Python for Mastering Machine Learning and Data Science

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Jifry Issadeen

19:37:10

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1. Introduction

1. Welcome Message and Important Instructions.mp4

02:59

2. Download Resources.mp4

01:51

3. Python Installation.mp4

11:15

4. Access Notebook Files with Jupyter Notebook.mp4

06:07

5. Jupyter Notebook Walkthrough Tutorial.mp4

16:42

Exercise Files.zip

2. Python Basics - Starter Kit

1. Getting started with Python.mp4

03:03

2. Variables - Types.mp4

10:37

3. Variables - Usage.mp4

06:46

4. Variables - Strings.mp4

13:05

5. Variables - Integers, Floats and Booleans.mp4

06:35

6. Lists.mp4

15:35

7. Tuples.mp4

11:59

8. Dictionaries and Sets.mp4

16:23

9. If Statements.mp4

18:11

10. for loop.mp4

16:54

11. while loop.mp4

09:53

12. Custom Functions.mp4

12:43

13. List Comprehensions.mp4

10:50

14. Lambda Function.mp4

14:14

15. Built-in Functions.mp4

21:19

16. External Libraries.mp4

06:30

17. Python Exercise Overview.mp4

10:20

18. Python Exercise Solution - Part 1.mp4

20:06

19. Python Exercise Solution - Part 2.mp4

18:50

3. Introduction to Machine Learning

1. Introduction to Machine Learning.mp4

13:48

2. Introduction to Machine Learning.html

3. Machine Learning Life-Cycle.mp4

06:12

4. Machine Learning Life-Cycle.html

5. Introduction to Performance Evaluation - Classification.mp4

05:27

6. Introduction to Performance Evaluation - Classification Metrics.html

7. Confusion Matrix.mp4

08:41

8. Confusion Matrix.html

9. Main Classification Metrics.mp4

07:19

10. Main Classification Metrics.html

11. Performance Evaluation - Regression.mp4

07:18

12. Performance Evaluation - Regression.html

13. Introduction to Sklearn.mp4

02:45

14. One Hot encoding.mp4

05:52

15. Split the Data.mp4

04:15

16. What is Fit.mp4

02:46

4. Linear Regression

1. Linear Regression Theory.mp4

09:53

2. Linear Regression - Theory.html

3. Linear Regression - Salary Prediction - Practical - Part 1.mp4

19:17

4. Linear Regression - Salary Prediction - Practical - Part 2.mp4

18:28

5. Linear Regression - House Price Prediction - Practical - Part 1.mp4

20:06

6. Linear Regression - House Price Prediction - Practical - Part 2.mp4

14:45

7. Linear Regression - Practical.html

5. Logistic Regression

1. Logistic Regression - Theory.mp4

08:35

2. Logistic Regression - Theory.html

3. Logistic Regression - Iris Flower - Practical.mp4

14:09

4. Logistic Regression - Gender Classification - Exercise Overview.mp4

03:31

5. Logistic Regression - Exercise Solution - Gender Classification - Part 1.mp4

15:02

6. Logistic Regression - Exercise Solution - Gender Classification - Part 2.mp4

08:33

6. Lasso and Ridge Regression Regularizations

1. Lasso and Ridge Regression - Theory.mp4

14:45

2. Lasso and Ridge Regression - Theory.html

3. Lasso and Ridge Regression - Melbourne Housing - Practice - Part 1.mp4

28:28

4. Lasso and Ridge Regression - Melbourne Housing - Practice - Part 2.mp4

16:21

5. Lasso and Ridge Regression - Melbourne Housing - Practice - Part 3.mp4

12:17

6. Lasso and Ridge - Insurance - Exercise overview.mp4

04:17

7. Lasso and Ridge - Insurance - Solution to the Exercise.mp4

17:50

7. Dealing with Practical Issues

1. Bias Variance Trade-off.mp4

08:14

2. Bias Variance Trade-off.html

3. Dealing with Imbalanced Data.mp4

16:35

4. Dealing with Imbalanced Data.html

5. Dealing with Missing Values.mp4

16:25

6. Dealing with Missing Values.html

7. Dealing with Outliers - Theory.mp4

21:55

8. Dealing with Outliers - Practical.mp4

11:12

9. Dealing with Outliers.html

10. Feature Scaling of Data - Theory.mp4

10:14

11. Feature Scaling - Practical.mp4

12:26

12. Feature Scaling of Data.html

8. Naive Bayes Classifier (Gaussian)

1. Gaussian Naive Bayes Classifier - Theory.mp4

15:32

2. Gaussian Naive Bayes Classifier.html

3. Gaussian Naive Bayes Classifier - Titanic - Practical - Part 1.mp4

15:44

4. Gaussian Naive Bayes Classifier - Titanic - Practical - Part 2.mp4

16:21

9. Decision Trees

