Python for Machine Learning: The Complete Beginner's Course
Learn to create machine learning algorithms in Python for students and professionals
4.30 (872 reviews)
105,404
students
2.5 hours
content
Jan 2024
last update
$59.99
regular price
What you will learn
Learn Python programming and Scikit learn applied to machine learning regression
Understand the underlying theory behind simple and multiple linear regression techniques
Learn to solve regression problems (linear regression and logistic regression)
Learn the theory and the practical implementation of logistic regression using sklearn
Learn the mathematics behind decision trees
Learn about the different algorithms for clustering
Why take this course?
To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm!
When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.
Like the Wall Street "quants" of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.
That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.
The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.
In this course, mathematical notations and jargon are minimized, each topic is explained in simple English, making it easier to understand. Once you've gotten your hands on the code, you'll be able to play with it and build on it. The emphasis of this course is on understanding and using these algorithms in the real world, not in a theoretical or academic context.
You'll walk away from each video with a fresh idea that you can put to use right away!
All skill levels are welcome in this course, and even if you have no prior statistical experience, you will be able to succeed!
Content
Introduction to Machine Learning
What is Machine Learning?
Applications of Machine Learning
Machine learning Methods
What is Supervised learning?
What is Unsupervised learning?
Supervised learning vs Unsupervised learning
Course Materials
Simple Linear Regression
Introduction to regression
How Does Linear Regression Work?
Line representation
Implementation in python: Importing libraries & datasets
Implementation in python: Distribution of the data
Implementation in python: Creating a linear regression object
Multiple Linear Regression
Understanding Multiple linear regression
Implementation in python: Exploring the dataset
Implementation in python: Encoding Categorical Data
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Training the model on the Training set
Implementation in python: Predicting the Test Set results
Evaluating the performance of the regression model
Root Mean Squared Error in Python
Classification Algorithms: K-Nearest Neighbors
Introduction to classification
K-Nearest Neighbors algorithm
Example of KNN
K-Nearest Neighbours (KNN) using python
Implementation in python: Importing required libraries
Implementation in python: Importing the dataset
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Feature Scaling
Implementation in python: Importing the KNN classifier
Implementation in python: Results prediction & Confusion matrix
Classification Algorithms: Decision Tree
Introduction to decision trees
What is Entropy?
Exploring the dataset
Decision tree structure
Implementation in python: Importing libraries & datasets
Implementation in python: Encoding Categorical Data
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Results prediction & Accuracy
Classification Algorithms: Logistic regression
Introduction
Implementation steps
Implementation in python: Importing libraries & datasets
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Pre-processing
Implementation in python: Training the model
Implementation in python: Results prediction & Confusion matrix
Logistic Regression vs Linear Regression
Clustering
Introduction to clustering
Use cases
K-Means Clustering Algorithm
Elbow method
Steps of the Elbow method
Implementation in python
Hierarchical clustering
Density-based clustering
Implementation of k-means clustering in python
Importing the dataset
Visualizing the dataset
Defining the classifier
3D Visualization of the clusters
3D Visualization of the predicted values
Number of predicted clusters
Recommender System
Introduction
Collaborative Filtering in Recommender Systems
Content-based Recommender System
Implementation in python: Importing libraries & datasets
Merging datasets into one dataframe
Sorting by title and rating
Histogram showing number of ratings
Frequency distribution
Jointplot of the ratings and number of ratings
Data pre-processing
Sorting the most-rated movies
Grabbing the ratings for two movies
Correlation between the most-rated movies
Sorting the data by correlation
Filtering out movies
Sorting values
Repeating the process for another movie
Quiz Time
Conclusion
Conclusion
Reviews
Tim
September 25, 2023
Ganz okay... gibt einen groben Überblick. Das bereitgestellte Jupyter Notebook enthält zum Teil Fehler und die Erklärung sowie Reihenfolge des Codes ist auch verbesserungswürdig.
Maxim
September 13, 2023
Really good, but not deep enough in some ways.. I wished more graphical result insights into KNN, Decision Tree etc.. And Section 9 was too chopped up, meaning that the content in the videos was too small and it could be summed up in less videos. With every video, I have to watch the title again and so on, leading to a loss of time. But overall, this course was good
Bijan
August 15, 2023
very clear in concept and also pronunciation ;).
