WOMEN WHO CODE DELHI MENTORSHIP PROGRAM 3.0 — WEEK 4

The week-4 of the mentorship program was majorly focused on learning and to understand our favorite technical topic in depth. During the week, we have discussed about the technology trends around the world and specified the topic ‘Machine Learning’ as it is one of the applications of the Artificial Intelligence (AI) and we knew that this field is increasing rapidly. There is a wide scope of machine learning in the world and it’s one of the best career choices of the 21st century.

In this blog, I have mentioned about the basics of machine learning, steps to build a mini project on a ML Dataset and the opportunities for people in this field.

What is Machine Learning?

It is a branch of Artificial Intelligence (AI) that focused on building applications without any sort of programming. In machine learning, algorithms are ‘trained’ to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data. The application of machine learning in the real world includes Image Recognition, Speech Recognition, Medical Diagnosis, Statistical Arbitrage, Learning Associations.

The steps involved in creating a well-defined ML project are: -

1. Understand and defined the problem- The foremost thing that is required to create a ML project is to understand, the initial data and find out the solution of the particular problem.
2. Analyse and prepare the data- After the collection of the required data, it’s important to access the condition of the data that includes the looking for trends, outliers, exceptions, incorrect, inconsistent, and skewed information. Then, format the data to make it consistent.
3. Apply the algorithms- Then, apply the common machine algorithms such as Linear Regression, Decision tree, Random forest, Naive Bayes, SVM, and Logistic regression.
4. Reduce the errors- Error analysis refers to the process of finding out the mishandling algorithms and to understand which problem requires more time and reduce the errors the make it more compatible.
5. Predict the result- After that we will connect the predictions with inputs to the model in order to get the desired result.

Woohoo! You have created your first project on the ML Dataset.

In order to understand the concept of Machine Learning better, there are some platforms to help you out such as: -

1. Kaggle- It is a no-setup, customisable, Jupyter Notebooks environment by Google. It is similar to the platform provided by the Google Colab in the aspect that both the platforms provide free GPUs along with a large community of published data and code.

Kaggle: Your Machine Learning and Data Science Community

2. Machine Learning (Coursera) — This Machine Learning Course is offered by the Stanford University that is accessible from the platform of Coursera. It is one of the rated and the popular course om ML. According to the survey, around 33% started a new career after the completion of this course and around 32% got a tangible career benefit from this course.

Machine Learning

3. Data Robot- It is a popular end-to-end enterprise AI platform for fast and easy deployment of accurate predictive models.

Enterprise AI | DataRobot

4. Microsoft Azure Automated Machine Learning- It is a no-code tool which can be used to train and tune a machine learning model.

Automated Machine Learning | Microsoft Azure

5. Machine Hack- It is an online platform by Analytics India Magazine for Machine Learning Hackathons where one can test and practice their machine learning skills.

MachineHack

Opportunities for women in machine learning- According to the survey by AIM Research, in association with Great Learning found out that there were greater opportunities for women in AI and Analytics now than five years ago. There are many communities for women developers that provides a base for them in the field of machine learning such as Women in Machine Learning & Data Science, Women Who Code, Anita Borg Institute, Women in Big Data, and Women in Tech summit. They are driving the positive change in the STEM industry.

I hope this blog will help you out to find your passion for Machine Learning. Thank you for reading it.

Looking forward to the final week. Till then, stay tuned and keep learning. 😊

“There is no recipe, there is no one way to do things — there is only your way. And if you can recognize that in yourself and accept and appreciate that in others, you can make magic.” — Ara Katz