The transition from school to college where one says goodbye to one part of life and enters into another is probably the most memorable in everyone’s life. However most of the students are confused about what career to follow next. In today’s parlance, they become bewildered to see the thousands of career options. Choosing a right career path becomes even more difficult if they don’t have clear idea about what do they want to do? Following below mentioned steps may make it easy and increases the chances of taking a good decision for one:
- Self-Assessment: One must attempt to learn about oneself in terms of interests, aptitude, values, soft skills, and personality type.
- Prepare a List of Career Options: Prepare a list of careers appealing to you based on your interests and those are a good fit for you. Also include the career options you know a bit about and those about which you don’t know much too.
- Explore the List of Career Options: Gather basic information about each of the career option in your list in the form of job opportunities in that domain and educational training required for the same. One should also look for higher study options in the same domain.
- Narrow Down the List of Options: Based on the information gained in the previous step, narrow down the list of options by eliminating the options you are not interested in pursuing.
- Gather Concrete Information: Meet people working/teaching the area you are interested in and gather concrete information of the option in which you are interested.
- Finalize your Career Option: Finalize the option now which you find most satisfactory for you and you wish to pursue as a career.
- Define Goals: Define your long as well as short term goals. Long term goals include completing the education required to pursue your choice as a career and short term goals could be applying to right university, training, internship, and apprenticeships.
- Develop an Action Plan: Develop an action plan laying down the steps to follow to achieve short term and in turn long turn goals. It should also incorporate the plan to avoid/resolve any hindrances in your way.
Now let me help you out in answering the following question:
How to Choose the Best Career Path: Machine Learning Engineer or Data Scientist
Now days, we are generating thousands of Terabytes of data per day, and a person who can handle this huge amount of data and offer business solutions is highly in demand. So, Machine Learning Engineer and Data Scientist are two such most sought after professions these days.
The competition between Machine Learning Engineer and a Data Scientist is increasing day by day and the line between them is diminishing. Traits like experience, and analytical skills required in them are very hard to find and hence qualified Machine Learning Engineers and Data Scientists are in high demand.
Introduction to Machine Learning
Alan Turing stated in 1947 that “What we want is a machine that can learn from experience.”
This was perhaps the time Machine Learning began. We use machine learning several times a day without even being aware of it.
You became wondered. How? Right!
Just think of YouTube recommendations and Facebook image recognition features. With YouTube as soon as you finish watching a tutorial or video on any topic you start receiving a recommendation to watch other videos on the same topic or genre .You might be wondering how does YouTube come to know about what to recommend you next? It really seems complex but YouTube analyses everything you watch and even keywords in the videos you watched previously. Based on this it recommends you. Isn’t it amazing, right?.
Similarly, you can be amazed by the tagging feature of Facebook. Suppose that you upload your vacation pictures with your friends on Facebook and it tags your friends in each and every picture very intelligently in no time . You may also consider about the Google Map which offers you so many features without revealing the complex logic behind it. Machine learning has so strongly been integrated into our day to day activities now that we don’t even become aware of its presence now and keep working. Machine Learning is actually a type of Artificial Intelligence itself.
As you can see from the figure above, Artificial Intelligence consists of Machine Learning which in turn consists of Deep Learning Artificial Intelligence makes use of Machine Learning in order to enable them to learn a task from previous experience without explicitly programming them. All we need to do is, feed machines with good quality data and train them by developing various learning models using data and learning algorithms. Choice of learning algorithms depends on the type of data in hand and the type of task which needs to be automated.
All this makes Machine Learning a most popular career opportunity amongst youngsters. According to Indeed, with a growth rate of 344%, Machine Learning Engineer was awarded best job of 2019 with an average base salary of $146,085 per annum. It can greatly optimize human efforts in enabling machines to learn from them and increases their performance. This results in many popular and well paid career options such as Natural Language Processing Scientist etc.
