Terms Beginning With 'm'

Machine Learning

What is Machine Learning?

Machine Learning, or ML, is the science that enables computers to function without being programmed explicitly. It is because of Machine Learning algorithm that we are able to see significant advancements in Artificial Intelligence in the present times.

Artificial Intelligence, in a broad sense, can be referred to as machines that can learn, reason and act for themselves.

Machine Learning deals with the development of computer programs capable of accessing data and use it to learn for themselves. The primary objective of ML is to allow computers to learn on their own without any human intervention.

Real-world examples of Machine Learning

In an age where technology is omnipresent, most of us are familiar with names, including Netflix, Google, Amazon, Facebook, and Twitter. One must have noticed that when one runs a search on any one of these platforms, related searches and recommendations are also displayed.

Let us take the example of YouTube. Suppose you logged in to YouTube and searched for food-related blogs. Now when you log in the next time, you will notice that YouTube shows related videos related to food blogging. Now, the question arises how does this happen?

The answer is simple; it is because of Machine Learning.

In the applications mentioned above, each platform collects as much data as possible based on your taste, preference, the choice of link. Assessing the data, the application guesses about what you would want to search next.

How does a machine learn?

There are different ways in which a machine learns. These include supervised, unsupervised, and reinforcement learning.

Supervised Learning:

Supervised learning is a Machine Learning method, in which the models are trained based on the labelled data.

As the name suggests, Supervised learning requires supervision to train the model.

Y = f(X)

In the above function, X is the independent variable, while Y is a dependent variable. Whatever we input as variable X, we get the respective output as Y.

For example, we have two vegetables, and the supervised learning model is expected to identify each vegetable and classify them. We provide the input data as well as the respective output. It means that we would train the model based on features including shape, color, and size, among others.

Let us consider two vegetable: potato and okra. Potato has an uneven shape and has a brown color skin while okra is slim and green in color. Data related to each vegetable is provided as input. Here the name of the vegetable is “label” and size, and the color is the “features”.

Once the training is complete, the model is tested by providing a set of new veggies. The machine then recognizes the vegetables.

Unsupervised learning:

Unsupervised learning refers to learning with unlabeled data, also known as unsupervised data. In this, the system can infer a function to describe the hidden structure from unlabeled data. The system looks for data and derives a conclusion from dataset to describe the hidden structure from the unlabeled dataset.

Let us understand this using an example:

Suppose we have some bank data to detect fraudulent activity in a transaction and flag any fraud detection. In this case, the suspicious transactions are not defined. Hence there are no labels of “fraud” and “not a fraud”. The model attempts to find outliers by looking at abnormal transactions and signals them as a fraud.

Reinforcement Learning:

Reinforcement Learning is reward-based learning. This means that the model works on the principle of feedback. Let us consider an example where you provide the system with an image of a mango. However, the output provided by the system was the image of an apple, which is incorrect. You then give feedback to the machine that the image is of a mango. Next, in case a similar image comes, the system is able to identify the image as that of a mango quickly.

How does Machine Learning work?

Machine Learning drives inspiration from how the human brain functions. Just like humans learn from experience, the machine also learns similarly. The more you know, the better it is easy to predict.

The main objective of Machine Learning is to learn and provide inference. The learning process is done through the discovery of the pattern, possible through the massive data available. An essential step in this process is choosing and providing relevant data to the machine. The machine then filters the list of attributes required to solve the problem, known as the feature vector. The device uses some algorithm to simplify the data and then prepares a model.

Application of Machine Learning:

Below are some of the fields where we could see the application of Machine Learning:

  • Self-driving cars
  • Product recommendation
  • Traffic Prediction
  • Speech Recognition
  • Online Fraud Detection
  • Virtual Assistance
  • Image Recognition
  • Stock market trading
  • Medical diagnosis
  • Automatic language translations
  • Filtering of spam data from mail account.

INTERESTING READ: Technology Sector Shows Momentum: What Investors Should Know?

Top Five Programming Languages for Machine Learning:

  • Python
  • Java
  • C++
  • R
  • JavaScript

DO READ: Big Data- The Emerging Gold of The Modern Era

Calculating the cost of a product or an enterprise based on the direct and the indirect costs (overheads) involved. Multiple methods of absorption costing include Direct labour cost percentage rate, Direct material cost percentage rate, Labour hour rate , Prime cost percentage rate and Machine hour rate.    

