Fundamentals Of Communication

Understand the communication process and identify the key elements of effective communication.
Recognize different types of communication, including verbal, nonverbal, written, and interpersonal communication.
Apply practical communication strategies to improve clarity, confidence, collaboration, and audience engagement.
Develop public speaking and professional communication skills for workplace and business environments.
Requirements
No previous communication training is required
Basic English comprehension skills
A willingness to practice communication and presentation techniques
Interest in improving professional and interpersonal communication skills
Description
This course contains the use of artificial intelligence.Effective communication is one of the most valuable professional and personal skills in today's world. Whether you are working in a team, speaking with clients, delivering presentations, leading meetings, or communicating in everyday situations, strong communication skills can improve confidence, collaboration, and overall success."Communication Essentials" is a practical and beginner-friendly course designed to help learners understand the foundations of effective communication and apply them in real-world situations. The course explores the communication process, different communication styles, practical communication strategies, and essential techniques for speaking clearly and confidently in professional environments.Throughout the course, learners will discover how communication works, how messages are transmitted and interpreted, and how to avoid common communication barriers. The course also introduces verbal, nonverbal, written, and interpersonal communication techniques that are essential in both personal and business contexts.Special attention is given to public speaking and presentation skills, including rhetorical techniques, audience engagement, speech preparation, and confidence-building strategies. Learners will also explore communication in workplace and business settings, helping them improve professionalism, collaboration, and clarity in professional interactions.The lessons are concise, engaging, and practical, making the course accessible even for learners with no prior communication training. By the end of the course, students will have a stronger understanding of communication principles and greater confidence in expressing ideas effectively in both professional and everyday situations.
Beginners who want to improve their communication skills,Students and graduates preparing for professional environments,Team members, supervisors, and professionals working in collaborative settings,Individuals looking to improve public speaking confidenceFreelancers, entrepreneurs, and customer-facing professionals,Anyone interested in becoming a clearer and more confident communicator

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MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 9.94 GB | Duration: 16h 14m
Master AWS SageMaker Algorithms (Linear Learner, XGBoost, PCA, Image Classification) & Learn SageMaker Studio & AutoML
What you'll learn
Train and deploy AI/ML models using AWS SageMaker
Optimize model parameters using hyperparameters optimization search.
Develop, train, test and deploy linear regression model to make predictions.
Deploy production level multi-polynomial regression model to predict store sales based on the given features.
Develop a deploy deep learning-based model to perform image classification.
Develop time series forecasting models to predict future product prices using DeepAR.
Develop and deploy sentiment analysis model using SageMaker.
Deploy trained NLP model and interact/make predictions using secure API.
Train and evaluate Object Detection model using SageMaker built-in algorithms.
Requirements
Basic knowledge of programming
Basic knowledge in AWS
Basic knowledge in machine learning
Description
# Update 22/04/2021 - Added a new case study on AWS SageMaker Autopilot. # Update 23/04/2021 - Updated code scripts and addressed Q&A bugs. Machine and deep learning are the hottest topics in tech! Diverse fields have adopted ML and DL techniques, from banking to healthcare, transportation to technology.AWS is one of the most widely used ML cloud computing platforms worldwide - several Fortune 500 companies depend on AWS for their business operations.SageMaker is a fully managed service within AWS that allows data scientists and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.In this course, students will learn how to create AI/ML models using AWS SageMaker. Projects will cover various topics from business, healthcare, and Tech. In this course, students will be able to master many topics in a practical way such as: (1) Data Engineering and Feature Engineering, (2) AI/ML Models selection, (3) Appropriate AWS SageMaker Algorithm selection to solve business problem, (4) AI/ML models building, training, and deployment, (5) Model optimization and Hyper-parameters tuning.The course covers many topics such as data engineering, AWS services and algorithms, and machine/deep learning basics in a practical wayata engineering: Data types, key python libraries (pandas, Numpy, scikit Learn, MatplotLib, and Seaborn), data distributions and feature engineering (imputation, binning, encoding, and normalization).AWS services and algorithms: Amazon SageMaker, Linear Learner (Regression/Classification), Amazon S3 Storage services, gradient boosted trees (XGBoost), image classification, principal component analysis (PCA), SageMaker Studio and AutoML.Machine and deep learning basics: Types of artificial neural networks (ANNs) such as feedforward ANNs, convolutional neural networks (CNNs), activation functions (sigmoid, RELU and hyperbolic tangent), machine learning training strategies (supervised/ unsupervised), gradient descent algorithm, learning rate, backpropagation, bias, variance, bias-variance trade-off, regularization (L1 and L2), overfitting, dropout, feature detectors, pooling, batch normalization, vanishing gradient problem, confusion matrix, precision, recall, F1-score, root mean squared error (RMSE), ensemble learning, decision trees, and random forest.We teach SageMaker's vast range of ML and DL tools with practice-led projects. Delve intoroject #1: Train, test and deploy simple regression model to predict employees' salary using AWS SageMaker Linear LearnerProject #2: Train, test and deploy a multiple linear regression machine learning model to predict medical insurance premium.Project #3: Train, test and deploy a model to predict retail store sales using XGboost regression and optimize model hyperparameters using SageMaker Hyperparameters tuning tool.Project #4: Perform Dimensionality reduction Using SageMaker built-in PCA algorithm and build a classifier model to predict cardiovascular disease using XGBoost Classification model.Project #5: Develop a traffic sign classifier model using Sagemaker and Tensorflow.Project #6: Deep Dive in AWS SageMaker Studio, AutoML, and model debugging.The course is targeted towards beginner developers and data scientists wanting to get fundamental understanding of AWS SageMaker and solve real world challenging problems. Basic knowledge of Machine Learning, python programming and AWS cloud is recommended. Here's a list of who is this course for:Beginners Data Science wanting to advance their careers and build their portfolio.Seasoned consultants wanting to transform businesses by leveraging AI/ML using SageMaker.Tech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience using AWS SageMaker.Enroll today and I look forward to seeing you inside.
AI practitioners,Aspiring data scientists,Tech enthusiasts,Data science consultants
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