Machine Learning to Deep Learning Tutorial

Using Jupyter notebooks and various datasets we are able to see through the whole machine learning to deep learning landscape. The notebooks are arranged in order.

Prerequisites

Ensure you have installed python first and foremost. You can install python from here.

Virtual Environment

** Optionally you can create a virtual environment for your machine learning projects. If not skip this step and head over to install all the packages here.

python -m venv ml

After creating the virtual environment you'll need to activate the virtual environment with the command below depending on your terminal:

~/ml/Scripts/activate

or

~/ml/Scripts/activate.bat

or

~/ml/Scripts/Activate.ps1

Install Packages

Install all the packages needed:

pip install jupyterlab pandas numpy matplotlib tensorflow apyori xgboost

Notebooks

Here are all the notebooks available here:

Data Preprocessing

• Data Preprocessing

Regression

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
• Logistic Regression

Classification

• K Nearest Neighbour
• SVM
• Kernel SVM
• Naives Bayes
• Decision Tree Classification
• Random Forest Classification

Clustering

• K Means Clustering
• Hierarchical Clustering

Association Rule Learning

• Apriori
• Eclat

Reinforcement Learning

• Upper Confidence Bound
• Thompson Sampling

Natural Language Processing

• Natural Language Processing

Deep Learning

• Artificial Neural Network
• Convolutional Neural Network

Dimensionality Reduction

• Principal Component Analysis
• Linear Disciminant Analysis
• Kernel PCA

Model Selection and Boosting

• Model Selection
  • k-Fold Cross Validation
  • Grid Search
• Boosting
  • XG Boost

Thanks for checking this out, have fun with this code, happy coding 😎