Introduction to Data Science
In Simple words, we can define Data science as the describing, predicting and drawing causal inference from data to help individuals and businesses in better decision making. Data can be structured as well as unstructured. Data Science also takes care of origination of data, representation and the process of transformation of data into a valuable resource. It also helps business houses in :
- Gaining a competitive advantage
- Identifying new market opportunities
- Increased efficiency
- Reduction in costs
To achieve these objectives, it makes use of computer science disciplines like mathematics and statistics. It also employs techniques like data mining, cluster analysis, visualization, and—yes—machine learning to some extent.
Who is a Machine Learning Engineer?
Sophisticated programmers whose aim is to develop machines that can learn on their own and apply knowledge without any specific direction are Machine Learning Engineers. They write programs to enable machines to act humanly without being directed specifically to perform certain tasks. They are also required to analyse data and design various machine learning algorithms that can run autonomously without human intervention .
Who is a Data Scientist?
A Data Scientist is a specialist professional who solves complex data problems in scientific disciplines with his/her expertise. As mentioned previously also, they conduct a statistical analysis to decide which machine learning algorithm to use. Then they model the algorithm and put it into testing. Businesses look towards a Data Scientist to gather, process, and derive valuable insights from the data in order to answer a question or solve a problem. They help companies in achieving sustainable growth by better understanding themselves and their customers.
Machine Learning Engineer vs. Data Scientist
We may consider the following parameters to answer the question of Machine Learning Engineer vs. Data Scientist:
• Salary Trends
• Job Trends
• Skills Requirements
• Must-Have Machine Learning Engineer Skills
• Must-Have Data Scientist Skills
• Roles and Responsibilities
Table below shows the average salary of a Data Scientist as well as of Machine Learning Engineer in India and US based on 4,575 and 1,542 salaries reported by people on job seeking site indeed.com.
|Machine Learning Engineer
|Entry Level – IND
|₹306,054 – ₹1,215,966
|₹405,650 – ₹2,105,761
|Entry Level – US
|$60,894 – $124,247
|$76,184 – $141,190
|Experienced – IND
|₹972,106 – ₹2,928,194
|₹1,112,565 – ₹4,024,375
|Experienced – US
|$79,321 – $167,947
|$95,254 – $235,640
Factors like the company and the location of the company also affect these figures however we have reported the average salary range for different levels of experience. So, if we compare the Salary Trends of Machine Learning Engineer and Data Scientist, we can see that in general, a Machine Learning Engineer earns a little more than a Data Scientist.
Table below shows the number of jobs reported in different three different locations of US and Bengaluru India in 2019.
|Data Scientist Job Trends
|No. of Jobs
|New York, NY
|San Francisco, CA
|Machine Learning Engineer Job Trends
|No. of Jobs
|New York, NY
|San Francisco, CA
We can see from the above tables that although there are more job openings for a Data Scientist than a Machine Learning Engineer, yet the later get slightly more paid than a Data Scientist. This is due to the fact that Machine Learning Engineers work on Artificial Intelligence which is relatively a new domain.
There are several skills which are required by both a Machine Learning Engineer and a Data Scientist. So let’s first look at the common skillsets:
- Programming Languages: Both of them are required to have a good understanding of programming languages like C++, Java, R, and preferably python as it is easy to learn and its applications are wider than any other language.
- Statistics: Both are required to know Statistics and should be familiar with Matrices, Vectors and Matrix Multiplication.
- Data Cleaning and Visualization: Data pre-processing in the form of cleansing and visualization is a valuable process. It helps you in saving time and increasing your efficiency by quickly identifying your findings. Data visualization can have a make-or-break effect when it comes to the impact of your data.
- Machine Learning and Neural Network Architectures: Machine Learning and predictive modelling are the two buzzwords today. Both professionals are supposed to understand machine learning techniques like supervised and unsupervised machine learning, decision trees, and logistic regression etc. It will help them solve different analytical problems based on predicting major organizational outcomes.Inspired by biological neurons (Brain Cells), Deep Learning takes traditional Machine Learning approaches to next level. It attempts to mimic the human brain. A large network of such Artificial Neurons is known as Deep Neural Network.
- Big Data Processing Frameworks: Huge amount of data is being generated these days called Big Data. We, therefore, require frameworks like Spark and Hadoop to handle this Big Data. Big Data Analytics is a must-have skill for both professionals to gain hidden business insights.
