Machine Learning is a branch of artificial intelligence (AI) that allows computers to learn automatically, from provided datasets, and improve over time without the need for comprehensive programming. Machine learning algorithms are able to detect patterns in data and learn from them, allowing them to make their own predictions based on what they’ve seen before. All this happens quite naturally through the computer processing huge amounts of information and observing how this influences future predictions. Computers have been using machine learning algorithms for many years now – probably even since you started using any kind of modern technology as your mobile phone. This article will help you to take the first few steps to learn machine learning.
In traditional programming, a developer often has to write very detailed instructions and direct the computer on how to solve the problem using his/her own logic. However, machine learning is different as it allows machines to come up with problems on their own (without any human input) and solve them by themselves.
Steps to learn machine learning
- Learn mathematics.
- Select a programming language & learn its library.
- Learn Python libraries.
- Learn about the machine learning library.
- Also, learn machine learning algorithms.
Step 1: Learn Mathematics
This is one of the first few steps to learn machine learning. If you want to understand machine learning (and AI), then you need to be willing to put in the time to understand each statistic as it applies directly next to every other statistic. Statistics is very detailed and can be tricky, so don’t be surprised if you haven’t already learned everything there is to know about it. You can start learning now and making sure you know statistics will allow you to perform a better, more effective analysis of your processes that could lead your way towards eventually developing better solutions for all types of problems.
You will need to learn the topics mentioned below on Mathematics for Machine Learning:
- Statistics and Probability
- Linear Algebra
Statistics and Probability
Machine Learning is a field of computer science that uses statistical techniques, algorithms, and models from artificial intelligence to develop systems to enhance understanding and create predictive models derived from big data. If you want to get up-to-speed with Machine Learning, both statistics and probability will assist you in getting a better idea of how the math behind this field works and why it’s an important skill that every modern-day statistician should consider having as part of their toolset.
these are some of the following topics in statistics and probability for machine learning
- Probability Distributions
- Conditional Probability
- Sampling and Central Limit Theorem
- Bayesian theorem.
- normal distributions and the central limit theorem.
- Inferential Statistics
- Descriptive Statistics
- Hypothesis Testing
- random variables, conditional probability, etc.
Machine Learning and data science as a whole must rely on linear algebra in some capacity. Many ideas fail to create a precise mathematical model. Algebra gives developers the ability to understand how algorithms operate under the hood and therefore make better choices. With Machine Learning, one has to know linear algebra in some way, shape, or form because everything is based on data relationships often expressed with highly sophisticated equations involving math.
Some of the important topics in linear algebra you should learn for machine learning
- Vectors and Matrix
- Eigenvalues and Eigenvector
- Matrix Transpose and Inverse
- Dot Product
- Matrix Factorization
- Principal Component Analysis
Step 2: Programing Languages for Machine Learning
To start learning machine learning, you should know that there are different programming languages for machine learning. But the most suitable programming language is Python because of its simplicity and has a large library to do machine learning. R also has its own advantage, like it’s well capable of highly processing on the user’s side.
Python is the ideal language for AI development. This makes it easier to address machine learning challenges because it provides users with a user-friendly syntax and natural way of coding. Python also offers various libraries and frameworks that help you analyze your raw data in a short amount of time. Furthermore, this language uniquely supports portability as well as high-speed performance which are essential features of machine learning. Learning Python can give you an edge in the first few steps to learn machine learning.
Step 3: Learn about Python libraries
Python library and frameworks for machine learning:
NumPy is an open-source library designed to be used in mathematical operations. It provides lots of quick calculations and manipulations of large data sets which makes it extremely useful for scientists, researchers, data analysts, and engineers who are working on projects related to the future of technology. Some widely used software like TensorFlow use NumPy because it offers fast computations of arrays and matrices that are necessary for deep neural networks.
NumPy is a Python extension module providing array computing capabilities for scientific computing. It’s flexible, efficient, has plenty of capable functions to choose from and best of all, it’s open-source which means it’s compatible with a variety of databases and operating systems (especially Linux-based systems)! NumPy is highly optimized for speed and convenience which makes it particularly easy to use.
Pandas is a popular and proficient Python library for data analysis. It is not directly related to Machine Learning, however, it can be used to help predict trends using analytics. It has a wide variety of features and high-level data structures that make it easy for people to work with data, read information from CSV or Excel files – and there are a lot more operations available using Pandas. What’s impressive about Pandas is that it provides many inbuilt methods for grouping, combining, and filtering certain columns from the dataset making it much faster in comparison to coding this logic from scratch too.
Step 4: Learn about Machine Learning libraries in Python
These are some of the machine learning libraries
Scikit-learn is a machine learning library that can be used to collect insights from large sets of data. It offers an extensive range of learning algorithms that provide a consistent interface in Python. It can also be used for data analysis, outlining, tool selection, and preprocessing tasks. While the standard Scikit-learn package carries its own preprocessing functions, an additional module called cross_validation has been developed specifically to help with model validation using statistical measures.
Google engineers created TensorFlow, a machine learning framework that allows for rapid prototyping of deep neural models. Its open-source software was released under the Apache License 2.0 on November 8th, 2015 and it is now widely adopted by data scientists around the globe. It has become popular due to its ability to run on multiple platforms while still providing a consistent API. TensorFlow has become a star in the machine learning platform space primarily because it can scale from easy prototyping all the way up to large-scale production systems. The framework is not only used by developers but also by more advanced users who want access to advanced features such as fine-grained control over precision and performance characteristics of operations with a high degree of parallelism.
PyTorch is a python library that can be used to support computer vision, machine learning, and natural language processing. This library is open-source and based on the previous torch library. The most significant advantage of this library is its ease of learning and use. PyTorch allows us to seamlessly integrate with the python data science stack, including NumPy. PyTorch smooths out the differences between NumPy and PyTorch by making full use of python idioms such as list slicing, broadcasting, etc. Not only that, you hardly identify any performance loss going from Numpy to Pytorch. Another advantage is multi GPU support, simplified preprocessor, etc.
Step 5: Learn Machine Learning Algorithms
Machine learning is a strategic step. You need to understand what you want the machine learning algorithm to do for you in terms of your task and what kind of perks each type brings to the table in terms of its benefits. There are three types of machine learning algorithms Supervised, Unsupervised, and Reinforcement. Learning different algorithms can make your efforts a lot easier in the first few steps to learn machine learning.
These are some of the commonly used machine learning algorithms
- Linear Regression
- Logistic Regression
- Decision Tree
- SVM (Support Vector Machine)
- Naive Bayes
- KNN (K Nearest Neighbours)
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boosting algorithms
- Confusion Matrix
Step 6: How to practice Machine Learning?
Machine learning requires a lot of time just to get the data ready. You need data that’s interesting and unique or you’ll never develop anything new, so make sure you experiment with specific datasets so that you can practice integrating, cleaning up, and prepping your data.
Learn about various models and figure out how to apply them to real datasets. You’ll gain some experience this way.
Along with following the steps outlined above, it is also important to understand how to interpret the results obtained by using different models. Not practice is the key in the first few steps to learn machine learning.
There are many online resources available that can be used to learn Machine Learning (both free and pay-based ones!). Some of them are provided here:
Machine learning is a powerful technology that serves to increase the amount and quality of data that you can accumulate for your business’s purposes. Successful companies all make use of advanced analytics these days – it allows them to take in all sorts of information relevant to their operations and put this data to effective use. Machine learning makes it possible to analyze large volumes of complex data, provide valuable insights, and suggest appropriate solutions based on what it discovers there.
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