What is Data Analytics?  Data Analytics involves a set of quantitative and qualitative approaches and processes that can be used to determine useful information for business decision-making. The process involves various patterns and techniques, including: extracting a raw database, and categorising it to identify and analyse the behaviour, relation and connection of the results.  The ultimate goal is to acquire valuable information in order to make decisions for businesses’ benefit and productivity.  In today's competitive times, most companies chalk out their business plan with the help of data analytics. With organisations becoming customer-service oriented, data analytics has become a critical tool to reach the target audience in an effective manner while understanding their requirements. Once data is collected, it is analysed and stored according to organisations’ requirements.  The data analysis process has multiple layers involved, and its diverse modules are not just used in businesses but also in science and social science fields. Rather than making decisions based on just available information, one can utilise data analytics in examining the data in standard ways and churning out the results from it.  It has been observed that companies generally make decisions based on past references and future outcomes. Data analytics appears advantageous in providing useful information towards this end.  Why do Businesses Need to Use Data Analytics?  Many data analytics’ tools and softwares are readily available these days. These systems use resources, such as machine learning algorithms and automation.   Data scientists and analysts are counted amongst the leading career options as well. These professionals use data analytics techniques while researching and presenting useful information for businesses to increase productivity and gain. The process helps companies understand their target audience and determine effective ways to cater to their needs. Data analytics can further be used to design strategies in marketing campaigns and promotions and also evaluate its results.  Data analytics is primarily used in business-to-consumer (B2C) processes to boost business performance and improve the bottom line. There are data collection firms which gather consumer information and provide it to the businesses so that the companies can effectively influence the market. The collected data is not only used to understand and impact consumer behaviour but also determine market economics and its practical implementation.  The data used in the process can be either be data collected in the past or newly updated data. There are various methods to manage consumer and market information. It may come directly from the customers or potential customers or can be purchased from the data collection vendors. The data primarily includes audience demographics, behavioural patterns and expense threshold.  How Can Data Analytics be Effectively Used in Business Processes? Data analytics is an ever-evolving technique. Earlier, the data was collected manually, but with the rise of internet and technology, data is now collected online with the help of search engines and social media platforms. Subsequently, the information is analysed through available software.  Here is a list of some key steps businesses can follow to leverage the benefits of data analytics: Set up crucial metrics: This step reduces the guesswork and provide data-based insights to the businesses. Before embarking on the data analytics process, it is vital to determine the goal for your business. Analysing customer data helps in understanding conversion rate, consumer spending ability, demographics etc. The results of the analysis can support the businesses while making decisions in launching an advertising or marketing campaign. Similarly, the unwanted data can be erased from the database so that the brands can focus on their right target audience. The relevant metrics will change the course of the company and push it in the right direction. Moreover, once your key metrics are set, even when the market conditions change in the future, you can adjust the metrics according to the requirement and achieve the results. Set a clear module: It is important to examine the data correctly by avoiding common mistakes. An ambiguous path can produce confusing insights while wasting time and energy of businesses.  Therefore, it is recommended to draw a clear goal in order to achieve actionable insights. The data, when collected from different sources, need to be merged accurately in the analytics model. Businesses can modulate their data analytics systems either manually or through automation. There are various data modelling practices available in the market. The best use of these techniques can simplify the process of modelling complex data.  Data visualisation: Once the relevant data is collected, and the modules are set to analysis, visualisation of that data will assist in understanding the information correctly. When the businesses have an acute knowledge of what their target audience wants, they can then focus on strategising advertisement and content, which matches the consumers' interest.  It is the critical step in the data analytics process to distinguish insights from information.  Not everyone is comfortable dealing with numbers. Hence, ensuring that key stakeholders understand essential points and information can be displayed in a visually appealing format seem crucial to capitalise on data effectively. Right tools to implement insights:Having access to data and insights can get overwhelming. However, the information is worthless if the businesses are unable to implement it successfully. While it is important to collect the data and set critical metrics and modules to analyse it, it is also imperative to translate the data into practical actions. The eventual goal is to improve sales or grow profits. It is ultimately in the marketers' hands to transform the gained insights into a successful implementation. The consumers' insights should be incorporated while establishing a marketing plan and at all decision-making steps. 