- Industry Knowledge: Successful projects address the real business issues. No matter which industry one work for, one should be aware of the working of that industry and what is in interest of business. Both the Machine Learning Engineer and Data Scientist are therefore required to have business acumen so that all their technical skills can be channelled productively to make up a successful business model.
Must-Have Skills for a Machine Learning Engineer
- Language, Audio and Video Processing: Machine Learning Engineers are supposed to have a good control over libraries like Gensim, NLTK, sentimental analysis, and summarization. This is required as Natural Language Processing combines Computer Science and Linguistics and need to work with text and audio/video.
- Applied Mathematics: A Machine Learning Engineer is required having a firm understanding of algorithmic theory and concepts like Convex Optimizations, Gradient Descent, Partial differentiation, and Quadratic Programming.
- Signal Processing Techniques: A Machine Learning Engineer is also required to have an understanding of Signal Processing. It helps to solve different complex problems as feature extraction, time-frequency analysis and wavelets- which are integral to signal processing are important parts of Machine Learning.
- Software Development: Being inherent software developers, Machine Learning Engineers need to have a sound understanding of software engineering principles and concepts like Data Structures, Memory Management, and how to package software.
Must-Have Skills for a Data Scientist
- Creative and Critical Thinking: There is a saying that smart people ask hard questions while really smart people ask simple ones. Data Scientists must be able to play with numbers. They must look at numbers, trends, and data to draw new conclusions.
- Effective Communication: A Data Scientist need to have effective communication skills as they work with peoples from different segments including laymen, marketing and sales person to a team of engineers. They should be able to convey their findings effectively to people with little or no expertise.
- Roles and Responsibilities: Now let’s see the challenges faced by them and the roles and responsibilities borne by them:
Roles and Responsibilities of a Machine Learning Engineer:
- Study and transformation of various Data science prototypes
- Design and develop new Machine Learning Systems
- Carry out Research on new Machine Learning algorithms and tools
- Develop new machine learning applications to fulfil requirements
- Selection of appropriate Datasets and Data Representation Methods
- Run Machine Learning Tests and Experiments
- Perform Statistical analysis and Fine-Tuning of parameters using Test Results
- Train and Retrain Systems as and when necessary
- Extending existing ML Libraries and Frameworks
- Keep abreast of Developments in the Field of Artificial Intelligence
Roles and Responsibilities of a Data Scientist:
- Selecting features, Building and Optimizing Classifiers using Machine Learning Techniques
- Understand the customer’s business needs and guide them to a solution
- Data mining using state-of-the-art methods
- Processing, cleansing, and verifying the integrity of data used for analysis
- Perform Market Research
- Obtain Data and Recognize its Strength
- Use Deep Learning frameworks like MXNet, Tensorflow, Theano and Keras to build Deep Learning models
- Pinpoint Trends, Correlations, and Patterns in complicated data sets
- Identify new opportunities for process improvement
- Work with Professional Services like DevOps consultants to help customers operationalize models after they are built
No matter you work as a Machine Learning Engineer or as a Data Scientist, you are going to work with cutting edge technologies to offer simple solutions to complex business problems. Young bright minds with requisite skills will be highly in demand in this space for years to come as demand for such talent far outpaces supply. So whatever path you choose, you are always on the right path.