I knew a little about data science and machine learning and this tutorial really helps review.
Mustafa
June 16, 2023
Would be much better if more explaining of the code is involved, I have to search for the code and struggle to understand what is written and why
Damien
June 13, 2023
An awkward mix of very high level concepts mixed with low level Python code that is spoken as typed and never really explained. The lessons are far too short which breaks up the flow of listening and learning as you spend too much time in video transitions.
Norbert
June 4, 2023
Overall good Python ML course for beginners, however it is a trimmed version of the other course.
Easy to follow lecture blocks with concept introduction and coding examples.
Boukary
April 19, 2023
I really enjoyed this machine learning algorithms in Python training. The instructions were clear and concise, and the practical examples helped to reinforce my understanding of each concept. The practical projects were very well designed and gave me the confidence to apply this knowledge to real-world projects. I would highly recommend this training to anyone looking to get started in the field of machine learning using Python
Danny
February 10, 2023
It took awhile to understand how to do the coding necessary in Python for the various elements discussed but towards the end of the course the coding became much easier to understand.
Taruchit
October 7, 2022
The course is easy to follow and motivates me to continue learning the next session without taking the break.
Anuj
September 21, 2022
Great till now, starting with basics is what most of the course lack but this one really did a great job. Thank to the teacher :)
Amodu
August 27, 2022
No too beginner friendly! Was enjoying it from the beginning, but got lost when he started the importing
Munkaila
August 10, 2022
The instructions were clear and easy to understand. My knowledge on python has been broadened. Thank you so much
Nayibe
June 27, 2022
Es el mejor curso que he visto hasta el momento, he entendido cosas que no había tenido oportunidad de entender jamás. Lo recomiendo totalmente.
Coupons
| Submit by | Date | Coupon Code | Discount | Emitted/Used | Status |
|---|---|---|---|---|---|
| Angelcrc Seven | 3/21/2022 | SPRING0B760B04AC9B74 | 100% OFF | 1000/986 | expired |
| - | 3/26/2022 | A7AC295434ACD8C03B75 | 100% OFF | 1000/989 | expired |
| - | 4/7/2022 | 6D4F33911D62E6BA3EBB | 100% OFF | 1000/525 | expired |
| Angelcrc Seven | 4/11/2022 | 1CD42A69635CB427D14C | 100% OFF | 1000/973 | expired |
| - | 4/11/2022 | 767B3476A497CCAA1EEC | 100% OFF | 1000/951 | expired |
| Angelcrc Seven | 4/15/2022 | FB1ADF251F18A943F5AC | 100% OFF | 1000/431 | expired |
| - | 4/17/2022 | 08C10D816798ED6EF146 | 100% OFF | 1000/943 | expired |
| - | 4/19/2022 | 654AA669B20D98067480 | 100% OFF | 1000/973 | expired |
| Angelcrc Seven | 4/19/2022 | 4408B01D2ED84783EE4B | 100% OFF | 1000/776 | expired |
| Angelcrc Seven | 4/25/2022 | 74CFE726D52CD3105B3F | 100% OFF | 1000/993 | expired |
