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.gitattributes
.gitattributes
 
 
01-K Means Clustering with Python.ipynb
01-K Means Clustering with Python.ipynb
 
 
01-SF Salaries Exercise(with answers).ipynb
01-SF Salaries Exercise(with answers).ipynb
 
 
01-SF Salaries Exercise.ipynb
01-SF Salaries Exercise.ipynb
 
 
03-Ecommerce Purchases Exercise .ipynb
03-Ecommerce Purchases Exercise .ipynb
 
 
04-Numpy Exercises.ipynb
04-Numpy Exercises.ipynb
 
 
Assesment2(sret).ipynb
Assesment2(sret).ipynb
 
 
Assesment_1(SRET).ipynb
Assesment_1(SRET).ipynb
 
 
CSV_FileReadingWithPandas.ipynb
CSV_FileReadingWithPandas.ipynb
 
 
CSV_FileWritingWithPandas.ipynb
CSV_FileWritingWithPandas.ipynb
 
 
Classified Data
Classified Data
 
 
College_Data
College_Data
 
 
Contest_1_1_Solution_Sample-1550240301484 (2).ipynb
Contest_1_1_Solution_Sample-1550240301484 (2).ipynb
 
 
DataClensing.ipynb
DataClensing.ipynb
 
 
DataHacker.rs YoloV3 in TF 2.0.ipynb
DataHacker.rs YoloV3 in TF 2.0.ipynb
 
 
Data_Normalaization_and_standardization.ipynb
Data_Normalaization_and_standardization.ipynb
 
 
Decision Trees.ipynb
Decision Trees.ipynb
 
 
Digits recognition(k_means_clustering).ipynb
Digits recognition(k_means_clustering).ipynb
 
 
Ecommerce Purchases
Ecommerce Purchases
 
 
Excel_File Reading & Writing with Pandas.ipynb
Excel_File Reading & Writing with Pandas.ipynb
 
 
K Means Clustering.ipynb
K Means Clustering.ipynb
 
 
K Nearest Neigbhours .ipynb
K Nearest Neigbhours .ipynb
 
 
Linear Regression Training Models.ipynb
Linear Regression Training Models.ipynb
 
 
Logistic Regression Training Models.ipynb
Logistic Regression Training Models.ipynb
 
 
Logistic_Regression from Thiruvengadam.ipynb
Logistic_Regression from Thiruvengadam.ipynb
 
 
Matplotlib_Plotting.ipynb
Matplotlib_Plotting.ipynb
 
 
Mobile_Data.csv
Mobile_Data.csv
 
 
Mobile_Data_To_be_Cleaned.csv
Mobile_Data_To_be_Cleaned.csv
 
 
NumpyArrays.ipynb
NumpyArrays.ipynb
 
 
PandasInbuitGraphs.ipynb
PandasInbuitGraphs.ipynb
 
 
Practicals(Sret) Last day.ipynb
Practicals(Sret) Last day.ipynb
 
 
Prerequisites.ipynb
Prerequisites.ipynb
 
 
README.md
README.md
 
 
SVM_AdDetection.ipynb
SVM_AdDetection.ipynb
 
 
Salaries.csv
Salaries.csv
 
 
Seaborn_Plotting.ipynb
Seaborn_Plotting.ipynb
 
 
Support Vector Machines.ipynb
Support Vector Machines.ipynb
 
 
Weather_new.csv
Weather_new.csv
 
 
df1
df1
 
 
df2
df2
 
 
df3
df3
 
 
diabetes.csv
diabetes.csv
 
 
jester-17m-jokes-ratings-dataset.zip
jester-17m-jokes-ratings-dataset.zip
 
 
mobile_cleaned.csv
mobile_cleaned.csv
 
 
pokemon.csv
pokemon.csv
 
 
submission.csv
submission.csv
 
 
test.csv
test.csv
 
 
titanic.csv
titanic.csv
 
 
train.csv
train.csv
 
 
weather.csv
weather.csv
 
 
winequality.csv
winequality.csv
 
 

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HandsOn-MachineLearning

Getting Started

Programs build on Python lang. Clone the project and run on your local machine for development and testing purpose. See deployment for notes on how to deploy the project on a live system.

Prerequisites

Python compiler 32/64 bit based on your system processesor Recommended IDE:

  • Jupyter Notebook
  • Google Colab

Installing

Prerequisite: Python compiler While Jupyter runs code in many programming languages, Python is a requirement (Python 3.3 or greater, or Python 2.7) for installing the JupyterLab or the classic Jupyter Notebook.

Installing Jupyter Notebook using Conda

  • conda
    We recommend installing Python and Jupyter using the conda package manager. The miniconda distribution includes a minimal Python and conda installation.

Then you can install the notebook with:

conda install -c conda-forge notebook

  • pip
    If you use pip, you can install it with:

pip install notebook
 Congratulations, you have installed Jupyter Notebook! To run the notebook, run the following command at the Terminal (Mac/Linux) or Command Prompt (Windows):

jupyter notebook

Built With

  • Python

Topics Covered

  • Linear Regression
  • Logistic Regression
  • Support Vector Machines
  • K Means Clustering
  • K Nearest neighbours
  • Numpy
  • Seaborn
  • Matplotlib
  • Decision Trees
  • Data Normalization
  • Data Standardization

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Applying ML Algorithms

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