Mabadiliko kutoka shule hadi chuo ambapo mtu anasema kwaheri kwa sehemu moja ya maisha na kuingia katika nyingine ni pengine zaidi ya kukumbukwa katika maisha ya kila mtu. Hata hivyo wengi wa wanafunzi ni kuchanganyikiwa kuhusu nini kazi ya kufuata ijayo. Katika usemi wa leo, wao huwa na mshangao kuona maelfu ya chaguzi za kazi. Kuchagua njia sahihi ya kazi inakuwa ngumu zaidi ikiwa hawana wazo wazi juu ya kile wanataka kufanya? Hatua zifuatazo zinaweza kufanya iwe rahisi na kuongeza nafasi ya kuchukua uamuzi mzuri kwa mtu: - Kujitathmini: Mtu lazima ajaribu kujifunza juu yake mwenyewe katika suala la maslahi, uwezo, maadili, ujuzi laini, na aina ya utu. - Kuandaa orodha ya kazi chaguzi: Kuandaa orodha ya kazi ya kuvutia kwa wewe kulingana na maslahi yako na wale ni fit nzuri kwa ajili yenu. Pia ni pamoja na chaguzi za kazi unajua kidogo kuhusu na wale kuhusu ambayo hujui mengi pia. - Kuchunguza orodha ya chaguzi za kazi: Kukusanya habari ya msingi kuhusu kila moja ya chaguo la kazi katika orodha yako katika fomu ya fursa za kazi katika uwanja huo na mafunzo ya elimu inahitajika kwa ajili ya sawa. Mtu anapaswa pia kuangalia kwa chaguzi za masomo ya juu katika uwanja huo huo. - Kupunguza orodha ya chaguzi: Kulingana na habari iliyopatikana katika hatua ya awali, kupunguza orodha ya chaguzi kwa kuondoa chaguzi wewe si nia ya kufuatilia. - Kukusanya habari halisi: Kukutana na watu wanaofanya kazi / kufundisha eneo unalopendezwa na kukusanya habari halisi ya chaguo ambalo unavutiwa nalo. - Finalize chaguo lako la kazi: Finalize chaguo sasa ambayo wewe kupata kuridhisha zaidi kwa ajili yenu na unataka kufuatilia kama kazi. - Kufafanua malengo: Kufafanua malengo yako ya muda mrefu na pia ya muda mfupi. Lengo la muda mrefu ni kukamilisha elimu inayohitajika ili kufuata chaguo lako kama kazi na lengo la muda mfupi linaweza kuwa kuomba chuo kikuu sahihi, mafunzo, mafunzo, na mafunzo. - Kuendeleza mpango wa hatua: Kuendeleza mpango wa hatua kuweka hatua ya kufuata kufikia muda mfupi na kwa upande mwingine muda mrefu malengo. Inapaswa pia kujumuisha mpango wa kuepuka au kutatua vizuizi vyovyote katika njia yako. "Kama unavyojua, kuna aina ya kazi inayoitwa ""Machine Learning Engineer"" au ""Data Scientist"" ambayo inatoa maelfu ya terabytes ya data kwa siku, na mtu anayeweza kushughulikia kiasi hiki kikubwa cha data na kutoa suluhisho za biashara ni mahitaji makubwa." Kwa hiyo, Mhandisi wa Kujifunza Mashine na Mwanasayansi wa Takwimu ni kazi mbili zinazotafutwa zaidi siku hizi. Mashindano kati ya Mhandisi wa Mafunzo ya Mashine na Mwanasayansi wa Takwimu yanaongezeka siku kwa siku na mstari kati yao unapungua. Sifa kama uzoefu na ujuzi wa uchambuzi unaohitajika ni ngumu sana kupata, na kwa hivyo wahandisi wa kujifunza mashine na wanasayansi wa data wana mahitaji makubwa. "Kama Alan Turing alivyosema mwaka 1947: ""Tunataka mashine inayoweza kujifunza kutokana na uzoefu.""" Tunatumia kujifunza mashine mara kadhaa kwa siku bila hata kuwa na ufahamu wa hilo. Ulikuwa na mshangao. Kwa njia gani? Ni kweli! Fikiria tu mapendekezo ya YouTube na vipengele vya utambuzi wa picha za Facebook. "Kama wewe ni kuangalia video ya YouTube, unaweza kupata mapendekezo ya kuangalia