The music industry, healthcare sector, banking, and social media use machine learning applications.
Demand for machine learning experts is at an all-time high.
Learning the basics of machine learning is essential because it has many applications.
Studying machine learning on your own is possible if you have knowledge of calculus, statistics, and probability – as these fields are at the heart of machine learning. Knowing how to code is key to success when delving into machine learning and implementing algorithms.
Becoming a machine learning specialist may seem daunting as it requires many different skills.
Machine learning basics are easily accessible to all, especially those interested in math, coding, and probability.
How To Learn Machine Learning Successfully
Steps to successfully learning machine learning include:
- Access free and paid online resources and courses
- Understand the mathematical concepts
- Use statistics and probability
- Learn coding in Python and R
- Learn about data structures
- Understand deep learning
- Learning and implementing algorithms
- Create your projects using a machine learning life cycle
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1. Online Resources And Courses
Creating a curriculum is important to be successful while learning machine learning. Careful planning is necessary for this process; a hands-on approach works well.
Numerous online courses have been designed to help beginners in their machine learning quest.
One such course is the Google Course which offers real-world scenarios, practical coding exercises, and math concepts.
There are also plenty of different MOOCs designed by universities, such as this one from Stanford.
Andrew Ng teaches another great course on Coursera. In the course, you will learn linear algebra and calculus, which provides a broad introduction to machine learning.
Bootcamps are a popular way of learning; it’s focused but require dedication and can be time-consuming. They often last for a couple of months and can demand up to 20 hours a week to enable completion.
Some of them guarantee job placements once the course is completed. Bootcamps may charge a fee and can be more expensive than fee-paying MOOCs.
If self-teaching is not something you embrace, consider doing paid courses for certification. There are numerous paid courses available. Some popular ones include:
- IBM Machine Learning Professional Certificate
- MIT professional-certificate-program-machine-learning-artificial
- AWS certified-machine-learning-specialty.
2. It’s All About The Math
Calculus, probability, and linear algebra are all important components of machine learning; having a basic understanding is necessary on your machine learning journey. Learning math is best done with lots of practice.
Start with the Khan Academy.
Linear algebra uses linear equations and represents data in a matrix format; matrices are often used in machine learning.
Combining several vectors results in a matrix. Although linear algebra can be a tricky topic for some, anyone can learn the basics relatively easily.
Machine learning uses linear algebra to transform and manipulate datasets efficiently; to reduce dimensionality and in vectorized code.
Results are produced in one step; this is a major advantage in using vectorized code; non-vectorized code requires multiple steps.
Vectorized operations in the form of linear algebra optimize the machine learning process.
Dimensionality reduction is possible by applying Principal Component Analysis (PCA) to datasets.
Dimensionality reduction aims to reduce large amounts of features or dimensions in datasets without losing too much information in the process.
This process is often done as many features can be highly correlated. Features can be categorical, text, or in image format.
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3. Learning Statistics And Probability
Probability and statistics are related but are inverse; probability goes from model to data, whereas statistics go from data to model.
Probability looks to the future, and statistics look to the past.
Probability theory has randomness or uncertainty and is modeled by random variables. Statistics observe something that occurred and explain why it is at its heart.
Probability is all about predicting the likelihood of a future event, whereas statistics involves analysis of the frequency of past events based on a model without actual data.
In statistics, the truth is inferred based on actual observed data.
Statistics uses quantified models to analyze a dataset.
Descriptive statistics are ways of summarizing and organizing the dataset.
Many algorithms are designed using probability techniques; Naïve Bayes is one such algorithm that is constructed using Bayes Theorem.
Many machine learning models are designed, tuned, and evaluated using probabilistic frameworks.
Many models are designed on the assumption that data is normally distributed. Gaussian Naïve Bayes, logistic regression, and linear regression are examples of this assumption. Sigmoid functions work best with normally distributed data.
Many datasets, for example, financial and forecasting data follow a log-normal distribution; this data will need to be transformed into a normal distribution.
Because of this phenomenon, understanding the dataset is critically important, done through descriptive statistics.
Descriptive statistics summarize, organize, and visualize a dataset to understand them better. Visualization techniques such as histograms, scatter plots, and heatmaps are commonly used to display data in Python and R.
