Machine Learning Practical: 6 Real-World Applications
What you will learn
You will know how real data science project looks like
You will be able to include these Case Studies in your resume
You will be able better market yourself as a Machine Learning Practitioner
You will feel confident during Data Science interview
You will learn how to chain multiple ML algorithms together to achieve the goal
You will learn most advanced Data Visualization techniques with Seaborn and Matplotlib
You will learn Logistic Regression
You will learn L1 Regularization (Lasso)
You will learn Random Forest Classifier
Section 1: Welcome! Course introduction
Section 2: Introduction to neural networks
Section 3: Setting up the working environment
Section 4: Minimal example - your first machine learning algorithm
Section 5: TensorFlow - An introduction
Section 6: Going deeper: Introduction to deep neural networks
Section 7: Overfitting
Section 8: Initialization
Section 9: Gradient descent and learning rates
Section 10: Preprocessing
Section 11: The MNIST example
Section 12: Business case
Section 13: Appendix: Linear Algebra Fundamentals
Section 14: Conclusion
Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python
- You need to know Python (Machine Learning A-Z level is enough) in order to complete this course.
- You need to know how to set up your working environment (Anaconda, Jupyter Notebook, Spyder)
- This should not be your first Machine Learning course. You need to understand main concepts.
So you know the theory of Machine Learning and know how to create your first algorithms. Now what?
There are tons of courses out there about the underlying theory of Machine Learning which don’t go any deeper – into the applications.
This course is not one of them.
Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges?
Then welcome to “Machine Learning Practical”.
We gathered best industry professionals with tons of completed projects behind.
Each presenter has a unique style, which is determined by his experience, and like in a real world, you will need adjust to it if you want successfully complete this course. We will leave no one behind!
This course will demystify how real Data Science project looks like. Time to move away from these polished examples which are only introducing you to the matter, but not giving any real experience.
If you are still dreaming where to learn Machine Learning through practice, where to take real-life projects for your CV, how to not look like a noob in the recruiter's eyes, then you came to the right place!
This course provides a hands-on approach to real-life challenges and covers exactly what you need to succeed in the real world of Data Science.
There are most exciting case studies including:
● diagnosing diabetes in the early stages
● directing customers to subscription products with app usage analysis
● minimizing churn rate in finance
● predicting customer location with GPS data
● forecasting future currency exchange rates
● classifying fashion
● predicting breast cancer
● and much more!
All helpful and applicable.
And as a final bonus:
In this course we will also cover Deep Learning Techniques and their practical applications.
So as you can see, our goal here is to really build the World’s leading practical machine learning course.
If your goal is to become a Machine Learning expert, you know how valuable these real-life examples really are.
They will determine the difference between Data Scientists who just know the theory and Machine Learning experts who have gotten their hands dirty.
So if you want to get hands-on experience which you can add to your portfolio, then this course is for you.
Enroll now and we’ll see you inside.
Who this course is for:
- Data Science and Machine Learning enthusiasts who want to understand how real data science projects look like.
- Anyone with Machine Learning and Python knowledge who wants to practice their skills