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Dhaval Maheta (DM)
Registrace 16. 12. 2011
Dr. Dhaval Maheta is an accomplished professor specializing in Business and Industrial Management. He is a Doctorate in Management. He has authored insightful books on various software tools like "Minitab," "Statistical Analysis using R Software," "Machine Learning Using R-Rattle," and "Data Analysis using R."
Dr. Maheta offers comprehensive training sessions on numerous software platforms, including SPSS, STATCRAFT, STATA, R and R-Studio, Minitab, Jamovi, Excel, Power BI Desktop, Google Data Studio, Tableau, SPSS-AMOS, ADANCO, Smart-PLS, SEM using R, Qualitative Data Analysis using Nvivo and Atlas.it, Design of Experiment using Minitab and Design Expert, Eviews, Gretl and Econometric Analysis using R.
This CZcams channel is a gateway to a treasure trove of insights in data analysis and management. Subscribers gain access to tutorials, insights, and tips curated by Dr. Dhaval Maheta himself.
Dr. Maheta offers comprehensive training sessions on numerous software platforms, including SPSS, STATCRAFT, STATA, R and R-Studio, Minitab, Jamovi, Excel, Power BI Desktop, Google Data Studio, Tableau, SPSS-AMOS, ADANCO, Smart-PLS, SEM using R, Qualitative Data Analysis using Nvivo and Atlas.it, Design of Experiment using Minitab and Design Expert, Eviews, Gretl and Econometric Analysis using R.
This CZcams channel is a gateway to a treasure trove of insights in data analysis and management. Subscribers gain access to tutorials, insights, and tips curated by Dr. Dhaval Maheta himself.
Generative AI and Machine Learning using IBM Watsonx AI || Dr. Dhaval Maheta
IBM Watsonx AI is part of IBM's suite of artificial intelligence tools and services. It is designed to provide advanced AI capabilities for enterprises, offering a range of features to support machine learning, natural language processing, and data analysis.
Generative AI: Watsonx AI includes capabilities for generative AI, enabling the creation of new content, ideas, and solutions based on existing data. This can be particularly useful for content creation, product design, and innovation.
Natural Language Processing (NLP): It excels in understanding and processing human language, allowing for improved customer interactions, sentiment analysis, and automated responses.
Machine Learning: Watsonx AI supports building and deploying machine learning models, helping businesses leverage predictive analytics and data-driven insights.
Generative AI: Watsonx AI includes capabilities for generative AI, enabling the creation of new content, ideas, and solutions based on existing data. This can be particularly useful for content creation, product design, and innovation.
Natural Language Processing (NLP): It excels in understanding and processing human language, allowing for improved customer interactions, sentiment analysis, and automated responses.
Machine Learning: Watsonx AI supports building and deploying machine learning models, helping businesses leverage predictive analytics and data-driven insights.
zhlédnutí: 599
Video
21. Hallucinations and Mitigation Strategies || Dr. Dhaval Maheta
zhlédnutí 211Před měsícem
21. Hallucinations and Mitigation Strategies || Dr. Dhaval Maheta
20. Generated Knowedge Prompting (GKP) || Dr. Dhaval Maheta
zhlédnutí 100Před měsícem
20. Generated Knowedge Prompting (GKP) || Dr. Dhaval Maheta
19. Direct Stimulus Prompting (DSP) || Dr. Dhaval Maheta
zhlédnutí 51Před měsícem
19. Direct Stimulus Prompting (DSP) || Dr. Dhaval Maheta
18. Reason + Action [ReAct] Prompting || Dr. Dhaval Maheta
zhlédnutí 92Před měsícem
18. Reason Action [ReAct] Prompting || Dr. Dhaval Maheta
17. Retrieval Augmented Generation [RAG] Technique
zhlédnutí 53Před měsícem
17. Retrieval Augmented Generation [RAG] Technique
16. Use of Exemplars in Large Language Model (LLM) || Dr. Dhaval Maheta
zhlédnutí 34Před měsícem
16. Use of Exemplars in Large Language Model (LLM) || Dr. Dhaval Maheta
