Machine Learning with Python from Scratch
Mastering Machine Learning Algorithms including Neural Networks with Numpy, Pandas, Matplotlib, Seaborn and Scikit-Learn
Machine Learning is a hot topic! Python Developers who understand how to work with Machine Learning are in high demand.
But how do you get started?
Maybe you tried to get started with Machine Learning, but couldn’t find decent tutorials online to bring you up to speed, fast.
Maybe the information you found was too basic, and didn’t give you the real-world Machine learning skills using Python that you needed.
Or maybe the information got bogged down in complex math explanations and was too difficult to relate to.
Whatever the reason, you are in the right place if you want to progress your skills in Machine Language using Python.
This course will help you to understand the main machine learning algorithms using Python, and how to apply them in your own projects.
But what exactly is Machine Learning?
It’s a field of computer science that gives computers the ability to “learn” – e.g. continually improve performance on a specific task, with data, without being explicitly programmed.
Why is it important?
Machine learning is often used to solve tasks considered too complex for humans to solve. We create algorithms and apply a bunch of data to that algorithm and let the computer process (execute) the algorithm and search for a model (solution).
Because of the practical applications of machine learning, such as self driving cars (one example) there is huge interest from companies and government in Machine learning, and as a result, there are a a lot of opportunities for Python developers who are skilled in this field.
If you want to increase your career options, then understanding and being able to work with Machine Learning with your own Python programs should be high on your list of priorities.
What will you learn in this course?
For starters, you will learn about the main scientific libraries in Python for data analysis such as Numpy, Pandas, Matplotlib and Seaborn.
You’ll then learn about artificial neural networks and how to work with machine learning models using them.
You obtain a solid background in machine learning and be able to apply that knowledge directly in your own programs.
What are the Main topics included in the course?
Data Analysis with Numpy, Pandas, Matplotlib and Seaborn.
The machine learning schema.
Overfitting and Underfitting
K Fold Cross Validation
Regularization: Lasso, Ridge and ElasticNet
Support Vector Machines for Regression and Classification
Naive Bayes Classifier
Decision Trees and Random Forest
Hyperparameter Optimization: GridSearchCV
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Kernel Principal Component Analysis (KPCA)
Ensemble methods: Bagging
K means clustering analysis
Regression model and evaluation
Linear and Polynomial Regression
SVM, KNN, and Random Forest for Regression
Neural Networks: Constructing our own MLP.
Perceptron and Multilayer Perceptron
And don’t worry if you do not understand some, or all of these terms. By the end of the course you will know what they are and how to use them.
Why enrolling in this course is the best decision you can make.
This course helps you to understand the difficult concepts of Machine learning in a unique way. Rather than just focusing on complex maths explanaitons, simpler explanations with charts, and info displays are included.
Many examples and genuinely useful code snippets are also included to make it even easier to learn and understand.
After completing this course, you will have the necessary skills to apply Machine learning in your own projects.
The sooner you sign up for this course, the sooner you will have the skills and knowledge you need to increase your job or consulting opportunities. Your new job or consulting opportunity awaits!
Why not get started today?
Click the Signup button to sign up for the course!
- Basic knowledge of Python
- Basic knowledge of Linear Algebra
- No previous experience in Machine learning, or any of the various libraries are needed.
What you will learn
- Have an understand of Machine Learning and how to apply it in your own programs
- Understand and be able to use Pythons main scientific libraries for Data analysis - Numpy, Pandas, Matplotlib and Seaborn.
- Understand and be able to use artificial neural networks
- Obtain a solid understand of machine learning in general
- Potential for a new job in the future.
Who should attend
- Students who wish to take their basic Python skills to the next level by mastering Pythons various scientific libraries
- Students who want to understand and apply Machine Learning into their own programs
- Students wanting to empower themselves with machine learning.
Is available on google app?
Is available on ios?
- Lectures 64
- Quizzes 0
- Exercises 0
- Duration 12.5 total hours
- Skill level Beginner Level
- Students 3536
- Last Updated February, 2020
Extra Information - Source code, and other stuff
1 month ago
Mr. Tim always offers high-quality courses. I love the way he teaching deeply, and did learn a lot from the course. It would be an excellent choice for beginners, but we still need more practice and applications to improve the skill. Many thanks to Tim, and hope you will offer more interesting tutorials!
1 month ago
Dr. Hussein Bakri
A lot of good & yet basic material using Python and Jupyter Notebook. The accent of the instructor is a bit difficult to understand.
5 months ago
Well done, and really well explained, despite the fact that the lecturer's English is not his mother language.
7 months ago
pace is good for beginner.
7 months ago
yes it is good but the trubble is in understanding some of the words of instructor
10 months ago
Pedro A Ramirez
Excellent material, very well explain and lot of information.
1 year ago
Great introduction to data science and machine learning. Very good overview of Neural Networks. This course is a good starting point but is brief: lots of links for additional reading provided.
1 year ago
1 year ago
James Barry Towe
To get the most out of the course you need a background or elementary knowledge of Python and Statistics. The course does a great job of providing templates and springboards for learning. Each Algorithm is neatly organized in Jupyter Notebooks and all the code follows a similar pattern. Be attentive to the lectures as some of the Notebooks are a little out of sync with the lectures, if you follow the lectures there is no problem. I would have preferred more examples and exercises. Several points in the course some activities are left as 'homework' but not covered. To get the most out of the course you need to take the supplied references and follow through on the home work on your own. That said the instructor is very responsive. With more exercises and a refresh of the notebooks in the source files this would be a 4.5 star course.
1 year ago
Juan Abel Landeo Jacinto
It was a good match for my programming skills. I have reviewed and reinforced what i have learnt so far. Update: there it goes the 4 star rating.