Some useful online resources include:
The Khan Academy gives an overview of statistics and probability.
A series of 34 YouTube lectures by Joe Blitzstein is also a useful online resource Statistics 110: Probability (Harvard).
Think Stats provides a free introduction to probability and statistics for programmers.
Professor Leonard also has 28 YouTube statistics lectures.
4. Use Python And R to Learn How To Code
Python and R share similar features. R, developed by statisticians, is created for statistical analysis, making it a popular machine learning language, particularly in statistics-heavy projects.
Caret is a useful library that boosts the machine learning capabilities of R.
Python is one of the most popular programming languages because of its broad range of applications. With its simple syntax, Python is an easier language to learn; it can also handle large volumes of data well.
Python has more libraries than R; these include NumPy, Pandas, SKlearn, TensorFlow, and SciPy, often used in machine learning. Python doesn’t have many statistical model packages, however.
Python, and R, are both free, open-source software packages. Both languages have great community support with Reddit, RStudio, GitHub, and Stack Overflow forums.
Fundamental concepts such as understanding variables, data structures, functions, loops, and objects and learning how to use the various libraries and packages are important elements of learning Python or R.
Whether it’s Python or R, both will have you machine learning in no time.
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5. Learn About Data Structure
Data structures are code structures for storing and organizing data. In machine learning, two types of data structures are utilized, linear and non-linear.
The Linear data structure arranges data in an ordered way; elements are attached alongside each other.
Non-linear structures do not sequentially store the data.
Linear structures can be arrays, stacks, queues, or linked lists.
An array, one of the most common data structures in machine learning, is a collection of items stored at contiguous or unbroken memory locations, where multiple items of the same type are stored together; this calculates each element’s position easier. Online ticket booking systems use 2-D arrays.
Text editors like Microsoft Word and browsers use a stack data structure; this structure follows a Last in, first out (LIFO) principle, meaning the last element inserted inside the stack is removed first.
Think of a pile of plates on top of one another. In programming, an item on top of the stack is called push and pop when an item is removed.
Queues use the first in-first out (FIFO) structure, comparable to people standing in a queue. Priority queues are where elements are sorted, and the lowest valued element is first out.
Queues are used in handling large amounts of data and queueing documents or dealing with a list of websites that are scraped.
Linked lists use a sequence of nodes where each node has a value that points to the next. Some have the address of the next node; these are called doubly linked lists. Lists are often used in machine learning.
Non-linear data structures are maps, graphs, and trees. In Python, maps are called dictionaries and are extremely useful in machine learning. Dictionaries help implement sparse matrices; these are matrices where most of the values are zero.
Graphs are a way to visualize the data and are used to solve real problems. Knowledge graphs, social networks graphs, and keyword graphs are all possible; these graphs can be loaded into algorithms and used to perform regression, clustering, and classification.
Trees structures such as those of decision trees work well for classification and regression as they can represent non-linear relationships.
Tree structures have roots with or without subtrees. Knowing how to prune a tree helps improve machine learning models.
If you want to learn about data structures, Code Spaces has summarized a useful list of websites that offer courses; these include courses on Coursera, Udemy, and edX, amongst others.
6. Learn About Deep Learning
Although many use the terms interchangeably, machine learning and deep learning differ.
Machine learning requires human intervention to identify features, it’s less complex, and the machine learns to detect patterns and trends with training data. Predictions are then made with new data.
On the other hand, deep learning focuses on algorithms modeled on the human brain; it involves complex mathematical calculations; the need for manual feature extraction is removed.
Data is filtered through a series of layers to find patterns and trends that are often not predictable or explainable. Deep learning is what humans do naturally.
Deep learning is not always suitable for all datasets; it requires massive amounts of data to be effective and much more computes power than machine learning.
Deep learning is applied to facial recognition, music-streaming services, and Netflix; self-driving cars also rely on deep learning platforms.
These applications of deep learning incorporate several neutral network layers to perform.
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7. Machine Learning Algorithms
Machine learning algorithms use supervised, unsupervised, semi-supervised, or reinforcement learning methods to learn.