What is EBITDA? Earnings Before Interest, Taxes, Depreciation, and Amortisation (EBITDA) is a widely used financial metric in evaluating cash flows and profitability of a business. Market participants closely track EBITDA and apply it in decision making extensively. Although conventional investors like Charlie Munger had raised concerns over the use of EBITDA, it is very popular in markets, and M&A transactions are mostly priced on EBITDA-based valuation like EV/EBITDA (x). EBITDA is not recognised by IFRS and GAAP but is used extensively in the Corporate Finance world. It is now a mainstream financial metric that companies look to target. EBITDA depicts operational cash generation capacity of a firm in a given period. It acts as an alternative to financial metrics like revenue, profit or earnings per share. EBITDA allows to evaluate a business operationally and outcomes of operating decisions. Non-operating items are excluded to arrive at EBITDA. EBITDA excludes the impact of capital structure or debt/equity, and non-cash expenses like depreciation and amortisation. A particular criticism of EBITDA has been the inappropriate outlook of capital intensive businesses, which incur large depreciation expenses. Business with large assets incurs substantial costs related to repair and maintenance, which are not captured in EBITDA because depreciation expenses are accounted to calculate EBITDA. Meanwhile, EBITDA can paint an appropriate picture for asset-light business with lower capital intensity. While revenue, profit and earning per share remain sought-after headline generators for corporates, EBITDA has also found its growing application in the corporate finance world and is now a mainstream metric to evaluate a business financially. Perhaps the growth of asset-light business models has also added to the use of EBITDA. Its debt-agnostic approach to evaluate businesses has given reasons to investors, especially for high growth firms during capital expenditure cycles. But EBITDA has been present for close to four decades now. In the 1980s, the growth in corporate takeovers through leverage buyout transaction was on a boom. EBITDA grew popular to value heavy industries like broadcasting, telecommunication, utilities. John Malone is credited for coining this term. He was working at TCI- a cable TV provider. Since EBITDA has remained an important metric to determine purchase price multiples and is highly used in M&A transactions. EBITDA’s application in large businesses with capital intensive assets that are written down over a long period has been a source of concern for many investors. Although EBITDA is an effective metric to evaluate the profitability of a firm, it does not reflect actual cash flow picture of a firm during a period. Also, it does not account for capital expenditures of the firm, which are crucial in successfully running a business. EBITDA does not give a fair cash flow position because it leaves out crucial items like working capital, debt and interest repayments, fixed expenses, capital expenditure. At the outset, there can be times when EBITDA may overstate performance, value and ability to repay debt. How to calculate EBITDA? NPAT: Net Profit after tax is the amount reported by a firm in the given period. It is present on the income statement of the firm and is used in the calculation of earnings per share of an entity. To calculate EBITDA, interest expense, tax, depreciation and amortisation are added to NPAT. Interest Expense: Firms can employ debt in their capital structure, and interest expense is funds paid to lenders as interest costs on principal debt. Most companies have different financing structure, and excluding interest payments enable comparing firms on operating grounds through EBITDA. Tax: Firms also pay income tax on profits. Excluding taxes gives a fair picture of the operating performance of the business since tax vary across jurisdictions, and sometimes according to size of business as well. Depreciation: Depreciation is the non-cash expense to account for the steady reduction in value of tangible assets. Firms can incur depreciation expense on machinery, vehicles, office assets, equipment etc.  Amortisation: Amortisation is the non-cash expense to account for the reduction in the value of intangible assets like patents, copyrights, export license, import license etc. Operating Profit: Operating profit is the core profit of a firm generated out of operations. It includes cash and non-cash expenses of a firm, excluding income tax and interest expenses. Operating Profit is also called Earnings Before Interest and Tax (EBIT). Read: EBIT vs EBITDA What is TTM EBITDA and NTM EBITDA? Trailing Twelve Months (TTM) or Last Twelve Months (LTM) EBITDA represents the EBITDA of the past twelve months of the firm. It allows to review the last operation performance of the business. Whereas NTM EBITDA represents 12-month forward forecast EBITDA of the firm. NTM EBITDA is also one-year forward EBITDA. Market participants are provided with consensus analysts’ estimates for a firm, which also include NTM EBITDA, NTM EPS, NTM Net Income or NPAT. What is EBITDA margin? EBITDA margin is the percentage proportion of a firm EBITDA against total revenue. It indicates the operational profitability of the firm and cash flows to some extent. If a firm has a higher margin, it means the level of EBITDA against revenue is higher. It is widely used in comparing similar companies and enable to evaluate businesses relatively. If a firm has a total revenue of $1 million and EBITDA is $800k, the EBITDA margin is 80%. What is adjusted EBITDA? Adjusted EBITDA is calculated to provide a fair view business after adding back non-cash items, one-time expenses, unrealised gains and losses, share-based payments, goodwill impairments, asset write-downs etc.