video nyingine juu ya mada hiyo au aina ya video, ""Kama wewe ni kuangalia video ya YouTube, unaweza kupata mapendekezo ya kuangalia video nyingine juu ya mada hiyo au aina ya video." YouTube inaonekana kuwa ngumu sana, lakini inachambua kila kitu unachotazama na hata maneno muhimu katika video ambazo umeangalia hapo awali. Kwa kuzingatia hili inakupendekeza. Je, si ajabu, haki? Vivyo hivyo, unaweza kushangazwa na kipengele cha kuweka alama kwenye Facebook. Ikiwa unaweka picha za likizo na marafiki zako kwenye Facebook, Facebook itaweka alama kwa kila picha kwa njia ya akili. Unaweza pia kufikiria juu ya Ramani ya Google ambayo inakupa vipengele vingi bila kufunua mantiki ngumu nyuma yake. Mafunzo ya mashine yameunganishwa sana katika shughuli zetu za kila siku sasa hivi kwamba hata hatujui uwepo wake sasa na kuendelea kufanya kazi. Mafunzo ya mashine ni kweli aina ya akili bandia yenyewe. Kama unavyoona kwenye picha hapo juu, akili bandia inajumuisha kujifunza mashine ambayo kwa upande wake inajumuisha kujifunza kina akili bandia hutumia kujifunza mashine ili kuwawezesha kujifunza kazi kutoka kwa uzoefu wa awali bila kuwa programu wazi. Tunachohitaji kufanya ni kulisha mashine na data bora na kuwafundisha kwa kuendeleza mifano mbalimbali ya kujifunza kwa kutumia data na algorithms za kujifunza. Uchaguzi wa kujifunza algorithms inategemea aina ya data katika mkono na aina ya kazi ambayo inahitaji kuwa automatiska. Yote haya hufanya Machine Learning fursa maarufu zaidi ya kazi miongoni mwa vijana. Kwa mujibu wa ripoti ya Indeed, na kiwango cha ukuaji wa 344%, Mhandisi wa Mafunzo ya Mashine alipewa kazi bora ya 2019 na mshahara wa msingi wa wastani wa $ 146,085 kwa mwaka. Inaweza kuboresha sana juhudi za binadamu katika kuwezesha mashine kujifunza kutoka kwao na kuongeza utendaji wao. Hii husababisha chaguzi nyingi maarufu na vizuri kulipwa kazi kama vile Asayansi wa Usindikaji wa Lugha ya Asili nk. "Kutoka kwa ""Sayansi ya Takwimu"" (DS) tunaweza kufafanua sayansi ya data kama ""kuelezea, kutabiri na kuchora inference ya sababu kutoka kwa data kusaidia watu binafsi na biashara katika kufanya maamuzi bora.""" Takwimu zinaweza kuwa na muundo na pia zisizo na muundo. Sayansi ya data pia inahakikisha asili ya data, uwakilishi na mchakato wa mabadiliko ya data katika rasilimali yenye thamani. Pia husaidia nyumba za biashara katika: - Kupata faida ya ushindani - Kutambua fursa mpya za soko - Kuongeza ufanisi - Kupunguza gharama Ili kufikia malengo haya, hutumia taaluma za sayansi ya kompyuta kama hesabu na takwimu. Pia hutumia mbinu kama uchimbaji wa data, uchambuzi wa kundi, taswira, na - ndiyo - kujifunza mashine kwa kiwango fulani. Ni nani Mhandisi wa Kujifunza Mashine? Programu za kisasa ambazo lengo lao ni kuendeleza mashine ambazo zinaweza kujifunza peke yao na kutumia maarifa bila mwelekeo wowote maalum ni Wahandisi wa Kujifunza Mashine. Wao huandika programu ili kuwezesha mashine kutenda kama wanadamu bila kuelekezwa hasa kufanya kazi fulani. Pia wanahitajika kuchambua data na kubuni algorithms mbalimbali za kujifunza mashine ambazo zinaweza kuendesha kwa kujitegemea bila uingiliaji wa binadamu. Mwanasayansi wa Takwimu ni nani? Data Scientist