The more algorithms you implement, your machine learning ability is more systematic and efficient.
Some algorithms are easier to implement than others; KNN, Decision Trees, and Random Forests are amongst the easiest.
The top 9 algorithms to learn as a beginner are:
- Linear regression
- Logistic regression
- Support Vector Machine (SVM)
- Decision Trees
- Random Forests
- K-nearest neighbors (KNN)
- K-means clustering
- Principal Component Analysis (PCA)
Linear regression is the relationship between input(x) and output(y) variables; a line nearest to most points is fitted.
Finding the regression formula’s values is linear regression’s main aim. The formula for linear regression is y = a +bx; a is the intercept, and b is the slope of the line.
Logistic regression has discrete values (e.g., passing or failing an exam) and is used for binary classification.
Logistic regression uses a transformation function called the logistic function; this forms an S-shaped curve.
It can predict whether a tumor is malignant or benign, for example.
The naïve Bayes theorem calculates the probability of a hypothesis (h) being true given previous knowledge. It is naïve in that it assumes all variables are independent of each other.
Decision Trees are easy to implement and interpret; they can be used with non-linear data and applied to classification and regression problems.
KNN uses an entire dataset as a training set. It looks for the k-nearest instances to a new data record, using distance measures such as Euclidean or Manhattan (Taxicab); it then outputs either the mean or the mode of the outcome; the k value is user-specified.
K-means groups similar data into clusters and calculates the centroids (cluster center) of k number of clusters when assigning a new data point to a particular cluster.
Support Vector Machine algorithms create a hyperplane that separates the data into multiple classes. It tries to maximize the margin between the classes.
The support vectors are those observations lying closest to the hyperplane.
Random Forests are fast, flexible, and have higher accuracy than other algorithms; this algorithm is easy to implement. Random Forest algorithms use randomness to increase accuracy.
The algorithm is used in disease prediction, fraud detection, and recommender systems like those implemented by Netflix.
Fav tutor has a good guide for algorithms for beginners; examples are shown in Python.
8. Practice Machine Learning Projects
A machine learning life cycle is a step-by-step approach used to manage and automate the machine learning process in an organization.
It’s important to incorporate these steps as it takes each project from start to finish with a proper structure.
The machine learning life cycle includes:
- Define the problem
- Data gathering and exploration
- Data preparation and feature engineering
- Algorithm selection
- Model training
- Model testing and validation
- Model deployment
- Performance and monitoring
Employers look for professionals with first-hand experience with Machine Learning tools and applications. Embarking on independent projects allows you to practice your skills, providing a good learning curve for beginners.
ProjectPro has provided links to various end-to-end machine learning projects that include source code.
Intellipaat has a list of the 10 top machine learning projects; some of the projects mentioned are bitcoin prediction, wine quality test, and music genre classification.
Become A Self-Taught Machine Learning Engineer
LinkedIn placed machine learning in the top 15 booming job sectors in the U.S in 2020. Jobs in this sector exploded by over 300% between 2015 to 2018. The average take-home pay of a machine learning engineer is almost $150k.
All industries have applications in machine learning, hence the high demand for machine learning engineers. The field of machine learning is facing skill shortages despite its exponential growth.
If ever there was a good time to learn, it’s now; combine it with data science, and you will be in even higher demand.
A hands-on approach in your machine learning quest will help you acquire the practical skills needed to get a job in the industry.
Learning the fundamentals of calculus, statistics, and probability, as well as acquiring programming skills in Python or R to implement the different algorithms, are important steps for any beginner.
A top-down or bottom-up approach can be taken when learning machine learning; top-down approaches approach the theory before the practical application of the theory.
Bottom-up approaches dive straight into the practical; learning the theory comes during the application or afterward.
Working on projects and participating in competitions on sites such as GitHub and Kaggle will be something you will likely do on your journey to get experience in the field.
Many free and paid courses are available online to master the basics.
Coursera, for example, offers online master’s degrees from top universities and schools.
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Stay focused on the core concepts to start with and find projects that interest you.
It will take a lot of time and effort to learn everything needed to be successful in machine learning.
The key to machine learning is to never stop learning.