ihab000 / Machine-learning

machine learning projects from scratch and with sklearn

Machine Learning Projects

Several Machine Learning projects describing :

• Steps of machine learning procedure.
• tools to help you work better and faster.
• algorithms from scratch to gain the deep understanding of how Machine learning works and how it affects on data.

Machine learning from scratch (python) :

We all know getting into Machine Learning it is all about the mathematics , statistics and probabilities behind every model and visualizing the important insights. In this file you will know :

• How to implement (Linear Regression , Normal equation , Logistic Regression , K-Means clustering , Associaton rule - Apriori , simple reinforcement learning techniques ) from scratch using only numpy and pandas.
• Important visualizations to gain better understanding about how Machine Learning works in depth.
• algorithms from scratch to gain the deep understanding of how Machine learning works and how it affects the data.

parts of this file:

Supervised Learning:

• Linear Regression

• Normal Equation

• Logistic Regression

Unsupervised Learning:

• Associaton rule - Apriori

• Clustering

simple re-inforcement-learning:

• Upper Confidence Bound

• Thompson sampling

Machine learning With Libraries (Python) :

after knowing the basics and methods to deal with machine learning problems , tools and frameworks will make things easier with sklearn. In this file you will know :

• tools to help you work better and faster.
• more advanced visualizations.
• advanced tools that takes you the extra mile.

parts of this file:

Supervised Learning:

• KNN Regressor

• KNN Classification

• Decision Tree Regressor

• Random Forest Regressor

• Gradient Boosted Decision Tree Classifier

• Random Forest Classifier

• Decision Trees and Random Forests_Classification (compare)

• Dummy Regressor

• Dummy classifier

• SVR

• SVC

• Lasso Regression

• Ridge Regression

• Polonomial Regression

• Logistic Regression

• Naïve Bayes

• Simple Neural Networks

Unsupervised Learning:

• K Means Clustering

• PCA

What Tools you will need:

pandas , numpy , sklearn , matplotlib , seaborn , apyori