Machine Learning
Step by Step Guide to Learn Machine Learning
🧐 Mastering this skill from scratch🧠
· Technology has completely altered the way people live and experience their life. Almost everything online is run by Machine Learning today.
· Machine learning is the learning of Computer Algorithms that improve automatically through its experience.
· Machine Learning is used in every software, Web-platform, Search Engine, and in every Application/Device in this age of science and technology.
· Due to its advanced uses, there is a lot of demand in companies for people who are experts in Machine Learning.
· More than 80% of employees in tech companies are working on Machine Learning algorithms and its application in a variety of domains, thereby working on large-scale, dynamic data.
General Outlook on Machine Learning
Machine learning is a method of Data Analysis that automates analytical model building. Mastering this skill is a demanding process, but it pays off. According to SpringBoard, an average Machine Learning expert earns up-to 70 lakhs per annum in the United States. It is also the highest growing job profile in terms of the number of postings with a 344% growth rate from 2015–2019.
There are countless sources available for learning Machine Learning (ML). As technology advances at a very high rate, these sources age by the second. It takes focused determination towards the subject to grasp the concepts and fundamentals of Machine Learning. The pros of learning Machine Learning outweigh the cons. With huge returns and job security, it is unquestionable that Machine Learning is an excellent skill to have in your resume.
Detailed Plan for Learning
Learning how to master advanced Machine learning (ML) requires a strict sense of principles. It is considered as one of the most advanced and skilled courses in the world. Since Machine Learning is a broad subject, it involves studying several Programming Languages, Data Engineering methods, and more topics. This means that several components are needed to be mastered in due course. There are various Blocks of learning that you must revise before getting further into the Machine Learning course.
Building Blocks
· Mindset Development: The first step before undergoing the ML course is to be psychologically prepared for it. This step is for students who pick-up second thoughts when they learn about Machine Learning, which is a challenging course to undertake.
To avoid being lost in Machine Learning, every student needs to make sure they can handle the pressure by sticking to their plan. If they have the right passion, determination, and focus on learning this skill, only then should they proceed further.
· Brush-up on Basics: Due to the advanced field of study, there are plenty of basics required to be learned to understand their uses in ML.
· Supervised and Unsupervised Learning: To detect patterns using labeled and unlabeled data
· Data Pre-processing: An essential part of Data Mining.
· Ensemble Learning: To improve predictive performance by using learning algorithms
· Model Evaluation: To establish the quality of the relationship between Model and Data using metrics.
· Splitting and Sampling: To divide sets of data in different categories for testing
Skill Development
After understanding the various basics of ML and their uses, the next step is to learn handy skills that can be used in ML Projects. As Machine Learning is algorithms and Data-based structuring, you must have these skills to understand and follow-up with the next steps of Machine Learning.
· Python Programming: Python is a programming language that is widely used in ML Projects. Focus on learning the language, Data structure, libraries, and application of Python.
Learning Python programming and its uses can be done at home using Certified Online Courses. This course can be completed within 30–45 days.
· R Programming: R Programming is widely used in ML Applications that deal with large sets of Data. It is equally crucial as Python in ML as these two languages are used to design the code of ML Projects.
· Computational Thinking: This is one of the most underrated skills that is very important for every aspiring ML professional to have. Computational Thinking involves deducing the solution towards a formulated problem and expressing it so that a computer can carry it out effectively.
Statistical Analysis
The next step in this journey is to learn Statistical Analysis and Mathematical equations. ML Applications are a set of algorithms that use Data to grow. The application of Data Analysis without the knowledge of Statistics and Mathematics is practically impossible.
· To work with large amounts of Data, conceptual knowledge of the Theory of Probability and Linear Algebra is a pre-requisite.
· As ML Projects involve summarizing and describing extensive data in quantitative terms, you need to be familiar with Descriptive Statistics.
· For estimate derivation and background hypothesis testing, it is also vital to be accustomed to Inferential Statistics.
· ML applications require derivative-based calculations. To be able to make these calculations, Calculus proficiency is a must.
Data Preparation
Preparing data means using Data analytics to perform functions, to produce visualized plots and histograms. It means that the attributes of data need to be described along with the relationship between these variables.
