Python Machine Learning

Introduction to Machine Learning With Python

Machine Learning also termed ML. It is a subset of AI (Artificial Intelligence) and aims to grants computers the ability to learn by making use of statistical techniques. It deals with algorithms that can look at data to learn from it and make predictions.

Tasks in Machine Learning Using Python

With Python Machine Learning, we divide the tasks of Machine Learning Algorithms in Python into two broad categories- Supervised and Unsupervised.

a. Supervised Learning
b. Unsupervised Learning

a. Supervised Learning

Here, a learning signal/ feedback is available to the system; we give it to sample data to learn from. The computer holds example inputs and desired outputs with the goal of learning a general rule that maps inputs to outputs. One such example of Python Machine Learning will be to search for images on Facebook using keywords centered around the contents of the image. Under Supervised Learning, we have the following kinds of Python machine Learning-

Semi-Supervised Learning- The computer receives an incomplete training signal. This is a training set with some target outputs missing.

Active Learning- The computer can secure training labels for only some instances. It also needs to make an optimal choice of objects to secure labels.

Reinforcement Learning- In this, the training data comes as feedback on how a program acts in a dynamic environment. Examples of this include driving a vehicle or playing against an opponent.

Steps involved in Supervised Machine Learning-

• Training
• testing

Among many Supervised Machine Learning Algorithms for beginners we observe, here we list some-

• Decision trees
• Support Vector Machines
• Naïve Bayes
• k-nearest neighbor
• Linear regression

b. Unsupervised Learning

In unsupervised learning, the Python Machine Learning Algorithm receives no labels; we only give the machine a set of inputs. It must rely on itself to find structure in its input. This kind of learning can be a goal or a means toward future learning. We can classify unsupervised learning as-

Clustering- The act of grouping data inherently. One example of this will be to group consumers by their shopping habits so they can target the right consumers to advertise.

Association- In association, we identify rules explaining large sets of our data. One example will be to associate books around author/ category.

Of the many Unsupervised Machine Learning Algorithms, we observe, here are a couple-

• K-means clustering
• Hierarchical clustering

Steps in Python Machine Learning 

We follow the following steps in Machine Learning Using Python-

1. Collecting data.
2. Filtering data.
3. Analyzing data.
4. Training algorithms.
5. Testing algorithms.
6. Using algorithms for future predictions.

Applications of Python Machine Learning

Where does machine learning with Python come to use? Let’s learn Applications of Machine Learning with Python:

a. Fighting and filtering webspam and malware

With rule-based spam filtering, latest tricks by spammers can go unnoticed. e-mail clients make use of machine learning to ensure its spam filters stay updated. Other than that, imagine getting to Google and searching for something only to find irrelevant listings right at the top. To fight these situations, Google uses ‘deep learning’, a neural network that takes data from users and from NLP, and determines the nature of the email in question. Some spam-filtering techniques under ML are Multi-Layer Perceptron and C 4.5 Decision Tree Induction.

b. Refining search-engine results

Suppose you went up to Google and typed in the keywords “DIY lampshade”. If you visit one or more of the top listings and stay for a while, Google assumes it did a good job serving your request. If, however, you end up on the third page and have not visited any result, Google knows it could have done better. So, it improves search results next time.

c. Virtual Personal Assistants

With assistants like Siri, Alexa, and Google Now, the term virtual personal assistant needs no explanation. This help finds information for you, make calls, set alarms, and check the weather among all other things they can do. And to make this easy for you, all they need you to do is use your voice and command them to do it for you. When you’ve got your hands filthy, or if you’ve just woken up and do not wish to lay your eyes on the light of a screen, this comes in handy. Not to forget the huge importance of this for those handicapped.

How you involve with them helps them collect and refine that information. This is machine learning and this is how they generate better results next time.

d. Social Media Services

On social media, facilities like ‘People You May Know’ and ‘Face Recognition’ work via machine learning. Considering your activity like the profiles you visit, the people you befriend, the people you tag, Facebook curates a list of suggestions for you to enrich your experience and make you stay.

e. Online customer support

Some websites will pop a live chat option up to make your stay in case you need a query to be answered. For some, it isn’t live but is a chatbot. Such a bot pulls information from the website and delivers it to the customer. The machine learning algorithms make it possible to improve this experience.

f. Product recommendations

Shopping giants like Jabong and Amazon curate a list of products similar to the ones you’re visiting. They also mail you shopping suggestions. This is machine learning behind the scenes; it pays attention to your past purchases, wishlist, cart contents, brand preferences, and so.

g. Online fraud detection

Companies like PayPal use ML to fight against issues like money laundering. They compare millions of transactions to differentiate between those legitimate and illegitimate.

h. Video Surveillance

With ML, video surveillance systems can detect a possible crime ahead of it. Risque behavior like people standing motionless for a while monitoring a situation, napping on a bench, and following another individual can alert human attendants. When this can prevent a mishap and save a life, incidents like these help improve such surveillance services.

i. Automatic Translation

ML makes it possible to translate text from one language to another. The algorithm learns how words fit together and use that to improve the translation. This is also possible to text on images. This is done with neural networks to identify letters in the images. It translates the text and then puts it back onto the picture.