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INSIDE THE SERIES




                    The key features of the series have been designed to ensure better
                                           learning and assessment.      S teps f or  Ef f ective T ime Manag ement
                                                                         Following steps help in effective time management:
                                            Learning Resources                            1. Set goals
                                                                                          correctly
                                           UNIT-1                                   7. Plan ahead  2. Prioritize
                                                                                                 wisely
                         COMMUNICATION SKILLS-I
        Learning Outcomes:                                                          6.   Time Management
                                                                                   Remove non   Tips
                                                                                                 3. Set a
        It provides an overview of the                                             essential     time limit  At a Glance:
                                                                                    tasks
        unit/chapter contents.                                                      This section provides a summary
                                                                                             4. Take
                                                                                      5. Organize
                                                                                       yourself  breaks
                                                                                            between   of the chapter.
                                                                                             tasks
                Learning Outcomes
           •  What is Communication?   •  Perspectives in Communication      At a Glance
           •  Factors Affecting Perspectives in Communication   •  Effective ways of Communication
           •  Types of Communication   •  3P’s of Public Speaking           •  Analysing your strengths and weaknesses is helpful.
           •  Writing Skills     •  Phrases                                 •  An analysis of strengths and  eaknesses begins  ith kno ing and understanding yourself first.
           •  What is a Sentence?   •  Construction of a Paragraph         Values are the principles or standards of action; your own judgment about what is important in your life.
           •  Parts of Speech    •  Use of Articles                         •
           •  When no Articles are used                                     •  Knowing ‘yourself’ means you understand who you are, what you like or do not like, what your beliefs or
                                                                           opinions are, what is your background, what you are good or bad at.
          Communication is an age old method to convey any information effectively. In this unit, we will discuss different     •  Strength or ability is what you do well and are good at.
          ways of communication which is very useful for a student to learn. If a person has good communication skills, then,     •  Weakness, also known as "area for improvement" is what you don't do well and what you're not good at.
          it becomes easy to convey a message in short and simple sentences. This develops confidence and helps a person     •  Grooming is the process of giving oneself a neat, orderly and clean appearance.
          survive in the vast ocean of Information Technology (IT).         •   he  ay you dress and groom can send a message that you are a confident and smart person.
                                                                            •  Personal hygiene is the habit or practice of keeping clean.
             What is Communication?                                         •  Cleanliness helps us maintain our health and spirit.
                                                                            •  A team is a group of people who work together to achieve a common goal.
          The word ‘communication’ is derived from the Latin word communicare, meaning “to share”. It is defined as a way of
        AI Glossary:                                                        •  Networking means getting to know people, staying in touch over time and using knowledge or skills to
          conveying a meaningful message from one entity to another in the form of signs, symbols, behaviour using verbal
                                                                           help each other.
          and non-verbal skills. It is important that whatever we want to communicate is conveyed effectively.     •  Self-motivation is what drives us to achieve our goals, makes us feel happy and improve the quality of
        This section contains definitions                                  our lives.
          Elements of  C ommunication
          Communication is the process of transmission
        of important AI terms.   Feedback  Sender                           •   •  Goals are a set of dreams with an achievable deadline.
                                                                            oal setting is finding and listing your goals and then planning ho  to achieve them.
          of an appropriate message from a sender to
          a receiver through a transmission channel in                      •  Time management is the ability to plan and control how you spend your time well and do whatever you
          a proper format. The communication process   Decoding  Ideas/Message  want.
          helps in sharing  of  a  common meaning
                                   Elements of
          between the sender and the receiver. Let us   Communication                            S e lf - M an ag e m e n t  S k i lls- III  4 7
          study all these elements in detail.
            • Sender: Can be any  person, group or  an
          organisation that initiates  the process  of   Receiver  Encoding
          communication. The sender’s knowledge,
         F   Chatbot: It is an AI application   Communication
          experiences and skills influence the quality  that can mimic a real conversation  ith a user in their natural
             language.
          of the message.           Channel                                                        Video Session:
                                    C om m uni cati on S k i l l s- I
                                              19
         F   Natural Language Processing (NLP): It is an area of artificial intelligence that employs natural   This section contains a link of the
             language for interaction bet een computers and humans.
                                                                                 video related to the topic for better
         F   Sentiment  Analysis:  It  is  also  called  Opinion   ining  or   motion  AI,  uses       to  determine   Brainy Fact
              hether data is positive, negative, or neutral.            Contrary to popular belief, one of the early adopters of ML is Israel (63%) followed by Netherlands (57%)
                                                                                      understanding of the concept.
                                                                        and then United States (56%). (Business Broadway Survey, February 2021)
         F   Cognitive Computing: It refers to individual technologies that perform specific tasks to enable
             human intelligence.
                                                                      Evaluation
         F   Deep Learning: It is a subset of machine learning based on neural net orks that permit a machine   Once a model has been created and trained, it must be properly tested to calculate the model's efficiency and
             to train itself to perform a task.                       performance. As a result, the model is evaluated using Testing Data (which was extracted from the acquired dataset
                                                                      during the Data Acquisition stage) and the model's efficiency is assessed.
