Instructional Design Pro (Part 2): No Beginners Allowed!

Woooow! Are you at university and you don't understand what your statistics teacher is talking about? Here I will explain everything in an easy way :) Just day to day examples, and you will learn that statistics is not hard at all ;) A lot of my students who first did not pass statistics get not only a pass but a really high grade :) Of course this will not be a completely tailored course for you, (every statistics subject is different), like my private lessons, but I will structure it for you in such a way that you only have to learn the subjects which are relevant for you! This will mean that you can skip a lot of lessons. But hey, perhaps you will become a statisitics geek, and you can finish all the lessons (hahahaha, don't think so!) I guess to finish one statistics subject on average you will need around 40 hours, but for some people it can be much more or much less. In this course you will find: all the descriptive statistics which you need to knowBasic math skills which you need to perform the statistical calculationsCalculate correlation coefficient and a regression slope by handNext to the calculations I explain you fully what all topics mean and how to interpret them :)Most important I explain it in an easy going way.A lot of clear examples and hand on exercises to master your new skills. In the next course I will explain everything about the Normal Distribution (and explain also other distributions) (Coming soon!) And afterwards all inferential statistics (hypothesis testing) (coming soon!) And in my last course (Statistics Explained Easy - SPSS) (Already available), I explain completely to you how SPSS works! (Statistical package which will do all the calculation for you ;) )

Content of Soil Mechanics Part (1) :1- lateral earth pressure and retaining structures2- Bearing Capacity of Soil3- Soil Settlement4- Soil Stress5- Newmark's Chart Using AutoCADContent of Soil Mechanics Part (1) :1- lateral earth pressure and retaining structures2- Bearing Capacity of Soil3- Soil Settlement4- Soil Stress5- Newmark's Chart Using AutoCAD Content of Soil Mechanics Part (1) :1- lateral earth pressure and retaining structures2- Bearing Capacity of Soil3- Soil Settlement4- Soil Stress5- Newmark's Chart Using AutoCAD Content of Soil Mechanics Part (1) :1- lateral earth pressure and retaining structures2- Bearing Capacity of Soil3- Soil Settlement4- Soil Stress5- Newmark's Chart Using AutoCADContent of Soil Mechanics Part (1) :1- lateral earth pressure and retaining structures2- Bearing Capacity of Soil3- Soil Settlement4- Soil Stress5- Newmark's Chart Using AutoCAD Content of Soil Mechanics Part (1) :1- lateral earth pressure and retaining structures2- Bearing Capacity of Soil3- Soil Settlement4- Soil Stress5- Newmark's Chart Using AutoCAD Content of Soil Mechanics Part (1) :1- lateral earth pressure and retaining structures2- Bearing Capacity of Soil3- Soil Settlement4- Soil Stress5- Newmark's Chart Using AutoCADContent of Soil Mechanics Part (1) :1- lateral earth pressure and retaining structures2- Bearing Capacity of Soil3- Soil Settlement4- Soil Stress5- Newmark's Chart Using AutoCAD Content of Soil Mechanics Part (1) :1- lateral earth pressure and retaining structures2- Bearing Capacity of Soil3- Soil Settlement4- Soil Stress5- Newmark's Chart Using AutoCAD Content of Soil Mechanics Part (1) :1- lateral ea

Course Description:Embark on an enlightening journey through the cosmos with our comprehensive Udemy course, "Astronomy Research Data Analysis with Python." This course is designed for astronomy enthusiasts, students, and researchers keen on mastering Python for analyzing astronomical data. With a focus on practical skills and real-world applications, this course simplifies complex concepts, making it accessible to learners with basic programming knowledge.What You'll Learn:Module 1: Starting with Python Dive into Python programming, beginning with the basics. Understand Google Colab, variables, data types, and control flow. Learn about f-strings, user inputs, and functions. This foundation is crucial for handling astronomical data efficiently.Module 2: Tabular Data Visualization Explore the world of tabular data with Pandas, Matplotlib, and Seaborn. Learn how to import libraries, analyze star color data, detect outliers, and create line plots and HR diagrams. You'll gain the ability to visualize and understand complex astronomical datasets.Module 3: Image Data Visualization Uncover the secrets of astronomical image data. Learn about FITS files, and use Python to visualize galaxies like M31. Understand image processing techniques like MinMax and ZScaleInterval scaling, enhancing your ability to interpret celestial images.Module 4: Image Processing | Apply Filters and Extracting Features Delve deeper into image processing. Learn about convolution operations, Gaussian kernels, and feature enhancement. Discover techniques for identifying and extracting features from astronomical images, a skill vital for research and analysis.Feedback, Conclusion, Further Steps Wrap up your learning experience with feedback sessions, a course conclusion, and guidance for future learning paths in astronomy and data analysi

