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 4462
- Last Updated June, 2023
Extra Information - Source code, and other stuff
10 months ago
Learning a whole lot. Very nice what we can actually do with a spreadsheet and various information. Some things its hard to understand what is going on for me but as I progress some things are making a little more since. Very great course and I am enjoying it.
1 year ago
Great content & very useful course for beginners!
1 year ago
Overall content is through and self-explanatory. The instructors made sure that they are giving the information in a way that won't make me confused. However, personally I had a tough time with the accent.
2 years ago
Juan Carlos M
Clear ... Concise ... On Point ... Thanks!
2 years ago
Explanation is very clear and understandable. I love learning and this course is helping me to do so.
2 years ago
Atul Singh Kushwah
a great course specially ANN that's really great combination of scratch and an in-built library.
2 years ago
Rajgaurav Govind Sheth
I am 64 year old and having experience in IT. It is good to recollect the mathematics and it's applications in machine learning.
2 years ago
I appreciate his easy instruction.
2 years ago
Salvador Pérez Viramontes
3 years ago
its awsome course get lots of knowlegde.....thanks you learn programming...
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