15. Tree of Thought [ToT] Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
zhlédnutí 27Před měsícem
15. Tree of Thought [ToT] Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
14. Self-Consistency Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
zhlédnutí 33Před měsícem
14. Self-Consistency Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
13. Chain of Thought [CoT] Prompting || Dr. Dhaval Maheta
zhlédnutí 32Před měsícem
13. Chain of Thought [CoT] Prompting || Dr. Dhaval Maheta
12. Few Shot Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
zhlédnutí 22Před měsícem
12. Few Shot Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
11. One Shot Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
zhlédnutí 32Před měsícem
11. One Shot Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
10. Zero Shot Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
zhlédnutí 38Před měsícem
10. Zero Shot Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
9. Prompt Structures in Large Language Model (LLM) || Dr. Dhaval Maheta
zhlédnutí 34Před měsícem
9. Prompt Structures in Large Language Model (LLM) || Dr. Dhaval Maheta
8. Role Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
zhlédnutí 21Před měsícem
8. Role Prompting in Large Language Model (LLM) || Dr. Dhaval Maheta
7. Optimizing the Prompts || Dr. Dhaval Maheta
zhlédnutí 20Před měsícem
7. Optimizing the Prompts || Dr. Dhaval Maheta
6. Tips for Designning Effective Prompts || Dr. Dhaval Maheta
zhlédnutí 27Před měsícem
6. Tips for Designning Effective Prompts || Dr. Dhaval Maheta
5. Elements of Good Prompt || Dr. Dhaval Maheta
zhlédnutí 41Před měsícem
5. Elements of Good Prompt || Dr. Dhaval Maheta
4. Settings of Large Language Model LLM Parameters || Dr. Dhaval Maheta
zhlédnutí 63Před měsícem
4. Settings of Large Language Model LLM Parameters || Dr. Dhaval Maheta
3. Introduction to Generative A.I. || Dr. Dhaval Maheta
zhlédnutí 120Před měsícem
3. Introduction to Generative A.I. || Dr. Dhaval Maheta
2. CO-STAR Framework and Master Prompting || Dr. Dhaval Maheta
zhlédnutí 127Před měsícem
2. CO-STAR Framework and Master Prompting || Dr. Dhaval Maheta
1. Introduction to Prompt Engineering || Dr. Dhaval Maheta
zhlédnutí 487Před měsícem
1. Introduction to Prompt Engineering || Dr. Dhaval Maheta
writesonic: An AI Art Generator || Dr. Dhaval Maheta
zhlédnutí 1KPřed 4 měsíci
writesonic: An AI Art Generator || Dr. Dhaval Maheta
wombo: An AI Art Generator || Dr. Dhaval Maheta
zhlédnutí 265Před 4 měsíci
wombo: An AI Art Generator || Dr. Dhaval Maheta
Night Cafe: An AI Art Generator || Dr. Dhaval Maheta
zhlédnutí 129Před 4 měsíci
Night Cafe: An AI Art Generator || Dr. Dhaval Maheta
craiyon: An AI Art Generator || Dr. Dhaval Maheta
zhlédnutí 131Před 4 měsíci
craiyon: An AI Art Generator || Dr. Dhaval Maheta
Blue Willow: An AI Art Generator || Dr. Dhaval Maheta
zhlédnutí 104Před 4 měsíci
Blue Willow: An AI Art Generator || Dr. Dhaval Maheta
Adobe Firefly: An AI Art Generator || Dr. Dhaval Maheta
zhlédnutí 108Před 4 měsíci
Adobe Firefly: An AI Art Generator || Dr. Dhaval Maheta
36. TeachAnything.AI: Understand any concept with AI || Dr. Dhaval Maheta
zhlédnutí 344Před 4 měsíci
36. TeachAnything.AI: Understand any concept with AI || Dr. Dhaval Maheta
Great Sir
Sir aap mujhe apke Excel course ke files denge to bahut benefit hota mujhko to practice
👍
Sir, I want this data file so I can do practice in SPSS.
Sir, im currently using smart PLS 4.0 for my data analysis. I have one issue that the hypotheses is not in the same order in test results (discriminant validity) in the order I created model. Is there a way to rearrange hypotheses in smart PLS 4.0?
Sir can you please explain confirmatory factor analysis also throught your CZcams channel. It will be a grt help.
i have done it using spss amos: czcams.com/video/XXftNXEiLcI/video.htmlsi=9Wkse6d5ne4pM8uB
Awesome . Such a nice explanation that a novice can also learn and expertise.
