Aaron Gallant
Aaron Gallant
Data Science Curriculum Lead
Unlike relative databases, you don't need to be a high-level expert to start exploring MongoDB. Since it’s a NoSQL database, you don't have to know SQL. You can work with MongoDB using JavaScript or any other major programming languages.
Chukwuemeka Okoli
ML engineer at Ledios
Former Petroleum engineer
LinkedIn
Unlike relative databases, you don't need to be a high-level expert to start exploring MongoDB. Since it’s a NoSQL database, you don't have to know SQL. You can work with MongoDB using JavaScript or any other major programming languages.
Chukwuemeka Okoli
ML engineer at Ledios
Former Petroleum engineer
LinkedIn
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From optimizing an advertising budget to predicting the cost of a house ― a machine learning model can do it all, quickly and accurately! And creating one is easier than you think. (Hint: No math or tech expertise required!) All you need to get started is an easy-to-understand toolkit. Luckily, one already exists — Scikit-learn. But what makes it such a great choice for machine learning newbies? Let's find out!

A few words about machine learning 

Before we move to a toolkit for machine learning, let’s cover some basics about machine learning itself. 

Machine learning is the process of teaching computers to learn and make predictions based on patterns in data, rather than being explicitly programmed to perform a specific task. It is used in a wide range of fields, such as healthcare, manufacturing, finance, computer vision, and many more. For example, Netflix uses machine learning algorithms to personalize recommendations based on your viewing history, rating, and other data.

The entire machine-learning process can be broken down into several steps:

1. Start with a dataset of input data and corresponding output labels.

We have some data with corresponding labels or outputs. This data will be used to train a machine-learning model.

2. Split the dataset into training and testing sets.

The model is trained on the training set and then evaluated on the testing set to see how well it performs.

3. Train a machine learning model on the training set using an algorithm.

The algorithm will try to identify patterns or relationships between the input data and output labels.

4. Evaluate the performance of the model on the testing set using metrics.

We use metrics such as accuracy, precision, and recall to measure how well the model predicts the output labels.

5. Adjust the model.

If the model does not perform well on the testing set, we may need to adjust the model and repeat steps three–four until we achieve the desired performance.

6. Use the trained model to make predictions on new, unseen data.

Once the model has been trained and tested on training datasets, it can be used to make predictions on new data for real problem-solving.

7. Collect feedback on the model's predictions.

Finally, we can collect feedback on the model's predictions and use it to improve the model. This can be done by retraining it on additional data, or by adjusting its parameters based on the feedback. 

What is Scikit-learn?

Now back to Scikit-learn.  

When working with Python, you will often use libraries. In programming, a library is a set of pre-written code intended to simplify certain tasks. For example, imagine you need to make lasagna. You can either knead and roll out the dough yourself, or buy pre-made pasta. It's the same in programming: you can write your own functions from scratch or use existing and proven ones. Scikit-learn is just one Python library.

Scikit-learn’s aim is to help you write programs with predictive statistical models. These models are needed to make a prognosis about future behaviors or data. Scikit-learn teaches a computer to recognize patterns in texts or images and finds relationships between different pieces of information. Based on that, Scikit-learn makes predictions about information. For example, a model created with Scikit-learn can identify photos containing a cat out of thousands of other images and do so in seconds. 

Scikit-learn provides a fast, convenient way to work with large datasets and perform complex mathematical operations. As a result, we can get quite accurate classification or prediction models. 

How Scikit-learn helps ML specialists and analysts

Professionals from various fields, such as commerce and high-tech manufacturing, often use Scikit-learn. The tool makes the work of ML specialists and analysts easier with ready-to-use algorithms and functions for data analysis and model building.

Let's say you’re working on an e-commerce website. To increase sales, you need to predict which customers are more likely to buy which product. You have a lot of data about each customer, including age, gender, location, and past purchases. 

With Scikit-learn, you can quickly and easily build a predictive model that considers all this information and generates a probability estimate for each customer, indicating how likely they are to buy that product. You can then use this information to run more effective marketing campaigns and increase sales.

For example, if your site's statistics show that women aged 25–35 from Texas often buy teapots, you can set up your site's banner ads so that new female customers that match these parameters are more likely to see the teapot banners.

Want to see something even cooler? Suppose you want to develop a prosthetic arm that responds to signals from the patient's brain and allows the patient to move their limb the way they might a natural one. To do this, you need to analyze a lot of data from the patient's brain and movements. With Scikit-learn you can!

That is just one example. You can apply the same principle to self-driving cars, automated manufacturing, multi-factor medical diagnostics, and more, from artificial intelligence. Some of these things are already out there, while others are coming soon — all thanks to machine learning.

Other ML libraries

There are many different tasks in machine learning and libraries to solve them. Sometimes they are similar in functionality to Scikit-learn. Let's look at some of them.

TensorFlow

TensorFlow is a tool used for making very complex programs, such as training neural networks with many layers, speech, and image recognition, etc. It's great for working with huge amounts of information, but it takes more time to learn and write code than simpler tools like Scikit-learn.

Pros:

  • Can handle really large amounts of data
  • Lets you make more complicated programs ― neural networks that are cutting-edge predictive models
  • Gives you more control over how the computer learns

Cons:

  • Takes more time to learn and write code
  • Can be slower than Scikit-learn for some tasks
  • Can be harder to understand than Scikit-learn

Keras

Keras is a tool for writing computer programs that can learn and perform tasks on their own, but it's easier to use than TensorFlow. It's great for making simpler programs and trying out new ideas.

Pros:

  • Easy to learn and use
  • Can make lots of different types of programs
  • Functions as an interface for using TensorFlow, which makes working with it much easier

Cons:

  • Not as good as Scikit-learn for the types of tasks that do not require deep learning models, linear regressions, decision trees, or support vector machines
  • Can be slower than Scikit-learn for some tasks
  • Has a smaller community compared to Scikit-learn

PyTorch

PyTorch is another tool for writing computer programs that can learn and perform tasks on their own. It's easy to use and lets you try out new ideas quickly. It's great for writing more complex programs that work with lots of data, such as multi-layer neural networks.

Pros:

  • Easy to use and lets you try out new ideas quickly
  • Can write complicated programs that work with lots of data ― such as neural networks
  • Works well with GPUs to accelerate and run faster

Cons:

  • Can be slower than Scikit-learn for some tasks
  • Not as good as Scikit-learn for some types of tasks

The way to machine learning

Scikit-learn is the simplest tool for machine learning, so it's better to start in this field. And to become a master of machine learning, it’s worth learning a few more libraries, as well as the basics of the Python language. Practicum’s Data Science Bootcamp teaches Python, Scikit-learn, and other libraries as a part of their curriculum. If you’re ready to break into IT, give Practicum a shot!

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