This is my journey from the start to end when I learnt Machine Learning from start to end. Each .ipynb file represents my single day or the topic covred in that particular file. By this one can learn and can give a kickstart to the basic understanding of Machine Learnings. The codes offered are easily readable with short notes on the side.
You can clone this into your system.
- Fork this repository.
- Clone this forked repository.
git clone https://github.com//Machine-Learning
- Navigate to the project directory.
cd Machine-Learning
- Create a new branch
git checkout -b
<your-branch-name>
you can name it anything of your wish
- Make changes in it.
- Run it.
- Stage your changes and commit it.
Add changes to index
git add .
Commit to the local repository
git commit -m "<your_commit_message>"
- Push your local commits to your local repository.
git push -u origin <your_branch_name>
Pre-Requisites to run this project on your System are
-Python 3
-Anaconda
-Jupyter Notebook Installed in your system
Jupyter notebook through your terminal is lighter option for your system.
- Python List Vs Numpy Arrays
- Different Ways To Create Array In Numpy
- Handle Multi-Dimensional Array With Numpy
- Introduction To Pandas
- Data Analysis With Pandas
- Series And Data Frames
- Read CSV, Excel And Other Files With Pandas
- Exploratory Data Analysis With Pandas
- Statistical Analysis
- Mean, Median, Mode, Range, Variance, Standard Deviation
- Introduction To Data Visualization
- Plot Graphs With Matplotlib
- Plot Histograms, Bar Plot, Scatter Plot, Box Plot, Pie Charts Etc.
- 2D And 3D Plots With Matplotlib
- Introduction To Seaborn
- Making Graphs More Attractive With Seaborn
