QS has launched the QS World Future Skills Index 2027 today (https://t.co/LZXbHZSaR1), which is a global benchmark of how effectively nations develop, align and apply skills in the age of AI. The Index assesses 89 countries across four indicators – Skills Alignment, Academic Readiness, Future of Work and Economic Transformation.
India ranks 13th in world, climbing from 25th position last year (a rise of 12 places - the biggest jump in the top 30). India leads South Asia and leads the entire lower-middle-income group across all four indicators.
Congratulations to @dpradhanbjp Shri Dharmendra Pradhan Ji whose leadership of the National Education Policy 2020 and reform of India's education ecosystem is laying the foundation for this rise. The road to Viksit Bharat 2047 runs through skills – and India is walking it with purpose. 🇮🇳
It is thus evident that 'Khan Sir' aka Faizal Khan has a definitive set of pre-conceived notions and uses his rhetoric to lend credence to them during these podcasts.
Most importantly, his claims are not based on either facts or understanding of ground realities that facilitate/ hinder functioning at the governmental level.
While it is easy for him to ridicule those who believe in the idea of India, a large section of the society who are bullish about India's progress may be affected by his deliberate pessimistic attitude (which is not rooted in truth).
In the coming days, OpIndia will de-construct more such logical fallacies and pedestrian arguments of Khan Sir. Stay tuned
Faizal Khan forgets about 'MUDRA Yojna' while asking govt to help finance the poor in entrepreneurship
Khan Sir got carried away in his rant about the distress of the poor that he forgot about the Mudra Yojna introduced by Modi govt to encourage entrepreneurship in the country.
He said that if govt helped the poor with financing, they got increase their income substantially.
Mudra Yojna serves this exact purpose. It provides collateral-free loans to small business owners up to Rs. 20 lakhs.
FYI: More than 52 crore people benefitted from this scheme (which vastly includes the poor).
(9/n)
Nothing done to benefit women in India, says Khan sir
For the easier understanding of Faizal Khan, we are listing out the schemes launched by the Modi government to particularly uplift women and girls
1. Sukanya Samriddhi Yojana (SSY)
2. Lakhpati Didi Scheme
3.Drone Didi Scheme
4. Mission Indradhanush
5. TREAD Scheme
6. Ujjwala Yojana (benefits women by providing clean fuel)
7. Pradhan Mantri Awas Yojana (women get ownership of the house)
8.STEP Initiative
9. Mahila E-Haat Scheme
10. Mahila Samman Savings Certificate (MSSC) Scheme
11. Mahila Shakti Kendras
(8/n)
Unhinged rant about No 'Roti, Kapda, Makan'
During the same podcast, Faizal Khan claimed that the poor have no access to healthcare or sanitation.
To address these challenges, Modi govt had taken them on a mission mode since 2014.
Over 11 crore Toilets & 2.23 lakh Community Sanitary Complexes have been built to prevent OD.
The issue of Healthcare for the poor have been met with policies like Ayushman Bharat, which covers 55 cr people today.
It is world's largest publicly funded health assurance scheme.
For the sake of sounding 'intellectual' during a podcast, Khan sir is deliberately misleading the audience.
He is making outrageous claims that the poor in India have no access to healthcare and toilets.
(7/n)
Khan Sir wants you to forget the atrocities of the past
During one of the podcasts, Faizal Khan remarked that the govt is unjustly focused on Babar and not what is happening in the present.
"Those who forget the past are condemned to repeat it.
Much of the problems faced by present-day India are rooted in past. So, we cannot afford to forget Babar (even if it draws Khan's ire) and the atrocities perpetrated by the Mughal tyrant.
Khan thinks it is a 'gimmick' when the govt talks about the future. He is aghast at recounting of the deeds of Babar.
And in the same breath, he has issues with the policies implemented by govt presently.
(6/n)
States like Bihar, Uttarakhand and the Union Territories have already achieved this goal of doubling farmers' income.
It is important to mention that when the plan was set in motion in 2016-2017, the likes of Covid-19 pandemic and well-coordinated Opposition to progressive farm laws was not accounted for by the Modi government.