1. Decision Tree - Theory.mp4

12:12

2. Decision Tree - Penguin - Practical.mp4

17:21

3. Decision Tree - Wine Quality - Exercise - Overview.mp4

02:56

4. Decision Tree - Wine Quality - Exercise Solution.mp4

09:12

10. Random Forest

1. Random Forest - Theory.mp4

11:31

2. Random Forest - Theory.html

3. Random Forest - Practical - Bike Sharing - Part 1.mp4

20:44

4. Random Forest - Practical - Bike Sharing - Part 2.mp4

21:42

5. Random Forest - WeatherAUS - Exercise Overview.mp4

02:26

6. Random Forest - weatherAUS - Solution Part 1.mp4

20:02

7. Random Forest - weatherAUS - Solution Part 2.mp4

17:57

8. Extra Tree - Theory.mp4

02:49

11. Boosting Techniques

1. Introduction to Boosting Techniques.mp4

05:23

2. Boosting Techniques Theory - Adaboost.mp4

20:49

3. Boosting Techniques Theory - Gradient Boosting.mp4

20:09

4. Boosting Techniques - Adult - Practical Implementation.mp4

19:05

5. Boosting Techniques.html

12. Support Vector Machines

1. SVM Theory.mp4

13:09

2. SVM - Practical - Heart Disease Classification.mp4

10:42

3. SVM - Water Potability - Exercise Overview.mp4

03:54

4. SVM - Water Potability - Exercise Solution.mp4

22:06

13. K-Nearest Neighbors

1. KNN Theory.mp4

10:04

2. KNN - Practical - Classified Data.mp4

16:52

3. K-Nearest Neighbor.html

14. Unsupervised Machine Learning Algorithms

1. K-Means Clustering Theory.mp4

14:34

2. K-Means Clustering - Practice - Iris.mp4

18:52

3. K-Means Clustering.html

4. DBSCAN Clustering - Theory.mp4

10:26

5. DBSCAN Clustering - Practical.mp4

19:10

6. DBSCAN Clustering.html

15. PCA - Principal Component Analysis

1. Principal Component Analysis - Theory.mp4

06:25

2. PCA - Practical - Airline Passenger - Part 1.mp4

10:33

3. PCA - Practical - Airline Passenger - Part 2.mp4

14:21

4. PCA - Principal Component Analysis.html

16. Natural Language Processing

1. NLP - Natural Language Processing - Introduction - Theory.mp4

20:58

2. NLP - Naive Bayes Multinomial Classification - Theory.mp4

18:16

3. NLP - Practical - Amazon Reviewer Classification - Part 1.mp4

11:50

4. NLP - Practical - Amazon Reviewer Classification - Part 2.mp4

19:20

5. NLP - Practical - Amazon Reviewer Classification - Part 3.mp4

13:15

6. NLP - Natural Language Processing.html

Description

Learn Pandas, Scikit-Learn, Seaborn, Matplotlib, Machine Learning, NLP, Dealing with practical problems and more!

What You'll Learn?

Understand Python programming concepts: Variables, lists, tuples, sets and Dictionaries.
Comfortably deal with Python programming concepts: If statements, loops, custom functions, built-in functions, comprehensions, lambda functions and more..
Comfortably create, evaluate and improve the performance of famous machine learning models with the help of Python
Identify the most suitable machine learning algorithm to practically deal with the problem you are solving.
Be comfortable with the theoretical elements of each machine learning model.
Broad understanding of each machine learning concepts and their practice implementation with Python programming language.
Be comfortable with Exploratory data analysis.
Distinguish the different algorithms and capable of selecting the best.
Parameter tuning and model improvements.
Be comfortable dealing with Outliers, Missing Values, Feature Scaling, Imbalanced data and feature selection.
Understand the idea behind the boosting techniques and how to implement them effectively.
Be a pro who can deal with machine learning algorithms by your own.

Who is this for?

Anyone who is curious about data science.

Anyone who wants to properly understand and learn both theoretical and practice aspects of Machine learning.

Those who expect quizzes and practices to improve their skills while learning machine learning.

If you are someone who expects the real world challenges in the journey of machine learning.

You know machine learning but you prefer to improve both theoretical and practical aspect of it.

What You Need to Know?

We have included a Python training kit for beginners, so, NO programming knowledge is required.