| Angelcrc Seven | 4/26/2022 | 55D6F190199B5CF72C62 | 100% OFF | 1000/796 | expired |
| Angelcrc Seven | 4/26/2022 | 21D992C971A5CA16E7A5 | 100% OFF | 1000/921 | expired |
| Angelcrc Seven | 5/4/2022 | 4D767FB5C9431CB1AC98 | 100% OFF | 1000/993 | expired |
| Angelcrc Seven | 5/4/2022 | 08A6FF117DF55908A780 | 100% OFF | 1000/830 | expired |
| - | 5/5/2022 | 78D570C5B8447ECF8DF2 | 100% OFF | 1000/722 | expired |
| - | 5/10/2022 | C1C06F38FB724A4389E2 | 100% OFF | 1000/916 | expired |
| Angelcrc Seven | 5/14/2022 | LEARNWITHMACHINES | 100% OFF | 1000/460 | expired |
| - | 5/17/2022 | A470D85177CE671B9BC1 | 100% OFF | 1000/893 | expired |
| - | 5/23/2022 | 78F44095E97F1832F47F | 100% OFF | 1000/988 | expired |
| - | 5/24/2022 | 0C6B284DD66D742674B0 | 100% OFF | 1000/966 | expired |
| - | 5/25/2022 | ILOVEMACHINES22 | 100% OFF | 1000/984 | expired |
| - | 5/31/2022 | E91476F42DD1BC3EACDF | 100% OFF | 1000/967 | expired |
| - | 5/31/2022 | LEARNINGLIKEMACHINES | 100% OFF | 1000/979 | expired |
| - | 6/1/2022 | C07154673A60C30A2C81 | 100% OFF | 1000/959 | expired |
| - | 6/3/2022 | 4F0577D1E6EF7ED1EE31 | 100% OFF | 1000/966 | expired |
| - | 6/3/2022 | 8C0F9A6E50C491CB5EEC | 100% OFF | 1000/876 | expired |
| - | 6/13/2022 | 3EF8CFCFFF3DB39F749B | 100% OFF | 1000/993 | expired |
| - | 6/16/2022 | 8B36F4847839967CD5A3 | 100% OFF | 1000/869 | expired |
| - | 6/18/2022 | 28BD647C2E56650923E2 | 100% OFF | 1000/908 | expired |
| - | 6/20/2022 | 6593B1401789FF7376CE | 100% OFF | 1000/896 | expired |
| - | 6/30/2022 | A1B4068D6DE3F0B2BCAA | 100% OFF | 1000/996 | expired |
| - | 7/1/2022 | 01062743920FE3512E09 | 100% OFF | 1000/999 | expired |
| - | 7/4/2022 | F978B3A24A6D6172224A | 100% OFF | 1000/771 | expired |
| - | 7/6/2022 | B7F208F0FE1CF6A31545 | 100% OFF | 1000/989 | expired |
| - | 7/9/2022 | 36600929C0835C4A4205 | 100% OFF | 1000/795 | expired |
| Angelcrc Seven | 7/9/2022 | A59A41687567FBAF715C | 100% OFF | 1000/912 | expired |
| - | 7/16/2022 | 65C371CB66192912FD4F | 100% OFF | 1000/908 | expired |
| - | 7/19/2022 | 7F03EE0AA8DE34D1C8B9 | 100% OFF | 1000/621 | expired |
| Angelcrc Seven | 7/20/2022 | 1FF61138E2846CF9971E | 100% OFF | 1000/667 | expired |
| - | 7/22/2022 | 80B6CAD2512995ECCAF8 | 100% OFF | 1000/949 | expired |
| - | 7/25/2022 | 27820CE94FC1946634C6 | 100% OFF | 1000/949 | expired |
| - | 7/25/2022 | 8746A99D520B6654103E | 100% OFF | 1000/594 | expired |
| Angelcrc Seven | 7/31/2022 | B38D7BC53DE352C4B513 | 100% OFF | 1000/941 | expired |
| Angelcrc Seven | 7/31/2022 | 12633A7EDB3CB5343F70 | 100% OFF | 1000/682 | expired |
| - | 8/8/2022 | FBECEC2E06469F65D28F | 100% OFF | 1000/999 | expired |
| Angelcrc Seven | 8/9/2022 | 6AE46ED07966C2497EB0 | 100% OFF | 1000/834 | expired |
| - | 8/22/2022 | 8A6FC41F313B740673C2 | 100% OFF | 1000/894 | expired |
| - | 9/12/2022 | 4F123B77FDD08F532875 | 100% OFF | 1000/814 | expired |
| - | 9/15/2022 | D4BFDAB98BDF6576BB35 | 100% OFF | 1000/929 | expired |