These 2 key developments had certainly hindered the fulfilment of the stated objectives within the 2022-2023 period.
Faizal Khan is openly ignoring realities of a global pandemic and politically motivated campaign to gut farm laws for juvenile arguments such as 'Ho gaya Dugna'
(5/n)
Mocking vision to double income of farmers
Faizal Khan notoriously mocked the vision to double farmers' income by the Modi government. He claimed 'Ho gaya dugna?"
The fact of the matter is that no Indian government has ever before envisioned such a goal with a fixed timeframe.
(4/n)
It is clearly difficult for individuals with a limited outlook and myopic vision to imagine the transformation of India into a developed nation.
Without a clear vision, no goal has ever been achieved.
PM Modi's innate ability to think about the country's future progress has ushered India into an era of economic development.
Our economy is now the 4th largest in the world. In the last 11 years, it has witnessed a 109% increase.
Faizal Khan could possibly have never imagined in 2014. And hence, he lacks the ability to envision India's development in the next 22 years.
(3/n)
No need for 2047 ‘Viksit Bharat’ vision
In one of the podcasts, Khan Sir claimed that the vision to transform India into a developed nation (Vikshit Bharat) by 2047, i.e. 100 years of Independence, is a 'political gimmick'.
(2/n)
🧵Thread
Faizal Khan (aka Khan Sir) has recently made several comments in an attempt to downplay the vision and achievements of the Modi government.
Let us look closely at the truth behind some of the comments:
(1/n)
🚨 I recently added a brand new chapter to my free, online e-book, Applied Machine Learning in Python
📷'#DeepLearning with Autoencoders'!
This chapter includes:
1️⃣ A walkthrough of model training with all math
2️⃣ A simple autoencoder built from the ground up using NumPy
Check it out here: https://t.co/6ejtjBiJIu
#Python #AI #ML #OpenEducation
🧐Can large earthquakes be more predictable than smaller ones?
Venegas-Aravena & Zaccagnino say YES - large quakes are influenced by large-scale energy buildup, while small quakes tend to be more random due to local stress variations.
Read more: https://t.co/Maje18IB1I
#ANRFIndia invites nominations for the prestigious Ramanujan Fellowship!
Formulated for exceptional scientists and engineers of Indian origin.
Start Date: 1 June 2025
Last Date: 30 June 2025
🔗For more, visit: https://t.co/oME7Eiybdh
#researchgrant#ramanujanfellowship
Indian delegation actively participated in #G20 2nd RIWG Meeting at Mbombela, Mpumalanga province (South Africa) & undertook negotiations during discussions pertaining to different Priority Areas & outcome deliverables in context of Draft Research Ministerial Tshwane Declaration.
I just added a new chapter to my free, online e-book,
"Applied Geostatistics in Python",
on decision making in the presence of uncertainty.
What can I say?
1. If we don't impact the decision, we don't add value!
2. A good uncertainty model is more important than one best estimate!
3. I'm stoked to help you put this together to maximize your impact!
Check it out @ https://t.co/ynASgrxEoo ∀.
When I teach PCA #MachineLearning, I tell my students that PCA is a simple 2-way trip!
First Leg – Projecting to Lower Dimensions: We start with high-dimensional data and use PCA to project it onto a new set of axes (the principal components). This is like flattening the data into a simpler, lower-dimensional space that captures the most important patterns in the data.
Second Leg – Reversing the Operation: After projection, we can reverse the process to reconstruct the data back to its original space. While it may not be perfect if we've reduced dimensions, we can still get a close approximation of the original data, preserving most of the important information.
Arbitrary Feature Combinations: The beauty of PCA is that we can take any arbitrary combination of features, perform the 2-way trip (projection and reconstruction), and visualize or analyze the data in a simpler form.
Key Idea: PCA allows us to project data into a lower-dimensional space and reconstruct it, preserving the most important information while reducing the dimensionality.
Then I demonstrate this with this Python @matplotlib interactive dashboard, on #GitHub @ https://t.co/UVJ2zheeXA ∀.