Machine Learning will be embedded everywhere soon. 

The size of data available to the world is growing exponentially. No matter what industry or field you are working on, the ability to analyze data is becoming more valuable.

STEPHACKING is designed for people who want to learn about data science, but don’t know where to start.

You can find tutorials and demo on various data science topics here.

All topics are broken into easy steps.

We want you to be able to start your data projects even if you have no mathematical or programming background.

 


In STEPHACKING, we will keep everything simple and quick.

You will find examples on data analysis using Python or R or VBA. We will break down every step for you so that you can follow easily. Moreover, this site is completely free of charge.

 

 


 

 

Our goal is to turn topics related to artificial intelligent and machine learning into bite-sized step by step format.

 

 

Even if you have ZERO science background, you can also enjoy the fun of machine learning.

 


 

 

Hands-on Machine Learning Projects

Python and R are popular for works related to data manipulation. We will be building projects together step by step; using some of the most popular libraries, such as, Tensorflow, Theano, Pytorch, Keras, Scikit-learn, numpy, matplotlib, pandas.

 

 

Learn by Visualizing

Many people learn by visualizing the steps. We will break down the most popular Machine Learning codes for you steps by steps. Just follow the screenshots, and you can enjoy the fun of machine learning.

Libraries for Artificial Intelligent & Deep Learning
Libraries for Artificial Intelligent & Deep Learning

 


Machine Learning Topics

 

Linear Regression

Multiple Linear Regression

Polynomial Regression

Support Vector Regression (SVR)

Decision Tree Regression

Random Forest Regression

Logistic Regression

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

K-fold Cross Validation

Grid Search

 

K-Nearest Neighbors (K-NN)

Support Vector Machine (SVM)

Kernal SVM

Naive Bayes

Decision Tree Classification

Random Forest Classification

K-Mean Clustering

Hierarchical Clustering

Apriori

Eclat

 

Upper Confidence Bound (UCB)

Thompson Sampling

Natural Language Processing

Artificial Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Self-Organizing Maps

Boltzmann Machines

Autoencoders

XGBoost