Machine Learning Roadmap for beginners
How to start with Machine Learning
If you are a beginner and you are planning to learn machine learning and data science in the future so this article would guide you on how to start with machine learning.
In this article, I would give you roadmap for machine learning if you are planning to learn machine learning.
The first thing we need to do is learn a programming language. There are several programming languages for machine learning, including Python and R.
Python is one of the best and widely used programming languages for machine learning and data science, and due to recent demands, it can also be useful in a wide range of other fields.
Python
Python is easy to learn and it is a powerful programming language
Being a full-fledged programming language, Python is a great tool to implement algorithms for production use. There are various Python packages available for basic data analysis and machine learning.
Reading Data
So, we’ll begin by reading the data first. Data reading is the first stage of our model preparation in the machine learning model. Pandas (a Python package for data reading and analysis) can read and analyse data.
Data Analysis
The next step is data analysis, which comes after reviewing the data. Real-world data is difficult to collect, therefore we occasionally need to make it more effective in order to increase the performance and accuracy of our models and to learn anything important from the data.
Data cleaning
We need to clean the data first to improve the accuracy of our model because gathering data in the real world may be highly challenging and sometimes messy. You can clean up your dataset with the help of NumPy and Pandas.
Data visualisation
The visual representation of information and data is known as data visualisation. utilising visual components such as maps, graphs, and charts. Visualizing data patterns and how our model interacted with the data is necessary. Matplotlib and Seaborn will be used in this instance to visualise the data.
Models in Machine learning
Models in supervised learning
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Logistic Regression
- Decision Tree
- Random Forest
- Support Vector Models
- K-Nearest Neighbours
- Kernel SVM
- Naïve Bayes
Models in Unsupervised learning
You can go on to unsupervised learning after becoming comfortable with supervised learning models.
- K-means clustering
- Neural Networks
- Principle Component Analysis
- Independent Component Analysis
- Apriori algorithm
- Singular value decomposition
- Hierarchal clustering
Here the most used python library will be scikit learn has a wide range of supervised and unsupervised learning algorithms that works on a consistent interface in Python. The library can also be used for data mining and data analysis.
Don’t be worried about how to learn these models. I have written articles on them with source code, which you can read if you are wanted to learn machine learning.
Python Libraries
As you would work in python so there are some python libraries which we would be using for machine learning.
- Pandas — Data analysis
- NumPy —used to process arrays and matrices
- Matplotlib — Data visualisation
- Seaborn — Data visualisation
- scikitlearn — Used to create models
After completing all of the machine learning topics, you now have a basic understanding of machine learning and may participate in competitions and hackathons.
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Conlcusion
Well, I have good news for you I would be bringing some more articles to explain machine learning models and concepts with codes so leave a comment and tell me how excited are you about this.
