Learning is something that we have to do every day. As a developer, you need to learn the latest and hottest technologies because if you don’t, you might not be able to succeed in this field.
I am a web developer and machine learning engineer. I am working on both and trying to improve my skills with time, but sometimes you need guidance to see where you are right now and where you see yourself in the future.
So for this blog, I am going to give you a perfect road map through which you can learn the basics and then start training your own different models for machine learning.
In order to be good at machine learning or deep learning, you must know programming, how to code, how to solve problems, and what kind of logic you will use to solve a complex problem.
The very first thing you must do is learn a programming language, and for that, I will recommend learning Python.
Learn the basics of the Python programming language, Get comfortable with the Python programming language, try to build your logic, and Learn how to solve a problem; the more you code, the better you will get.
Python is great for everything; you can do anything with the Python programming language: build websites, develop games, train machines to predict anything, or train machines to generate anything, like text and images. There is so much you can do with Python.
Once you’ve got all the basics down for machine learning, you need to learn some basic libraries so that you can get the job done, and for that, I would recommend a couple of libraries:
NumPy is a popular Python library for multi-dimensional array and matrix processing because it can be used to perform a great variety of mathematical operations. Its capability to handle linear algebra, the Fourier transform, and more makes NumPy ideal for machine learning and artificial intelligence (AI) projects, allowing users to manipulate the matrix to easily improve machine learning performance. NumPy is faster and easier to use than most other Python libraries.
With machine learning growing at supersonic speed, many Python developers were creating python libraries for machine learning, especially for scientific and analytical computing. Travis Oliphant, Eric Jones, and Pearu Peterson 2001 decided to merge most of these bits and pieces of codes and standardize them. The resulting library was then named as SciPy library.
The SciPy library offers modules for linear algebra, image optimization, integration interpolation, special functions, Fast Fourier transform, signal and image processing, Ordinary Differential Equation (ODE) solving, and other computational tasks in science and analytics.
The underlying data structure used by SciPy is a multi-dimensional array provided by the NumPy module. SciPy depends on NumPy for the array manipulation subroutines. The SciPy library was built to work with NumPy arrays along with providing user-friendly and efficient numerical functions.
Scikit-learn is a very popular machine-learning library that is built on NumPy and SciPy. It supports most of the classic supervised and unsupervised learning algorithms, and it can also be used for data mining, modeling, and analysis. Scikit-learn’s simple design offers a user-friendly library for those new to machine learning.
Pandas is another Python library that is built on top of NumPy, responsible for preparing high-level data sets for machine learning and training. It relies on two types of data structures, one-dimensional (series) and two-dimensional (Data frame). This allows Pandas to be applicable in a variety of industries including finance, engineering, and statistics. Unlike the slow-moving animals themselves, the Pandas library is quick, compliant, and flexible.
Matplotlib is a Python library focused on data visualization and primarily used for creating beautiful graphs, plots, histograms, and bar charts. It is compatible with plotting data from SciPy, NumPy, and Pandas. If you have experience using other types of graphing tools, Matplotlib might be the most intuitive choice for you.
These libraries are great for reading data and then storing it in multiple arrays, variables, or data frames. Once you have stored your data, you then need to display that data in the form of different graphs. I have provided all of the official documentation. So review them, and they can help you out.
One more thing: read the official documentation; that will help you like crazy.
Here we go. Whenever I mention maths, people say you don’t need maths for machine learning, but believe me, you need to learn maths, and moreover, you need to learn these subjects to be a full-fledged god in machine learning and deep learning.
- Probability and Statistics
- Linear Algebra
- Calculus
- Matrix
Each subject has a purpose. Probability and statistics will assist you in reading various types of data so that you can determine how data works and what the range of each dataset is. Linear algebra and matrices will help you reshape the data according to the model. Once you know how to play and reshape the data, you can do anything with the given data, no matter how large or impure the dataset is. So Learn them they will help you out.
Once you have learned how to code in Python and how math works in machine learning, you need to learn different machine-learning models. There are so many out there, and each model has a purpose. Study them and find out what makes them unique. How each model is working, what are the inputs, what will be the output, what kind of data is required, and how can you reshape the data for that model?
There is a great place where you can find different machine learning models, and that place is called Kaggle.
Kaggle is great for learning about machine learning models; you can find a lot of datasets and even check out different machine learning models.
You can also get a lot of help from Github. GitHub is great for every developer because you can save all of your versions in one place.
Once you know how to read a model and code a model, now you need to create your own model, using different python libraries some of which are stated below:
TensorFlow
TensorFlow’s open-source Python library specializes in what’s called differentiable programming, meaning it can automatically compute a function’s derivatives within a high-level language. Both machine learning and deep learning models are easily developed and evaluated with TensorFlow’s flexible architecture and framework. TensorFlow can be used to visualize machine learning models on both desktop and mobile.
Seaborn
Seaborn is another open-source Python library, one that is based on Matplotlib (which focuses on plotting and data visualization) but features Pandas’ data structures. Seaborn is often used in ML projects because it can generate plots of learning data. Of all the Python libraries, it produces the most aesthetically pleasing graphs and plots, making it an effective choice if you’ll also use it for marketing and data analysis.
Theano
Theano is a Python library that focuses on numerical computation and is specifically made for machine learning. It is able to optimize and evaluate mathematical models and matrix calculations that use multi-dimensional arrays to create ML models. Theano is almost exclusively used by machine learning and deep learning developers or programmers.
Keras
Keras is a Python library that is designed specifically for developing neural networks for ML models. It can run on top of Theano and TensorFlow to train neural networks. Keras is flexible, portable, user-friendly, and easily integrated with multiple functions.
PyTorch
PyTorch is an open-source machine-learning Python library based on the C programming language framework, Torch. It is mainly used in ML applications that involve natural language processing or computer vision. PyTorch is known for being exceptionally fast at executing large, dense data sets and graphs.
You need to learn these libraries; once you have, you can create your model. You will learn how to train a model, how to test a model, and what ratios you can use to create a good machine learning or deep learning model.
Learn how to use git and GitHub; this is a critical skill for any developer. This will help you save your project and show the world that you have done some projects and have the skills to take care of problems.
The second most important thing to learn is how to document your code and your project, because you will forget everything after a while, and without documentation, it will take forever to find the solution to a problem that you have already solved in the past.
Now all you need are some project ideas so that you can practice. So here are some of the project ideas that I have used in the past.
- Use machine learning to forecast your own earnings.
- Forecast your spending for the next few months.
- Do some machine learning challenges on Kaggle.
- Do image processing and image restoration using deep learning and machine learning.
The current market is thought and without skills, you can’t survive so learn coming technologies and be great. Learn what you like. Don’t listen to others.
Here is the roadmap that I used to learn machine learning, and I hope you will like the blog. I will see you next time.
Name: Abdul Rafay
Email: 99marafay@gmail.com
website: https://rafay99.info/
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Please feel free to contact me if you have any questions.
