As the term implies, machine learning involves training computers, or any other technology for that matter, to learn anything depending on the dataset you provide. While the concept sounds futuristic and ahead of its time, people have created numerous applications for such technologies, and you can create one for yourself too. For instance, there are various speech recognition technologies or virtual assistants, such as Alexa, that you use. These technologies solve problems on command and answer questions, allowing you to perform various other tasks and activities.

Creating your Own Machine Learning Project

Creating a machine learning project is not a big deal in 2020. However, in the future, we can even create advanced bots that can solve just about any problem you give it. To create a machine learning project, you can follow the below basic steps:

  • Understanding your problem
  • Preparing the Datasets
  • Choosing the Tool for organizing the model
  • Applying the Algorithm
  • Reducing the errors
  • Predicting the result

Below, we will help you understand how to organize machine learning projects python, Git, anaconda, code, and no jupyter. So let’s get started:

Organizing your first Machine Learning Project in Python

Step 1

For starters, download and install Python and SciPy. You need to install the following SciPy libraries:

  • scipy
  • NumPy
  • matplotlib
  • pandas
  • sklearn

Step 2 

After that, you need to use a database tool such as the IRIS flowers dataset. Now, you need to import the libraries. Make sure to avoid any errors. You can load the dataset with the help of the UCI machine learning depositary.

Step 3

You need to check the data. There are various ways to do so:

  • Check the dimensions of the dataset
  • The peak of the dataset
  • Summary of the data set
  • Break out of the dataset into a different class

Step 4

Now, visualize the data by plotting each variable. This gives you a clear idea about the data you are using as input.

Step 5

After visualizing the data and making sure that everything is accurate, you need to calculate the estimation of the unseen data. For that, you need to:

  • Create the validation of the dataset
  • Test harness
  • Create various testing models
  • Select the model that is working better than others.

Step 6

Now we will make predictions out of our algorithm to check the accuracy.

Organizing your first Machine Learning Project in Git

Step 1

You can download the Git from the https://git-scm.com/downloads

Step 2 

Create an account on GitHub

Step 3

Now link your GitHub to your system with the help of an SSH key. This will enable you to insert code in GitHub’s cloud storage.

Step 4

Create a new repository by hitting the new tab on your profile.

Step 5

Now you can access the repository by following the below command:

echo “# PyTorch-computer-vision” >> README.md

gitinitgit add README.md

git commit -m “first commit.”

git branch -M master

git remote add origin https://github.com/rachellea/pytorch-computer-vision.git

git push -u origin master

Organizing your first Machine Learning Project in Anaconda

Step 1

At first, you need to download the Anaconda tool. This is a free and user-friendly tool that you can access easily.

Step 2 

Now, install the software by the following method:

  • Open the download file
  • Follow the instructions of the installation wizard
  • Now wait for almost 10 minutes, and you are done with the installation process

Step 3

You need to update the Anaconda tool before using it to create a new project.

Step 4

Now you need to update the scikit-learn and SciPy libraries for your machine learning project.

Step 5

Next up, it’s time to install Python libraries for your project. You need to install the following libraries:

  • Theanodeep learning libraries
  • TensorFlow deep learning libraries
  • Kerasdeep learning libraries

Organizing your first Machine Learning Project in NO JupyterNoteBooks

Step 1

To begin developing a machine learning project with NO JupyterNoteBook, the first step involves registering for an IBM Cloud account.

Step 2 

Now, you need to search for Watson machine learning in the search tool and click the button at the bottom. The Tool will provide you 20 free capacities that can train, deploy, evaluate, and score your project.

Step 3

You need to create an IBM Cloud API key from the API key management page. Now you should copy and paste the API key into your notebook.

Step 4

Create a development space so you can store and manage the deployed models. Now you will be writing Python code in the notebook.

Step 5

Now you create a model by using the following code in the Watson machine learning. Copy and paste the below code and paste it into the notebook.

imports learn
from sklearn.datasets import load_irisiris = load_iris()
X = iris.data
Y = iris.target
clf = sklearn.neighbors.KNeighborsClassifier()
clf.fit(X, Y)

Step 6

To deploy a model, you need to follow these steps:

  • Install the Python SDK by running

pip install IBM-Watson-machine-learning

  • Import the package and supply of the WML
  • Find the ID of your deployment space
  • Now you will publish the model that you created
  • Now you need to use the ID of the published model and deploy your model

Step 7

After the deployment of your model, you need to check the test if the model is running properly or not.

Conclusion

Data is essential to help your machine learning project learn from training datasets. Choosing accurate data will help you in making a better decision-making process. Machine learning has applications in numerous industries. You can create projects for healthcare, financial services, and other industries. Machine learning projects can help you in understanding the pattern and structure of your data, improving your operational tasks through an informed decision.