the average salary is $100.000 for data science careers!

Data Science Careers Are Shaping The Future
Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.
If you want to learn one of the employerâ€™s most request skills?
If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?
If you are an experienced developer and looking for a landing in Data Science!
In all cases, you are at the right place!
We've designed for you â€œMachine Learning & Data Science with Python & Kaggle | A-Zâ€ a straightforward course for Python Programming Language and Machine Learning.
In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples.
Also you will get to know the Kaggle platform step by step with hearth attack prediction kaggle project.
Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community-published data & code.
Kaggle is a platform where data scientists can compete in machine learning challenges. These challenges can be anything from predicting housing prices to detecting cancer cells. Kaggle has a massive community of data scientists who are always willing to help others with their data science problems.
You will learn the Numpy and Pandas Python Programming Language libraries step by step.
Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms.
This Machine Learning course 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).

What is machine learning?
Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.

Why we use a Python programming language in Machine learning?
Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, 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.

What is machine learning used for?
Machine learning a-z is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.

Does Machine learning require coding?
It's possible to use machine learning data science without coding, but building new systems generally requires code. For example, Amazonâ€™s Rekognition service allows you to upload an image via a web browser, which then identifies objects in the image. This uses a pre-trained model, with no coding required. However, developing machine learning systems involves writing some Python code to train, tune, and deploy your models. It's hard to avoid writing code to pre-process the data feeding into your model. Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine. They also perform â€œfeature engineeringâ€ to find what data to use and how to prepare it for use in a machine learning model. Tools like AutoML and SageMaker automate the tuning of models. Often only a few lines of code can train a model and make predictions from it

What is the best language for machine learning?
Python is the most used language in machine learning using python. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It's useful to have a development environment such as Python so that you don't need to compile and package code before running it each time. Python is not the only language choice for machine learning. Tensorflow is a popular framework for developing neural networks and offers a C++ API. There is a complete machine learning framework for C# called ML. NET. Scala or Java are sometimes used with Apache Spark to build machine learning systems that ingest massive data sets.

What is a Kaggle?
Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners.
How does Kaggle work?
Every competition on Kaggle has a dataset associated with it and a goal you must reach (i.e., predict housing prices or detect cancer cells). You can access the data as often as possible and build your prediction model. Still, once you submit your solution, you cannot use it to make future submissions.
This ensures that everyone is starting from the same point when competing against one another, so there are no advantages given to those with more computational power than others trying to solve the problem.
Competitions are separated into different categories depending on their complexity level, how long they take, whether or not prize money is involved, etc., so users with varying experience levels can compete against each other in the same arena.
What is a Pandas in Python?
Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays.
What is Pandas used for?
Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel.
What is difference between NumPy and pandas?
NumPy library provides objects for multi-dimensional arrays, whereas Pandas is capable of offering an in-memory 2d table object called DataFrame. NumPy consumes less memory as compared to Pandas. Indexing of the Series objects is quite slow as compared to NumPy arrays.

What are the different types of machine learning?
Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning, we train machine learning models on labeled data. For example, an algorithm meant to detect spam might ingest thousands of email addresses labeled 'spam' or 'not spam.' That trained model could then identify new spam emails even from data it's never seen. In unsupervised learning, a machine learning model looks for patterns in unstructured data. One type of unsupervised learning is clustering. In this example, a model could identify similar movies by studying their scripts or cast, then group the movies together into genres. This unsupervised model was not trained to know which genre a movie belongs to. Rather, it learned the genres by studying the attributes of the movies themselves. There are many techniques available within.

Is Machine learning a good career?
Machine learning python is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving, you can apply machine learning to a variety of industries, from shipping and fulfillment to medical sciences. Machine learning engineers work to create artificial intelligence that can better identify patterns and solve problems. The machine learning discipline frequently deals with cutting-edge, disruptive technologies. However, because it has become a popular career choice, it can also be competitive. Aspiring machine learning engineers can differentiate themselves from the competition through certifications, boot camps, code repository submissions, and hands-on experience.

What is the difference between machine learning and artifical intelligence?
Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine" that can derive information and make decisions, machine learning describes a method by which it can do so. Through machine learning, applications can derive knowledge without the user explicitly giving out the information. This is one of the first and early steps toward "true artificial intelligence" and is extremely useful for numerous practical applications. In machine learning applications, an AI is fed sets of information. It learns from these sets of information about what to expect and what to predict. But it still has limitations. A machine learning engineer must ensure that the AI is fed the right information and can use its logic to analyze that information correctly.

What skills should a machine learning engineer know?
A python machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science, and artificial intelligence theory. Machine learning engineers must be able to dig deep into complex applications and their programming. As with other disciplines, there are entry-level machine learning engineers and machine learning engineers with high-level expertise. Python and R are two of the most popular languages within the machine learning field.
What is data science?
We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods.

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 1000 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.
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Machine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examples
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Who this course is for:
Anyone who wants to start learning "Machine Learning"
Anyone who needs a complete guide on how to start and continue their career with machine learning
Students Interested in Beginning Data Science Applications in Python Environment
People Wanting to Specialize in Anaconda Python Environment for Data Science and Scientific Computing
Students Wanting to Learn the Application of Supervised Learning (Classification) on Real Data Using Python
Anyone eager to learn python for data science and machine learning bootcamp with no coding background
Anyone who plans a career in data scientist,
Software developer whom want to learn python,
Anyone interested in machine learning a-z
People who want to become data scientist
Poeple who want tp learn complete machine learning

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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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I am a professional data scientist and IT ınstructor who's been developing data science and working with startups since 2020. I also have a broad set of skills in software information technology.On the other hand, I am a Dangerous Goods Safety Advisor.Teaching over 170,000 students on Udemy alone, I have helped tens of thousands of people learn Data Science. From zero to hero and novice to expert, I am considered a top teacher by thousands. With so much experience, why not give my experience and knowledge to others so they can fulfill their dreams?The passion for learning and sharing his knowledge by teaching and helping others drives him. It's a passion he's had since he was born. My ability to turn complex programming concepts into easy-to-understand bits of knowledge has been called my "superpower".Teaching isn't an option in my life but a moral obligation to pass on knowledge to others.

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