ni mtaalamu wa kitaalamu ambaye hutatua matatizo magumu ya data katika taaluma za kisayansi na utaalam wake. Kama ilivyoelezwa hapo awali pia, wao kufanya uchambuzi wa takwimu kuamua ambayo mashine kujifunza algorithm kutumia. Kisha wao mfano algorithm na kuweka katika kupima. Biashara huangalia kwa Mtaalamu wa Takwimu kukusanya, kusindika, na kupata ufahamu muhimu kutoka kwa data ili kujibu swali au kutatua tatizo. Wao kusaidia makampuni katika kufikia ukuaji endelevu kwa kuelewa vizuri wenyewe na wateja wao. Mashine Learning Mhandisi vs Data Scientist: Mahitaji ya ujuzi na mahitaji ya mafunzo ya mashine na ujuzi wa data: Mfano wa mashine ya kujifunza mhandisi ni: Mashine ya kujifunza mhandisi vs Data Scientist: Mahitaji ya ujuzi na mahitaji ya mafunzo ya mashine na ujuzi wa mashine na ujuzi wa mashine. Mhandisi wa Ujifunzaji wa Mashine: Kiwango cha kuingia: $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.459.99 $ 1.450.00 Kwa hivyo, ikiwa tunalinganisha Mwelekeo wa Mshahara wa Mhandisi wa Kujifunza Mashine na Mwanasayansi wa Takwimu, tunaweza kuona kwamba kwa ujumla, Mhandisi wa Kujifunza Mashine anapata kidogo zaidi kuliko Mwanasayansi wa Takwimu. Orodha ya chini inaonyesha idadi ya kazi zilizoripotiwa katika maeneo matatu tofauti ya Marekani na Bengaluru India mnamo 2019. Data Scientist Job Trends - Kazi ya Sayansi ya Takwimu Kazi ya New York, NY, San Francisco, CA, Mhandisi wa Mafunzo ya Mashine """Kama tunavyoona katika meza zilizotajwa hapo juu, ingawa kuna nafasi zaidi za kazi kwa Mwanasayansi wa Takwimu kuliko Mhandisi wa Kujifunza Mashine, bado wa mwisho hupata malipo zaidi kuliko Mwanasayansi wa Takwimu." Hii ni kwa sababu ya ukweli kwamba Wahandisi wa Kujifunza Mashine hufanya kazi kwenye Akili ya bandia ambayo ni uwanja mpya. Kuna ujuzi kadhaa ambayo inahitajika na wote Machine Learning Mhandisi na Data Scientist. Kwa hiyo hebu kwanza kuangalia skillets kawaida: - Lugha za Programu: Wote wawili wanahitajika kuwa na uelewa mzuri wa lugha za programu kama C ++, Java, R, na bora python kama ni rahisi kujifunza na maombi yake ni pana kuliko lugha nyingine yoyote. - Takwimu: Wote wanatakiwa kujua Takwimu na wanapaswa kuwa na ufahamu wa Matrices, Vectors na Matrix Multiplication. - Data Usafi na Visualization: Data pre-processing katika fomu ya kusafisha na visualization ni mchakato wa thamani. Inakusaidia kuokoa muda na kuongeza ufanisi wako kwa kutambua haraka matokeo yako. Ugunduzi wa data unaweza kuwa na athari ya kufanya au kuvunja linapokuja suala la athari ya data yako. - Machine Learning na Neural Network Architectures: Machine Learning na predictive modeling ni maneno mawili buzzword leo. Wataalamu wote wawili wanatakiwa kuelewa mbinu za kujifunza mashine kama kujifunza mashine iliyoongozwa na isiyoongozwa, miti ya uamuzi, na kurudi nyuma kwa vifaa nk. Kujifunza kwa kina huchukua mbinu za kawaida za kujifunza mashine kwa kiwango cha pili. Inajaribu kuiga ubongo wa binadamu. Mtandao mkubwa wa Neurons za bandia kama hizo unajulikana kama Mtandao wa Neural wa kina. - Big Data Processing Frameworks: Kiasi kikubwa cha data ni zinazozalishwa siku hizi aitwaye Big Data. Kwa hiyo, tunahitaji mifumo kama Spark na Hadoop kushughulikia