         F   Machine Learning: It is a subset of AI that includes techniques that enable machines to improve   The set of measurements will differ depending on the problem you're working on. For regression problems, for
             at performing tasks  ith e perience.                     example, MSE or MAE are commonly used. On the other hand, for a balanced dataset, accuracy may be a useful
                                                                      choice for evaluating a classification model. Imbalanced sets necessitate the use of more advanced metrics. In such
             S
         F   tructured Data: It is the type of data  hich  e interact and  ork  ith every day.  instances, the F1 score is useful.
                                                                      A separate validation dataset is used for evaluation during training. It monitors how well our model generalises,
         F   Unstructured Data: It is the type of data  hich neither possesses any fi ed data type nor the si e   avoiding bias and overfitting.
             is fi ed.
        AI Innovators:                                                There are a few other things considered during this stage too:
                                                                        The volume of test data can be huge, which provides data complexities.
                                                                      •
             R
         F   elational Algebra: It is a set of algebraic operators and rules used to manipulate relational
        It presents information about the                             •  Human biases in picking test data might have a negative impact on the testing phase; thus, data validation is
             tables to generate the required information.
                                                                        critical.
        pioneers in the field of AI.                                  •  The  testing  team should put  the  AI and  ML  algorithms through  rigorous testing while maintaining model
         F   AI Bias: It is a phenomenon  hich occurs  hen algorithm results are systematically biased against
             a certain gender, language, race,  ealth, etc.             validity and keeping successful learnability, and algorithm efficacy in mind.
                                                                                                      Brainy Fact:
                                                                      •   As the system may deal with sensitive data, regulatory compliance and security testing are essential.
         F   Conflict: It can be defined as a clash bet een t o opposing forces that creates the narrative   •   Also, due to the sheer volume of data, performance testing is critical.
                                                                               It presents an interesting fact relevant
             thread for a story.                                      •   If the AI solution requires data from other systems, systems integration testing is critical.
                                                                      •  All relevant subsets of training data, i.e., the data you will use to train the AI system, should be included in test
                                                                                        to the topic of the chapters.
         F   Design Thinking: It is a process to solve problems creatively by putting consumers' needs first.  data.
                                                                      •   The team involved in testing must develop test suites to aid in the validation of the ML models.
                Gazal S. Kalra
         F   egression: It is a  achine  earning algorithm used to analyse the relationship among dependent
             R
              target) and independent  predictor) variables.
                In   urgaon,   ivigo   ervices   vt   td,  a  logistics
                business,   a al   .   alra  is  one  of  the  company's
         F   Correlation:  It  is  a  statistical  method  that  indicates   hether  a  pair  of  variables  has  a  linear   Brainy Fact
                co founders.  In  addition  to  being  an  II  Delhi
             relationship and  ill change together.                    Training Dataset vs Test Dataset vs Validation Dataset
                alumnus,  a al has an   A from  tanford  niversity     The training dataset is the set of data that was utilised to fit the model.
         F   Clustering: It is an unsupervised machine learning technique that automatically divides the data
                 raduate  chool of  usiness and a  aster of  ublic
             into clusters or groups of similar elements.              Validation Dataset: A subset of data used to offer an unbiased evaluation of a model's fit on the training
                Administration  from   arvard   ennedy   chool  of
                                                                       dataset while tuning model hyperparameters. As proficiency of the validation dataset is incorporated into
                 overnment.               AI Glossary                  the model setup, the evaluation becomes increasingly biased.
                 ivigo  as established by  a al in      because she   327  The sample of data used to offer an unbiased evaluation of a final model fit on the training dataset is referred
                                       Gazal S. Kalra                  to as the test dataset. The test data set is sometimes known as a holdout data set if the data in it has never
                en oys creating meaningful, inventive, and scalable    been used in training (for example, in cross-validation).
                enterprises.  he startup  ants to revolutionise the logistics industry by offering its customers
                dependable, precise delivery timeframes.
                 very pilot and truck driver received ample time to spend  ith their families thanks to the   Phase III: Deployment and Maintenance
                service model designed by  a al and her team.  hey can also live a life of respect and dignity.  Phase III is divided into two stages: Deployment and Feedback. Let us discuss about them in detail.
                                                                                                   Model Lifecycle  133
                                Andrej Karpathy
                                 enior Director of Artificial Intelligence at  esla,
                                Andre    arpathy  leads  the  team   orking
                                on  neural  net orks  of  Autopilot  in   esla s
                                cars.   e   orked  previously  at  OpenAI  as
                                a  research  scientist  on  Deep   earning  in
                                 omputer vision,  einforcement  earning and
                                 enerative   odeling.  Andre    orked   ith
                                 ei  ei   i  for  his   h.D  at   tanford,   here  he
                                 orked  on   onvolutional  ecurrent   eural
                     Andrej Karpathy   et ork  architectures  and  their  applications
                                in  atural  anguage  rocessing and  omputer
                  ision and their intersection.  e also interned at  oogle  orking on large scale feature
                 learning over  ou ube videos.
            240  Touchpad Artificial Intelligence (Ver. 2.0)-XII  AI Innovators  240
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