| - | 9/16/2022 | 5D493424B3165CD7672D | 100% OFF | 1000/973 | expired |
| - | 10/3/2022 | C69ADD53F9C27D83D08B | 100% OFF | 1000/985 | expired |
| - | 10/6/2022 | D876D8A47A8BA0EE22C1 | 100% OFF | 1000/993 | expired |
| - | 11/11/2022 | HOT_NOV | 100% OFF | 1000/920 | expired |
| - | 12/15/2022 | 17AFF316F641AEEF9F83 | 100% OFF | 1000/902 | expired |
| - | 12/25/2022 | 1CACE166A3EA49A7D380 | 100% OFF | 1000/984 | expired |
| - | 1/7/2023 | B3A4899C0D36AC4C5F11 | 100% OFF | 1000/705 | expired |
| - | 1/14/2023 | 855AE6159390F195370E | 100% OFF | 1000/958 | expired |
| - | 2/6/2023 | FEB-BESTDEALS | 100% OFF | 1000/992 | expired |
| - | 2/20/2023 | FEBSALES | 100% OFF | 1000/986 | expired |
| - | 3/18/2023 | MARCH-DEALS | 100% OFF | 1000/573 | expired |
| - | 3/21/2023 | MARCH-DEALS23 | 100% OFF | 1000/941 | expired |
| - | 4/14/2023 | 402494FFAF679DA4C6F2 | 100% OFF | 1000/926 | expired |
| Angelcrc Seven | 5/3/2023 | A06575A1E8B4B1DCD101 | 100% OFF | 1000/706 | expired |
| - | 5/19/2023 | 035A85C104E9A3CFDA87 | 100% OFF | 1000/767 | expired |
| - | 5/27/2023 | FA238A226DB0245BCECC | 100% OFF | 1000/942 | expired |
| - | 6/7/2023 | 88B6BE0B6B913CEA4045 | 100% OFF | 1000/918 | expired |
| - | 6/15/2023 | BF576C0036DB17055FD4 | 100% OFF | 1000/905 | expired |
| - | 6/29/2023 | BAC7D9C92FB63990451F | 100% OFF | 1000/837 | expired |
| - | 7/30/2023 | 29244E63D50AAD09940C | 100% OFF | 1000/964 | expired |
| - | 7/30/2023 | 7583D72D165829D11A77 | 100% OFF | 1000/645 | expired |
| - | 8/5/2023 | BACK2SCHOOL | 100% OFF | 1000/999 | expired |
| - | 8/14/2023 | VACAYVIBES80 | 100% OFF | 1000/689 | expired |
| - | 8/28/2023 | AUGUSTKISSES05 | 100% OFF | 1000/533 | expired |
| - | 9/3/2023 | SEPTEMBERWIND30 | 100% OFF | 1000/899 | expired |
| - | 9/11/2023 | WORKFORCE280 | 100% OFF | 1000/899 | expired |
| - | 10/14/2023 | SNUGSTUDY320 | 100% OFF | 1000/970 | expired |
| - | 10/28/2023 | COZYCHIC27V63 | 100% OFF | 1000/704 | expired |
| - | 11/11/2023 | COMFORT64FOOD | 100% OFF | 1000/927 | expired |
| - | 12/11/2023 | SEASON202319 | 100% OFF | 1000/938 | expired |
| - | 12/23/2023 | XMASJ6B8R0 | 100% OFF | 1000/978 | expired |
| Kevin2001 | 1/1/2024 | HAPPY202463 | 100% OFF | 1000/881 | expired |
| Kevin2001 | 1/5/2024 | IMPROVEME4SUCCESS | 100% OFF | 1000/986 | expired |
| - | 1/19/2024 | POLARPOSSIBILITY2024 | 100% OFF | 1000/939 | expired |
| - | 2/2/2024 | SPRINGSIGN24 | 100% OFF | 1000/880 | expired |
| - | 2/18/2024 | WINTERSIGH24 | 100% OFF | 1000/922 | expired |
| - | 3/2/2024 | SPRINGMORNING24 | 100% OFF | 1000/958 | expired |
| - | 3/9/2024 | LEAPSPRING | 100% OFF | 1000/854 | expired |
| - | 3/16/2024 | LEPRELAUGH | 100% OFF | 1000/598 | expired |
| - | 3/24/2024 | SUNBEAMSALE | 100% OFF | 1000/846 | expired |
| - | 4/14/2024 | EASYBREEZE13 | 100% OFF | 1000/993 | expired |
Charts
Price
Rating
Enrollment distribution
4396628
udemy ID
11/14/2021
course created date
3/19/2022
course indexed date
Bot
course submited by