Everything You Need to Know Before Starting Machine Learning with Python Training

Everything You Need to Know Before Starting Machine Learning with Python Training

Machine learning is the technology that enables computers to learn from the data available using statistical methods. It uses algorithms to learn from the data and make predictions based on the learning, much like humans learn from the events that take place around us. From self-driving cars to the spam filters on the email, everything uses machine learning. Knowingly or unknowingly, you may have encountered machine learning at some point. 

Even though there are many different languages that can be used for implementing machine learning, Python is one of the most widely used. Python is a powerful and simple language that also has the support of many libraries that can make it easier to write machine learning algorithms. This is why getting machine learning with Python training has become crucial to establishing yourself in this field. 

However, before plunging yourself into the field, wouldn’t it be good to know what to expect and what not to expect? Here are a few things to keep in mind.

It’s All About the Data 

The basis of any machine learning application is the data. From the training phase to the actual implementation, data plays a crucial role in each step. There are a myriad number of ways by which  data can affect the outcome and the effectiveness of the algorithm. Let us look at a few of these. 

Biased Training Set

If your training set is not really representational of the real-world data and is biased, it can affect the algorithm. The training set should be generated using the same distribution that generated the actual data set. 

Representational Data

Keep in mind that the algorithm is only trained to detect those patterns that were present in the training set. If the training set is not representational and doesn’t contain all the patterns, then your algorithm never really learns to detect them and will fail to do so in the practical data as well.

Clean the Data

The algorithms may be the most exciting part of machine learning, but you will be spending more time cleaning the data than implementing the algorithms. A clean data set is crucial for the proper working of the algorithm. However, cleaning the data is also a time-consuming and tedious job. So be prepared for this.

Not Enough Data

Machine learning is not something that works well with a small data set. Not having enough data can seriously impact your machine learning application. 

Too Many Parameters

The parameters help to fine-tune the data classification and are an important aspect of any ML algorithm. However, it is true here that too much of a good thing can also be bad. The parameters you use should be at scale with the amount of data you have. If you use too many parameters on a small data set, you end up overfitting the data. This will generate a model that will work extremely well for the training set alone and fail when other data sets are supplied. This totally negates the purpose of machine learning. 

Different Types of Machine Learning  

Machine learning is not just one type. There are different ways a machine can learn. These can be broadly classified into four categories – supervised, unsupervised, semi-supervised and reinforcement learning. 

Supervised learning is where you supply a set of inputs and give the machines a set of guidelines or rules to convert these into the desirable outputs. In unsupervised learning, the machine discovers patterns on its own. Semi-supervised is the middle ground between the two. Reinforcement learning is another type of machine learning where you reward the system for learning something. Your machine learning with Python training will delve into the details of each of these.     

Mathematics is Key

When you hear about machine learning, it may sound like it is all about coding. But machine learning is an intersection of many different areas and mathematics is one of those areas. Not only do you need to know statistics, but you also need to have a basic understanding of calculus, linear algebra, and optimisation theory as well. Your machine learning with Python training will be all about algorithms and coding and you may miss out fully understanding some key concepts if you do not know the math behind it all. 

Give Yourself the Knowledge Edge

A machine learning course, especially one with Python can do wonders for your career prospects. It can help you bag a prestigious and well-paying job. To get there, you need to open your mind to new possibilities, be ready to put in some hard work and spend some time practicing the concepts that you learn. In addition to this, you can also do a MEAN stack web development course that will enable you to develop web applications. Machine learning and MEAN stack web development feature among the most wanted skills in the job market right now. With both these achievements under your belt, there will be no stopping your career growth.