Ready to use 1000+ AI/ML Code Templates for Professional Data Scientists
I am graduating in computer science. I am not getting a job offer. I want to be a Data Scientist within a month. I am not willing to spend any money on taking up those expensive courses. Can you help me?
This is the view expressed by thousands of graduating students. They lack a clear direction in acquiring the skills to become a data scientist. Data Science is a very hot field that provides lucrative salaries and notably the ability to work from home. So, the above expectations are quite acceptable. Now, somebody needs to guide them to become a Data Scientist, that too on a fast track. With over decades of academic and industry experience, I will provide a quick solution that should help all such aspirants to achieve these goals.
First, let us try to understand the need for a data scientist as perceived by a businessperson and then you will understand what is Data Science in today’s world.
Need for a Data Scientist
The chief ambition for a businessperson is how can I grow my business? This is what they say:
In the past, I had thousands and millions of customers. I have collected an enormous amount of data on their purchases. Can somebody help me in understanding their purchase habits, preferences, buying patterns, which would provide me with advanced information on what they would purchase next? I will then manage my product inventories and plan the advertisements.
This is where a data scientist plays the role. Now, let us try to understand how a data scientist aids the businessperson to meet their requirements.
Now, let us first try to understand what are the job requirements of a Data Scientist?
The job of a Data Scientist
A data scientist develops a machine learning model that he trains on the historic data and then uses it to do future predictions. Sounds simple, isn’t it? Yes, it is that simple. In the past, many have made this look like a highly complicated task requiring tons of skills. Tell me; to drive a car, do you need to learn automobile engineering? The same thing goes for Data Science. You only need to understand how a machine learning model is developed.
How a Machine Learning Model is Developed?
The entire machine learning model development process is trivial. These are the standard steps of model development.
- Data Cleansing
- Data Preprocessing
- Creating Training/Validation Datasets
- Algorithm Selection
- Model Training
- Model Testing
- Inference on Unseen Data
The above steps remain the same irrespective of the problem that you are trying to solve — regression, classification, clustering. The steps are also independent of the type of learning — supervised v/s unsupervised. Well, I am talking about the classical ML, which has proven its merits in the last several years across a very large number of case studies. There are other advances like Neural Networks, DNN, pre-trained models, and, of course, to mention it, Reinforcement Learning. I will deal with these some other time. Let us currently focus on classical ML, where there are plenty of job opportunities.
If you look at these steps, you can easily gather that the most alarming step would be algorithm selection. The rest of the steps look like standard procedures and indeed they are. The code written for these steps remains the same across all your models. What if somebody gives me a template for model development? Indeed, somebody has already put in enormous amounts of resources in creating templates for hundreds of ML algorithms that are out in the market, proven and developed by top-notch data scientists. We are just going to reuse their work. The company that provides these templates is BlobCity (I am still Googling for other similar sites.)
A Quick Look at BlobCity AI Cloud
The BlobCity AI Cloud is an open-source project that provides 1000+ ready-to-use code templates for machine learning algorithms nicely categorized in groups. These are open-source and free for learning and commercial use.
They classified all these algorithms into various groups. Say you want to learn the working of different classification algorithms, select the Classification group. You will find a bunch of algorithms like SVM, Decision Tree, kNN, Naive Bayes, and several more. Not only this, you will find several variations of each. To use the template, just plug in your dataset, select the features and the target, and execute all commands in the notebook. It will train the model on your dataset and provide you with the evaluation results. It is that simple.
You can tinkle with the data preprocessing and model parameters to see how they affect the model’s prediction accuracy. You can repeat this process for the other algorithms of your choice and study how they perform on your dataset vis-a-vis other algorithms. You will also find several Cloudbooks, essentially a Juypter notebook contributed by the AI community on their site.
BlobCity has marked several groups such as classification, regression, clustering, EDA, dimensionality reduction, time series analysis, natural language processing, and audio-visuals. The last one that is audio-visuals facilitate you to develop machine learning models on image, video, and audio datasets. I would dare to say that the work is quite exhaustive and they have tried to cover most of the machine learning algorithms out on this planet and also simultaneously considering all data types — binary, text, image, audio, and video.
In my opinion, BlobCity has made it very easy for learners to study hundreds of algorithms in one place by providing curated, technically reviewed code templates. Professional data scientists, who know by their experience which algorithm to use, can simply apply the desired template on their datasets.
If you are interested, they take open source contributions on Github.
I have not come across any other site providing this kind of ready-to-use 1000+ code templates (and still growing as per my observations — some days ago it was 600) for machine learning algorithms. I will be happy to hear if any of you know of any similar sites. Thanks!
To quickly learn data scientist’s skills, focus on the hundreds of algorithms that are out there in the market. Study how they perform on different datasets. Compare their outputs. Study the effect of data-preprocessing like scaling, normalization, features selection on their performance.
Once you study the workings of different algorithms, think of using advanced tools like AutoML to discover the best-performing algorithm. Note that you do not have to study the algorithm implementations, the implementations provided in scikit-learn and several other libraries are done by highly skilled developers. Do not imagine that you would do a better job than them. Simply trust their implementations and learn to use them. A company like BlobCity has provided several code templates developed around these implementations to make your job further easy. Good luck with your quick learning of Data Science!