· Data Selection: Selecting the right data means considering the data available and analyzing it further to deduce what information is missing and what is unusable. Unusable data should always be removed.
· Data Preprocessing: In this step, the finalized Data needs to be cleaned and re-organized to make accurate calculations and further judgments.
· Data Transformation: Using Scaling and Attribute aggregation/decomposition, you can transform processed data in different formats for Machine Learning.
Data Cleaning
A Machine Learning Expert will always ensure that the original Data is filtered and has undergone Data Cleaning. This procedure is carried out to add structure to the Data.
· The first step in Data Cleaning would be to identify variables in data by conducting Univariate and Multivariate Analysis.
· Missing Value Treatment should be done upon Data to ensure that the analytical results are not misguided due to missing data items. Missing data poses a risk to the process of accurate classification of Data.
· In a broad set of Data, averaging of variables is carried out to simplify and classify it. However, some variables are profoundly different from the pattern followed by the remainder data. These variables are called Outliers. The Treatment of Outliers is essential for understanding the genuine average and pattern of data.
· After conducting the above steps, it is crucial to perform Variable Transformation and Variable/Feature Creation to create derived variables. These two methods combined are called Feature Engineering.
Data Modeling
Data Modeling deals with analyzing patterns among the various fields in a cluster of data. These patterns are then used in ML for predictions and probability-based calculations.
· To learn Data Modeling, you will first need to learn Structured Query Language (SQL). Learning SQL is useful for Database Administration. It helps in retrieving data or otherwise interface with a regional Database.
· The two primary components of Data modeling are Entity-Relationship (E-R) Model and Unified Modeling Language.
· Data modeling is like a construction plan for Architects. It constitutes of a conceptual model and relationship between data items.
Algorithm Development
ML Algorithms are the core subject in the Machine Learning course. It deals with ML’s fundamentals, which are automatically learning and advancing by using patterns from data.
There are three types of Machine Learning (ML) Algorithms:
· The first algorithm is the Supervised Learning Algorithms. These algorithms are used for learning mapped functions, which turn input variables (x) to output variables (Y). In layman terms, it solves the following equation:
Y =f(X)
· The next type of ML algorithm comes into play when there no output values available. Unsupervised Learning Algorithms use unlabeled Data to perform steps like Association, Clustering, and Dimensionality Reduction, to add structure to data models.
· The third type of Machine Learning algorithm is the Reinforcement Learning Algorithm. With this algorithm’s help, the user or agent can predict the next best possible step based on the current behavioral patterns.
Applied Machine Learning: System Design
Let’s assume you have created an ML algorithm after studying and experimenting with it. Now, a raw ML algorithm is practically unusable to people without a medium, which allows them to use the algorithms in practical life. Lines of coded algorithms are gibberish to the final consumer without a designed system.
· Problem Solving: This step involves deciding the main problem that your ML will solve for its users. This system must be a value-addition to the business of the user.
· Data: Work on Real-Time Data storage systems to provide constantly high-performing ML models such as Hadoop/Hive.
· Modeling: An ML system needs to be modeled to be in an interpretable format for its users.
· Evaluation: Evaluate the performance of the system by introducing new sets of independent Data. This evaluation’s primary goal is to check the success rate of accurate future predictions made by ML in different situations.
· Key features: Add critical features to the system which tune the overall performance. It will make the system more usable and improve the User Interface with the agent.
· Testing: Improvise with all features of the system. Assess possible risks and evaluate what can go wrong. It will help you work on the weaknesses in the system, in-order-to produce a foolproof design.
Machine Learning Projects
What is the point of learning all about Machine Learning without testing the knowledge you have? Machine Learning projects are the best way to examine your abilities in ML.
There are several ML projects available on the internet for you. The data required for these projects are also available on the web. You can refer to Kaggle for all Machine Learning project details.
· Forecast the future sales of Wal-Mart using previous year Datasets available online.
· Recognize types of Human Activities using location, pulse, and other available data.
· Recommend Movies and Music based on previous activities of a user.
· Classify cars into respective Brands and Models based on their individual data available.
· Quality Prediction of Wine.
· Predict the future price of stocks using Analytical tools and Trend Analysis.
If you can complete these projects and produce accurate results using your ML algorithms, you are considered an Intermediate in the field of Machine Learning.