AI Mastery - Learn Python, ML, NLP, Python and OpenCV Techniques From Basics to Advanced ConceptsArtificial Intelligence (AI), and Data Science offer abundant career opportunities in India and globally, particularly in fields such as image processing, pattern analysis, marketing, and data analysis. Our AI Masterclass offers an immersive journey into the dynamic world of Data Science and Artificial Intelligence (AI). Designed for aspiring professionals and enthusiasts alike, this comprehensive program equips participants with the essential skills and knowledge needed to excel in the rapidly evolving field of AI.Our AI Course is designed to equip participants with comprehensive technical training in Machine Learning concepts and algorithms. Key Technologies and Tools Covered:Python for AIPython BasicsPython FundamentalsData Structures- Lists, tuples- dictionaries- setsPython Programming FundamentalsPandas for Data ScienceMachine Learning LibrariesNumPy LibrariesData Analytics with Python - Matplotlib - SeabornPandas Libraries for Machine LearningMachine Learning Libraries and their applicationsMachine LearningMachine Learning Concepts and ApplicationsTraining and Evaluating ModelsProjects:- Employee Salary Prediction- Movie Recommendation System- Breast Cancer ClassificationNatural Language Processing (NLP)NLP Fundamentals and TechniquesProjects: - Hate Speech Detection - Speech Recognition and Emotion - AI Virtual Assistant with Python

DO YOU WANT TO LEARN FROM BASICS ?DO YOU WANT TO LEARN MORE NUMERICALS ?DO YOU WANT TO ANALYZE CONCEPTS WITH BEAUTIFUL EXAMPLE PROBLEMS ?DO YOU WANT TO CHECK HOW MUCH CONTENT YOU HAVE GRASPED AFTER EACH LECTURE ?DO YOU WANT TO PRACTICE MORE ASSIGNMENTS ?DO YOU WANT TO SOLVE QUIZ QUESTIONS AFTER LEARNING EVERY TOPIC ?THEN MY DEAR STUDENTS THIS COURSE IS THE ONE STOP SOLUTION FOR ALL THE ABOVE QUESTIONS.“Signals and systems” is the basis of all control and signal processing engineering. It will allow you to take a real world machine, process (the system) and create a mathematical model, at which we apply stimuli and analyze it's response (stimuli and response being signals).Examples of systems that manipulate signals are speech recognition, video streaming, cellular networks and medical scans such as MRI. The disciplines of signal and image processing are concerned with the analysis and synthesis of signals and their interaction with systems.Students will Be able to describe signals mathematicallyUnderstand mathematical description and representation of continuous and discrete time signalsBe familiar with commonly used signals such as the unit step, ramp, impulse function, sinusoidal signals and complex exponentialUnderstand how to perform mathematical operations on signalsBe able to classify signals as continuous-time Vs. discrete-time, periodic Vs. non-periodic, energy signal Vs. power signal, odd Vs. even, causal Vs. non- causal signalsUnderstand system properties - linearity, time in variance, presence or absence of memory, causality, bounded-input bounded-output stability and invertibilityBe able to perform the pro

نظم الانارة الداخلية و الخارجيةالمحاضرة الاوليالاعتبارات الواجب مراعاتها قبل البدء في اختيار نظام الانارة. الظل (Shadow) . 2. تجانس الانارة (Uniformity). 3. الوهج (Glare). 4. مستوى الانارة (Illuminance). 5. لون الضوء – درجة حرارة اللون (Color Temperature). 6. خاصية اظهار الألوان (Color Rendering). و فيما يلي شرح وافي لكل نقطة من هذه النقاط : لمحاضرة الثانيةأسس تصميم الانارةالمحاضرة الثالثةمصطلحات الانارةالمحاضرة الرابعةتجانس الإنارة Uniformityالتباين Contrastالوهج Glareزاوية الشعاع Beam Angleالمحاضرة الخامسةاختيار درجة حرارة اللون المناسب للمواد المعمارية الداخليةColor rendering درجة تمييز الألوانبريق الانارة BrillianceAccent خاصية الانارة المركزةعمر المصباح Average lifeالمحاضرة السادسةمصابيح الهالوجينHalogen Lampsالمحاضرة السابعةمصباح الميتال هاليدMetalhalide Lampالمحاضرة الثامنةمصباح الفلوريسنتمقارنة مصباح الليد معأنواع الانارة المختلفةLED Lampالمحاضرة التاسعةالمحاضرة العاشرةالأجهزة الغاطسة في السقفDown lightFluorescent Lampأجهزة الانارة العامةالمحاضرة الحادية عشر المحاضرة الثانية عشرنظم الانارة الداخلية و الخارجيةالمحاضرة الاوليالاعتبارات الواجب مراعاتها قبل البدء في اختيار نظام الانارة. الظل (Shadow) . 2. تجانس الانارة (Uniformity). 3. الوهج (Glare). 4. مستوى الانارة (Illuminance). 5. لون الضوء – درجة حرارة اللون (Color Tem