# After Learning Python, How do I approach Machine Learning?

Having strong roots in statistics, Machine Learning has become one of the most exciting and fast-paced computer science fields. Several industries and applications use machine learning for better intelligence and efficiency.

Chatbots, ad serving, fraud detection, spam filtering, and search engines are some of the most common examples that apply machine learning every day. With the **Machine Learning course**, you can find patterns and create mathematical models for several things, which is impossible for humans.

However, many people can still not understand the concept of machine learning and how to learn machine learning. We, therefore, created a **Machine Learning guide** to help you with the basics of machine learning. We will also discuss how you can become an expert in machine learning.

# Understanding Machine Learning

Machine learning is a field of study where you need to apply the principles of statistics and computer science to create statistical models for future predictions and identify patterns in data. Machine learning is a type of **IoT training**, which allows software applications to become more accurate in predicting outcomes without explicit programming.

# How can I Start Machine Learning after Python?

The **Machine Learning with IoT guide** is a rough roadmap that you can follow while you follow in the footsteps of becoming a talented Machine Learning Engineer. However, you can always modify the steps according to your requirements to achieve your desired goal.

# Understanding the Prerequisites

If you are a genius, you can start your **Machine Learning course** directly. However, there are some prerequisites that you require to know. These include Linear Algebra, Statistics, Multivariate Calculus, and Python. If you are not proficient in these topics, it will never hinder you from taking the course. However, you need to have a basic understanding of these topics.

**Learning Multivariate Calculus and Algebra**

You need to learn both topics, as they are essential for Machine Learning (ML). However, the extent to which you need these topics depends on your role as a data scientist. If you want to focus more on the applications of heavy machine learning, you need not have to focus more on math. On the other hand, if you desire to focus on research and development, mastery of linear algebra and multivariate calculus is essential, as you require implementing most ML algorithms from scratch.

**Learning Statistics**

Data is the most crucial part of ML. You are likely to spend over 80% of your time as an ML expert cleaning and collecting data. Statistics is the field that deals with the analysis, collection, and presentation of data.

Hypothesis testing, statistical significance, probability distributions, and regressions are some of the most common and essential topics that you as an ML expert need to implement in your career.

Since you have already learned Python, you will not require learning the programming language again at this stage.

# Data Exploration, Cleaning, and Preparation

If you want to be a proficient ML expert, you need to devote more time to data cleaning. The **IoT for beginners** teaches you data cleaning techniques on original data. The more time you devote here, the better you perform. Though it takes most of your time, it helps you to put a structure around it.

For practicing, you can take the titanic survival problem from Kaggle, build a set of hypotheses and then clean the data. You can also add some new features to the existing dataset. Similarly, consider the bike-sharing demand, forecast the problem, and repeat the same cycle that you did before.

# Learning the Various Machine Learning Concepts

After you have completed learning the prerequisites, you can start learning Machine Learning, which is undoubtedly the most exciting part. However, you need to start learning from the basics and then move on to more complicated stuff. Some of the basic concepts in Machine Learning include understanding the terms, various types of Machine Learning, and practicing Machine Learning.

# Participate in Competition

After understanding the basics of Machine Learning, you can head to the craziest part. Yes, we are talking about the competitions. Participating in the competitions will help you become more proficient in Machine Learning as you combine theoretical and practical knowledge. *Titanic: Machine Learning from Disaster* and *Digit Recognizer* are the two most common competitions in Kaggle.

The competitions help a learner to build confidence in Machine Learning. The knowledge competitions have less difficulty compared to the prize-winning challenges. Furthermore, you can find various related resources and start your journey with data science.

After participating in the competitions and other simple challenges, you are ready to start your career in Machine Learning. You can continue enhancing your skills by working more on the challenges and building more creative and challenging Machine Learning projects.

Furthermore, you can opt for advanced courses in Machine Learning, including deep learning and ensemble modeling. After completing your advanced-level course, you can participate in the mainstream Kaggle competition.

We hope that you enjoyed the path to becoming a Machine Learning expert. Taking up a Machine Learning course after completing Python is easier. You will need to learn the programming language and other prerequisites to become the best Machine Learning expert.

**The IoT Academy** is the one stop platform for the machine learning enthusiasts for knacking expertise over the machine learning domain. With dedicated mentors and industry experts the journey becomes productive and fruitful