hii Big Data. Uchambuzi wa Takwimu Kubwa ni ujuzi unaopaswa kuwa nao kwa wataalamu wote wawili kupata ufahamu wa biashara uliofichwa. - Ujuzi wa sekta: Miradi ya mafanikio kushughulikia masuala halisi ya biashara. Haijalishi ni sekta gani ya biashara, mtu anapaswa kujua jinsi sekta hiyo inavyofanya kazi na ni nini kinachohusu biashara. Kwa hiyo, wote wawili, Mhandisi wa Mafunzo ya Mashine na Mwanasayansi wa Takwimu, wanahitaji kuwa na akili ya biashara ili ujuzi wao wote wa kiufundi uweze kuelekezwa kwa uzalishaji ili kuunda mfano wa biashara uliofanikiwa. "Kama ""Machine Learning"" Engineer, lazima uwe na ujuzi wa lugha, sauti na video: ""Machine Learning"" Mhandisi wanapaswa kuwa na udhibiti mzuri juu ya maktaba kama vile Gensim, NLTK, uchambuzi wa hisia, na muhtasari." Hii inahitajika kama Natural Language Processing inachanganya Sayansi ya Kompyuta na Linguistics na haja ya kufanya kazi na maandishi na sauti / video. - Matematiksi ya Matumizi: Mhandisi wa Kujifunza Mashine inahitajika kuwa na uelewa thabiti wa nadharia ya algorithmic na dhana kama Convex Optimizations, Gradient Descent, Partial differentiation, na Quadratic Programming. - Signal Processing Techniques: Mhandisi wa Mafunzo ya Mashine pia inahitajika kuwa na uelewa wa Signal Processing. Inasaidia kutatua matatizo mbalimbali tata kama vile kipengele uchimbaji, muda-frequency uchambuzi na wavelets- ambayo ni muhimu kwa usindikaji ishara ni sehemu muhimu ya Machine Learning. - Maendeleo ya Programu: Kuwa watengenezaji wa programu ya asili, Wahandisi wa Kujifunza Mashine wanahitaji kuwa na uelewa mzuri wa kanuni na dhana za uhandisi wa programu kama muundo wa data, Usimamizi wa kumbukumbu, na jinsi ya kufunga programu. "Kama unavyojua, ""Uwezo wa Sayansi ya Takwimu"" ni ""kufikiria kwa ubunifu na muhimu"" - kwa hivyo, watu wenye akili wanauliza maswali magumu, wakati watu wenye akili wanauliza maswali rahisi." Wanasayansi wa data lazima wawe na uwezo wa kucheza na namba. Ni lazima wachunguze idadi, mwelekeo, na habari ili kupata matokeo mapya. - Mawasiliano ya ufanisi: Mwanasayansi wa data anahitaji kuwa na ujuzi wa mawasiliano ya ufanisi wakati wanafanya kazi na watu kutoka sehemu tofauti ikiwa ni pamoja na laymen, masoko na mtu wa mauzo kwa timu ya wahandisi. Wanapaswa kuwa na uwezo wa kufikisha matokeo yao kwa ufanisi kwa watu wenye ujuzi mdogo au bila ujuzi. Kazi na majukumu: Sasa hebu tuone changamoto zinazowakabili na majukumu na majukumu yanayobeba: Kazi na majukumu ya Mhandisi wa Kujifunza Mashine: - Utafiti na mabadiliko ya mifano mbalimbali ya Sayansi ya Takwimu - Kubuni na kuendeleza mifumo mpya ya Kujifunza Mashine - Kufanya utafiti juu ya mahitaji ya kujifunza mashine mpya - Kuchagua seti sahihi za data na mbinu za uwakilishi wa data - Kuhifadhi vipimo vya mashine - Kufanya uchunguzi wa takwimu na kurekebisha matokeo ya majaribio - Kufundisha na kurekebisha mifumo kama inavyotakiwa - Kupata maktaba na mifumo ya data - Kuimarisha na kuimarisha data. Akili za vijana wenye ujuzi wa kutosha zitakuwa na mahitaji makubwa katika nafasi hii kwa miaka ijayo kama mahitaji ya vipaji kama hivyo yanazidi ugavi. Kwa hiyo, njia yoyote unayochagua, daima uko kwenye njia sahihi.