Humans learn from past experience, so why not machine learn as well?

Hello there,

• If the word 'Machine Learning' baffles your mind and you want to master it, then this Machine Learning course is for you.

• If you want to start your career in Machine Learning and make money from it, then this Machine Learning course is for you.

• If you want to learn how to manipulate things by learning the Math beforehand and then write a code with python, then this Machine Learning course is for you.

• If you get bored of the word 'this Machine Learning course is for you', then this Machine Learning course is for you.

Well, machine learning is becoming a widely-used word on everybody's tongue, and this is reasonable as data is everywhere, and it needs something to get use of it and unleash its hidden secrets, and since humans' mental skills cannot withstand that amount of data, it comes the need to learn machines to do that for us.

So we introduce to you the complete ML course that you need in order to get your hand on Machine Learning and Data Science, and you'll not have to go to other resources, as this ML course collects most of the knowledge that you'll need in your journey.

We believe that the brain loves to keep the information that it finds funny and applicable, and that's what we're doing here in SkyHub Academy, we give you years of experience from our instructors that have been gathered in just one an interesting dose.

Our course is structured as follows:

1. An intuition of the algorithm and its applications.

2. The mathematics that lies under the hood.

3. Coding with python from scratch.

4. Assignments to get your hand dirty with machine learning.

5. Learn more about different Python Data science libraries like Pandas, NumPy & Matplotlib.

6. Learn more about different Python Machine learning libraries like SK-Learn & Gym.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the following: 

• Simple Linear Regression

• Multiple Linear Regression

• Polynomial Regression

• Lasso Regression

• Ridge Regression

• Logistic Regression

• K-Nearest Neighbors (K-NN)

• Support Vector Machines (SVM)

• Kernel SVM

• Naive Bayes

• Decision Tree Classification

• Random Forest Classification

• Evaluating Models' Performance

• Hierarchical  Clustering

• K-Means Clustering

• Principle Component Analysis (PCA)

• Pandas  (Python Library for Handling Data)

• Matplotlib (Python Library for Visualizing Data)

Note: this course is continuously updated ! So new algorithms and assignments are added in order to cope with the different problems from the outside world and to give you a huge arsenal of algorithms to deal with. Without any other expenses.

And as a bonus, this course includes Python code templates which you can download and use on your own projects.