What is depreciation? Depreciation is an accounting method used to allocate the cost of a tangible asset to the books of accounts over the useful life of the asset. It is essentially the accounting for wear and tear on the asset over its useful life.  Depreciation also refers to the value of the asset that has been used over time. Assets of a firm that are used for over a one-year period largely include physical assets. Although firms incur expense while purchasing these assets, the expenses are not charged in the income statement.  Such assets are recorded in the balance sheet of the firm and are expensed on the income statement as depreciation expense over time during the life of the asset. The tax authorities also decide the useful life of assets because overstating depreciation expense can lower tax liability.  Now assets come in two variety: tangible assets and intangible assets. As the name suggests tangible, the tangible assets can be touched, such as equipment, machinery, computers, vehicles etc. Depreciation is used to expense the tangible assets of a firm.  Intangible assets cannot be touched and include assets like licenses, copyrights, patents, brand names, logos etc. Amortisation of assets is an accounting method similar to depreciation used to expense intangible assets.  Long-term assets are the source of generating revenue for firms over a long period of time, therefore the cost of acquiring tangible long-term assets is not expensed fully at the time of purchases and is expensed over the life of the asset.  As the asset is used over periods, the carrying value of an asset in the balance sheet is reduced over time. Carrying value of an asset is the original cost minus accumulated depreciation on the asset over time.  Since the cost of acquiring the long-term tangible asset is not expensed fully at the time of purchase and is expensed over its useful life, the depreciation expense is a non-cash charge because actual cash outgo was incurred at the time of purchase.  But depreciation expense reduces the reported earnings of the company as it is charged on the income statement of the firm. Since the expenses are deducted from the revenue of the firm, the tax liability of the firm is also reduced.  What are the methods of depreciation? Straight-line method The straight-line method is the most common method of depreciating an asset over its life. Under this method, the recurring depreciating amount of the asset remains constant and is not changed over the life of the asset.  For example, a firm buys a machine for $10000 with a salvage value of $2000, and the useful life of the asset is ten years. The depreciable value of the asset will be $8000, which is the cost of machine minus salvage value.  Now the firm will depreciate the $8000 each year at a rate of $800 per year. The per-year depreciation charge of $800 is the depreciable value of the asset divided by the useful life of the asset (8000/10).  Double declining balance depreciation method  It is an accelerated type of depreciation method. Under this method, the depreciation expense in higher in the beginning years and gradually reduces over the life of an asset. It also reflects that assets are more valuable in the early years of production compared to later years.  In this method, the subsequent depreciation charges after the initial charge are calculated using the ending balance of the asset in the last period. Ending balance of the asset is the original cost of the asset less accumulated depreciation. Also, the depreciation factor in this method is twice of the straight-line method. Depreciation expense = (100%/Useful life of asset) x 2 Why is depreciation due diligence important? Depreciation can be used to manipulate the financials of the company. Overstating and understating depreciation charges directly impacts the profit of the company. When a firm is charging less depreciation than required, it would directly increase the profits of the firm.  When depreciation expense is lesser than the actual expense, the income statement will record lower amount of expenses, therefore the deductions from revenue will lesser and profits will increase.  Investors also assess whether the useful life of asset used in calculating the depreciation of firm is appropriate or not. The companies should use an appropriate useful life of the asset. When the useful life of the asset is increased, the depreciation charges will spread across an increased number of years.  As a result, the depreciation expenses during the life of an asset would be understated since the actual life of an asset is less than recorded. Investors prefer checking the number of years used as the useful life of an asset.  Sometimes firms may choose to change the method of depreciation. Although it could be appropriate when actual business conditions don’t match the method adopted, there remains a possibility that the decision to change the method could be driven by the motive to manipulate depreciation expenses.  Companies may seek to keep the assets in the balance sheet even though the asset is of no use. This will help the company to keep incurring depreciation expense on the income statement and reduce the tax liability of the business.  When the value of assets of the company has appreciated in light of the market environment, the balance sheet value of the asset will also increase. When the balance sheet value of an asset is increased, the depreciation charges should also increase. Therefore, appreciation in the value of an asset should also increase depreciation expense for the company. 

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