How to start with Machine Learning

Machine Learning is all about making machines capable of learning from the data and implementing intelligence through algorithms to predict the outcome without human interference. Today. Machine Learning is a buzzword that is a part of Artificial Intelligence. Everybody is jumping on the trend of implementing Machine Learning in their tasks to enhance it exponentially. If you wish to make your tasks easier, the machine learning course will help you understand the subject’s nuances and how to navigate through its concepts and implement it on a product used by an end-user. 

How does Machine Learning work?

Basically, Machine Learning is a part of Artificial intelligence, and that enables machines to learn a task from experience without being specifically programmed for it. Initially, the machines are fed with high-quality data, after that these are trained by creating machine learning models using various algorithms and data. The algorithms we choose to train the models are highly influenced by the data we have and the task we’re trying to automate.

Why do businesses use Machine Learning?

The boom of the internet and technological advancements in computing and networking have paved the way for machine learning today. Users across the globe are creating huge amounts of data online every minute which businesses can track, collect and gain insight from using these trained models. And this approach will ultimately help them in personalizing their products more and achieve more conversion rate.  Businesses of all sizes are now using this approach to cut costs, reduce risks, and optimize the quality of their products. This technology is already reshaping the way we interact with technology and in the coming days, it will have a mass effect across industries and will play a significant role in our daily lives. 

Machine Learning Roadmap

Follow the below-outlined roadmap to becoming a machine learning engineer. These are just pointers to help you guide, you can further customize your path once you tread on the journey that best suits you. 

Step 1: Validate your present level

At the starting stage, you need to have good background knowledge in linear algebra, multivariate calculus, statistics, and Python. But, again, you don’t need a Ph.D. in these subjects. Foundational knowledge in these areas is sufficient for you to help you get started. 

  • Learn Linear Algebra and Multivariate Calculus

As discussed above you must know linear algebra and multivariate calculus, but you don’t need to be an expert in these. The degree of depth you require depends on which data science positions you are targeting. 

  • Learn statistics

We now understand how important data is to machine learning. 80% of your time will be spent collating and cleaning data, as an ML expert. Statistics deals with collecting, collating, examining, and presenting data. 

Statistical Significance, Hypothesis Testing, Probability Distributions, Regression, and other crucial statistics concepts are just a few. 

(c) Master Python

Some people tend to learn Linear Algebra, Multivariate Calculus, and Statistics through trial and error. But Python is one of the most fundamental things you must pay close attention to. Many even go on to learn R and Scala. But Python is the most popular language of all in the Data Science realm. It is also used for machine learning. Many Python libraries, including Keras, TensorFlow, Scikit-learn, etc., are particularly helpful for artificial intelligence and machine learning. 

2. Learn ML concepts

As we discussed above, the best way to learn ML would be to start from the basics and then proceed to bigger challenges and that will help you develop good expertise in the subject. Let’s now discuss the basic terminologies of ML first. 

(a) Machine learning terminologies

A model is basically, a kind of representation that we get by training using an ML  algorithm. You can also hear some experts calling them hypotheses. 

A feature in simple terms is a measurable property of data. For instance, color, smell, taste, etc., may be used to predict a food item. These are all called feature vectors. These can describe numerical features collection. For a model, these feature vectors are fed as input for training. 

A label also known as a training variable is the value our model intends to predict. For example, the name of the fruit, such as orange, papaya, watermelon, etc. Now, these are termed labels and we fed the training models with feature vectors discussing this label. 

Training: The goal is to provide a set of inputs (feature vectors) and the expected results (labels) so that, after training, we will have a model (hypothesis) that will map new data to one of the categories used to train the model and eject the outcome which we intend to receive. 

Prediction: When our model is complete, it can be fed a set of inputs and produce a predicted result (label).

(b) Machine Learning Methods

Supervised learning entails using classification and regression models to learn from a training dataset of labeled data. This process of learning continues once the desired level of performance is attained.

Unsupervised learning entails using unlabeled data and factor and cluster analysis models to uncover the data’s underlying structure to learn more about the data itself.

Unsupervised learning is combined with a small amount of labeled data to create semi-supervised learning. Compared to supervised learning, using labeled data greatly improves learning accuracy and is more economical.

Reinforcement learning is discovering the best course of action through trial and error. Learning behaviors based on the current state that will maximize the reward in the future determine the next course of action.


Being an ML Engineer, most of your time is spent collecting and collating data, integrating it, cleaning it, and then preprocessing the data. Because training the model to receive high-quality output requires high-quality data as input. Start by learning various algorithms, practice on huge datasets available online, and then practice on real-world datasets. 

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