Where will I collect the same data
Dear Dr. Dhaval Maheta, When reporting the results of the extended repeated indicators for formative-formative higher-order constructs, should we report the values in the path coefficients (original sample, standard deviation, t-statistics, p-values) or the values in the total effects? The p-values in the path coefficients are insignificant, while the p-values in the total effects are significant. How should we decide on the hypothesis results?
Hi professor, may I know what is the name of the test used to test for endogeneity?
Nice videos!
Can we rely on auto-coding only when we want to analyze the data, or do we still need to go through coding and classification?
@@Alhamzah_F_Abbas no you cannot rely on it
sir please arrange a workshop on panel data analysis using gretl.
@@shivangikaushal6721 are you in my DM group
Thank you very much, doctor, for your valuable information. Could you please attach the data used in the tutorial to the video description?
Sir in the end you said non normal distribution is not desirable. Then what should we do ?
Sir what to do if data is not normal?
Dear Prof. Dhaval Maheta, I watched your SmartPLS-4 training videos on CZcams. You helped me a lot with my doctoral research. I have a question for you. I am studying a serial mediation model in my thesis. In the structural model, my model goodness of fit values were calculated as SRMR value 0.056, χ2 value 799.167, NFI value 0.836. Additionally, the d_ULS value was calculated as 0.718 and the d_G value was calculated as 0.342. My reliability and validity criteria were met and my hypotheses were significant. Since the NFI value is below 0.90, it does not seem acceptable. Is there anything you can suggest me on this matter? I appreciate any feedback you may have. Please respond at your earliest convenience.
@@MineDeğirmencioğlu how much is it
0.836
@@MineDeğirmencioğlu tolerable
@@DhavalSaifaleeAaryash Thank you. Can you recommend a bibliography I can cite for this?
which is better for data analysis (especially for research in environmental data, and survey data ) LITE, Standard or Pro...?
Hello sir can you share the command syntax file
Great 👍
Thank you sir. I didn't get the transformation here
I wish I had seen this tutorial before now
Concise!
thank you sir
Thank you very much sir
fantastic!
Can you mention the source for the speeches? It is hard to find it sometimes
Why we need to install it form this website, since we can try the trial form the official website?
👍
how to handle structure breakpoints in Johansen cointegration test please
Dear Sir, Thank you very much for your excellent explanations. Sir, how could I report model fit indices like CMIN/DF of a Structural equation model using imputed data?
I am really grateful to God that I met you. Thank you
Thank you for the video 😊
Thank you Prof. for this video. I have a question for you. When reporting data, should we report the reliability and validity results of the low-level model's data? Or should we give the reliability and validity results of the data of the high-level model?
@@MineDeğirmencioğlu both to be reported separately
@@DhavalSaifaleeAaryash Thank you
sir I have 5 categories in my moderator. How to perform multigroup analysis in that case?
use dummy variable analysis
Sir please Complete Full excel course sir please
@@shivajiganesh5671 I hv uploaded 102 videos, see my playlist
sir from where should i get this data which you are using
I am come from birla college BSc IT group
ok see my youtube channel
Sir please provide your Excel files so that I can practice..
Thanks for this video @Dhaval
@DhavalSaifaleeAaryash Thank you for this great efforts. Please if all independent variable stationary at first difference but the dependent variable is stationary at level, should I take the first differences for all of them including the dependent variable? And if the it is stationary at level when applying KSPP test, but stationary at first difference when using ADF AND PP TESTS should i use the first difference in this case?
I am following your video keenly. Thank you for such an good content ,for free.
Thank you so much
Awesome content and demontration. Saying Thank you from Ghana, West Africa.
Great content.
Sir . Thank you so much .
Sir, if there are two dependent variables and one independent variables, how to write the variables in estimation window.
Sir, if two variables are stationary at 2nd difference and 1 at 1st difference. Should I need to apply cointegration
@@artimalik1126 see my video on ARDL
can you estimate different frequencies with VAR model?
hello sir: I am not able to solve the issue in HTMT AR CB EHM IS IW PEOU PU SUIT TRU AR CB 0.607 EHM 0.607 1.083 IS 0.551 0.659 0.701 IW 0.555 0.846 1.01 0.751 PEOU 0.566 0.57 0.582 0.741 0.621 PU 0.576 0.43 0.422 0.681 0.498 0.918 SUIT 0.577 1.278 0.891 0.6 0.721 0.541 0.424 TRU 0.543 0.499 0.495 0.678 0.582 0.705 0.797 0.47