<prompt> Hlalosa mohopolo oa ho sefa spam.nan  <response>: Instruction in English: Explain the concept of spam filtering. . Response in English: Spam filtering is the process of detecting and preventing unwanted messages, commonly known as spam, from appearing in a users inbox. Spam emails are usually unsolicited bulk messages sent for commercial purposes or to spread malware, and can be a major annoyance for email users.To combat this problem, email services and programs use various spam filtering methods to identify and sort out spam emails before they reach the users inbox. Some common spam filtering techniques include:1. Content filtering: This involves analyzing the content of an email, including the subject line, the message body, and any attachments, for common spam characteristics such as certain keywords or phrases.2. Header analysis: Spam filters can also examine the header of an email, which contains information about its origin, destination, and routing, to detect inconsistencies or other signs that the email is spam.3. Blacklisting: Some spam filters use blacklists, which are lists of known spam senders, to block emails from those IP addresses or domains.4. Bayesian filtering: This is a statistical approach that uses algorithms to analyze emails and determine the likelihood that they are spam based on previous examples of spam and legitimate emails.5. Machine learning: Many modern spam filters use machine learning techniques, where the system is trained on a large dataset of emails to identify patterns and characteristics of spam emails, and can then filter out new emails based on this knowledge.Most email services and programs use a combination of these techniques to provide robust and effective spam filtering. While no spam filter is perfect, and some legitimate emails may occasionally be mistakenly flagged as spam, these methods have greatly improved the email experience for users by reducing the number of unwanted messages that reach their inboxes. . Response in Sesotho: Ho sefa li-spam ke mokhoa oa ho lemoha le ho thibela melaetsa e sa batleheng, e tsejoang ka hore ke spam, hore e se ke ea hlaha ka hara inbox ea mosebedisi. Li-imeile tsa spam hangata ke melaetsa e mengata e sa kōptjoang e romelloang merero ea khoebo kapa ho jala malware, me e ka ba khathatso e kholo ho basebelisi ba imeile. Ho loantsa bothata bona, litsebeletso tsa lengolo-tsoibila le mananeo a sebelisa mekhoa e fapaneng ea ho sefa li-spam ho tseba le ho hlophisa mangolo-tsoibila a spam pele a fihla ho inbox ea mosebelisi. Mekhoa e meng e tloaelehileng ea ho sefa li-spam e kenyelletsa: 1. Sefa sa litaba: Sena se kenyelletsa ho sekaseka litaba tsa lengolo-tsoibila, ho kenyeletsoa sehlooho, mele oa molaetsa, le lihokelo life kapa life, bakeng sa litšobotsi tse tloaelehileng tsa spam joalo ka mantsoe a bohlokoa kapa lipoleloana. 2. Tlhahlobo ea lihlooho: Li-filters tsa spam li ka boela tsa hlahloba sehlooho sa lengolo-tsoibila, le nang le tlhahisoleseding e mabapi le tšimoloho ea eona, sebaka seo e eang ho sona, le tsela ea ho tsamaisa, ho fumana ho se lumellane kapa matšoao a mang a hore imeile ke spam. 3. Lethathamo la Blacklist: Li-filters tse ling tsa li-spam li sebelisa li-blacklists, e leng manane a batho ba tsebahalang ba romellang spam, ho thibela mangolo-tsoibila ho tsoa ho liaterese tsa IP kapa libaka tseo. 4. Ho sefa ha Bayesian: Ena ke mokhoa oa lipalo-palo o sebelisang li-algorithms ho sekaseka li-imeile le ho fumana monyetla oa hore ke li-spam ho latela mehlala e fetileng ea spam le li-imeile tse molaong. 5. Ho ithuta ka mochine: Li-filters tse ngata tsa morao-rao tsa spam li sebelisa mekhoa ea ho ithuta ka mochine, moo tsamaiso e koetliselitsoeng ho dataset e kholo ea li-imeile ho khetholla mekhoa le litšobotsi tsa li-imeile tsa spam, me joale li ka sefa li-imeile tse ncha tse thehiloeng tsebong ena. Boholo ba litsebeletso tsa lengolo-tsoibila le mananeo a sebelisa motsoako oa mekhoa ena ho fana ka sefa se matla le se sebetsang sa spam. Le hoja ho se na mochine oa spam o phethahetseng, me ka linako tse ling li-imeile tse amohelehang li ka na tsa tšoauoa ka phoso e le spam, mekhoa ena e ntlafalitse haholo phihlelo ea imeile bakeng sa basebelisi ka ho fokotsa palo ea melaetsa e sa batleheng e fihlang ho li-inbox tsa bona. 