There is NO prerequisite knowledge of Machine Learning or Data Science. Everything will be taught to you from the ground up.

You should have a computer/tablet and time to learn.. That's all.

More details

Description
Welcome to the best Machine Learning and Data Science with Python course in the planet. Are you ready to start your journey to becoming a Data Scientist?

In this comprehensive course, youâ€™ll begin your journey with installation and learning the basics of Python. Once you are ready, the introduction to Machine Learning section will give you an overview of what Machine Learning is all about, covering all the nitty gritty details before landing on your very first algorithm. You'll learn a variety of supervised and unsupervised machine learning algorithms, ranging from linear regression to the famous boosting algorithms. Youâ€™ll also learn text classification using Natural Language processing where youâ€™ll deal with an interesting problem.

Data science has been recognized as one of the best jobs in the world and itâ€™s on fire right now. Not only it has a very good earning potential, but also it facilitates the freedom to work with top companies globally. Data scientists also gets the opportunity to deal with interesting problems, while being invaluable to the organization and enjoy the satisfaction of transforming the way how businesses make decisions. Machine learning and data science is one of the fastest growing and most in demand skills globally and the demand is growing rapidly. Parallel to that, Python is the easiest and most used programming language right now and thatâ€™s the first language choice when it comes to the machine learning. So, there is no better time to learn machine learning using python than today.

I designed this course keeping the beginners and those who with some programming experience in mind. You may be coming from the Finance, Marketing, Engineering, Medical or even a fresher, as long as you have the passion to learn, this course will be your first step to become a Data Scientist.

I have 20 hours of best quality video contents. There are over 90 HD video lectures each ranging from 5 to 20 minutes on average. Iâ€™ve included Quizzes to test your knowledge after each topic to ensure you only leave the chapter after gaining the full knowledge. Not only that, Iâ€™ve given you many exercises to practice what you learn and solution to the exercise videos to compare the results. Iâ€™ve included all the exercise notebooks, solution notebooks, data files and any other information in the resource folder.

Now, I'm gonna answer the most important question. Why should you choose this course over the other courses?

I cover all the important machine learning concepts in this course and beyond.
When it comes to machine learning, learning theory is the key to understanding the concepts well. Weâ€™ve given the equal importance to the theory section which most of the other courses donâ€™t.
Weâ€™ve used the graphical tools and the best possible animations to explain the concepts which we believe to be a key factor that would make you enjoy the course.
Most importantly, Iâ€™ve a dedicated section covering all the practical issues youâ€™d face when solving machine learning problems. This is something that other courses tend to ignore.
Iâ€™ve set the course price to the lowest possible amount so that anyone can afford the course.

Here a just a few of the topics we will be learning:

Install Python and setup the virtual environment
Learn the basics of Python programming including variables, lists, tuples, sets, dictionaries, if statements, for loop, while loop, construct a custom function, Python comprehensions, Python built-in functions, Lambda functions and dealing with external libraries.
Use Python for Data Science and Machine Learning
Learn in-dept theoretical aspects of all the machine learning models
Open the data, perform pre-processing activities, build and evaluate the performance of the machine learning models Implement Machine Learning Algorithms
Learn, Visualization techniques like Matplotlib and Seaborn
Use SciKit-Learn for Machine Learning Tasks
K-Means Clustering
DBSCAN Clustering
K-Nearest Neighbors
Logistic Regression
Linear Regression
Lasso and Ridge - Regularization techniques
Random Forest and Decision Trees and Extra Tree
NaÃ¯ve Bayes Classifier
Support Vector Machines
PCA - Principal Component Analysis
Boosting Techniques - Adaboost, Gradient boost, XGBoost, Catboost and LightGBM
Natural Language Processing
How to deal with the practical problems when dealing with Machine learning
Who this course is for:
Anyone who is curious about data science.
Anyone who wants to properly understand and learn both theoretical and practice aspects of Machine learning.
Those who expect quizzes and practices to improve their skills while learning machine learning.
If you are someone who expects the real world challenges in the journey of machine learning.
You know machine learning but you prefer to improve both theoretical and practical aspect of it.

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I'm a CIMA qualified member (ACMA, CGMA) with over 12 years of accounting data analysis experience, currently sharing the knowledge with the world through Udemy courses. Because of I'm so passionate about the data science and the data analysis, I decided to set foot in to the data science world. Throughout my career, I've developed a skill set in analyzing data and I hope to use my experience in teaching and data science to help other people learn the data science. Currently own a company called Data Kottu and providing high-quality data science training and data related outsourcing services.

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