Focused Experimentation
After working on the projects, you may have had plenty of practice. It is essential to keep increasing the standard of your ML Algorithms. It can be achieved by Targeted Practice.
· Regular flow check: Right from Data mining/collection to System Design and Evaluation, keep practicing the Machine Learning process flow.
· Practical Applicability: Do not stick with virtual data. Use real-time data to ensure that your system is ready for any challenges it may face in the working world.
· Prepare for big projects: Keep developing the cluster algorithms to work on more massive sets of complex data to ensure you are ready to take the next step with your work.
Decisions regarding Datasets
While working with Datasets, there are numerous situations where you will make essential decisions to micromanage your system and solve various problems. Hence, it is necessary to work with different Datasets.
· The most common Dataset, to begin with, is the UCI Machine Repository.
· Use different sets for regression, classification, and clustering.
· Work on automatic pre-processing of different sets of Data.
· Decide whether you should pick a sample or split the data for its Statistical Analysis.
· Contemplate the Performance Metrics needed for individual sets.
· Consider Ensemble for producing better results.
· Decide upon which features provide higher accuracy. The Dimensional Reduction or Feature Selection?
What is Data Science?
It is one of the most common questions that one has in mind while searching for Data Science.
· Data Science is an interdisciplinary field that derives knowledge and simplified information from structured and unstructured data. This simplified information makes it easy to read and retain it.
· Data Science exclusively refers to the process of assigning meaning to a group of data.
· Data Scientists use cloud computing tools to create an environment for virtual development. Mathematical Statistics, Big Data, and Machine Learning are some standard methods used in the process.
· Large Scale businesses use Data Science strategies in creative ways. It also increases their competitive advantage in the world of business.
· Data Science processes include Business Analytics, Business Intelligence, Data Mining, Predictive Analytics, Data Analytics, and Data Visualisation.
The Application of Data Science with Netflix
To begin their Analysis, Netflix gathers Raw Fata, from which it plans to extract resourceful information using Data Science Algorithms. A combination of these algorithms transforms plain numbers to a detailed Recommendation Plan. For every 5 minutes a user spends on scrolling, Netflix can predict more than 40% of their relative selection patterns. There are several fields on Netflix, where Data is collected, captured, and stored.
· Time: The primary step is to understand and store the Time and Date when users stream content. It helps them identify your Sunday night-horror movie plans or your Afternoon-thriller preferences.
· Searches: All Search Titles are automatically stored to re-direct further recommendations towards these searches. Let’s say you search “John Wick,” watch the movie and close Netflix. The next time you switch the application back on, you will undoubtedly find more Action movies or more Keanu Reeves starters.
· Browsing and scrolling behavior: Netflix also uses Advanced Analytical programs to identify which Movie/TV show you decided to stop and read about. It helps them showcase more similar content to catch your eye and get you interested again.
· Pause/Fast-forward: Using Data Science, Netflix catches the exact durations where a user starts Pausing or Fast-forwarding while streaming content. It helps it identify what kind of scenes are preferred over others. If you skip an action movie’s emotional scene, it develops the algorithm to avoid passionate movies in future recommendations. But if you re-watch an emotional scene, it will adapt accordingly.
· A device used: If you use separate mechanisms to stream different content, this differentiation is stored permanently. For example, Children watching cartoons on the home-TV will not be recommended movies watched by their parents on the iPad, despite using the same account.
Conclusion
Machine Learning is a broad subject in various fields to study. It takes an intense amount of time and energy to gain the knowledge required to become a Machine Learning Professional. Once you have reached this stage, nobody can stop you from conquering great heights in your life. ML experts are one of the most desired and respected job profiles in the world today.
I`ve always taken life as a journey from one experience to another. So far it has been a road full of interesting events and people. Join me on my Journey through LinkedIn, Instagram & Youtube
To become an expert in this field, you will need to become an expert in a dozen of smaller areas first. If you plan to become a Machine Learning Professional, several companies are already waiting for you to join them. If you begin today, you will kick-start your life towards a bright and secure future.
With all the information at hand, you are hopefully prepared to become a successful Data Scientist in the future. Hope this helps and all the best for your future endeavors! Thanks for reading this article! Leave a comment below if you have any questions.
