Tag: AI Training

Best AI Training Courses In 2025
A world where machines can think, learn, and make decisions just like humans? It’s not science fiction anymore. It’s the reality of artificial intelligence. As AI continues to shape our future, more and more people are eager to dive into this fascinating field. We at StartupTalky understand the buzz around AI and how it’s changing the game for businesses and individuals alike. So, you’re interested in riding the wave of AI but don’t know where to start? Don’t worry, we’ve got you covered.

The internet is brimming with artificial intelligence courses that cater to beginners and experts alike. From Google’s AI essentials to Stanford’s healthcare-focused AI program, there’s something for everyone. Whether you’re looking to boost your career or simply satisfy your curiosity, these online AI courses offer a chance to learn from industry leaders and top universities.

Google AI Essentials
IBM AI Developer Professional Certificate
DeepLearning.AI’s Deep Learning Specialisation
Udacity’s Artificial Intelligence Nanodegree
edX’s Artificial Intelligence Professional Certificate
MIT OpenCourseWare’s Artificial Intelligence
Google’s Machine Learning Crash Course

Google AI Essentials

Course Google AI Essentials

Price Free

Course Length Approximately 10 hours

Google AI Essentials – Best AI Training Courses

Google AI Essentials is a self-paced artificial intelligence course designed to help individuals across various industries boost their productivity using AI tools. In under 10 hours, learners gain practical skills to apply AI in real-world scenarios. The course covers essential topics such as using AI for idea generation, content creation, and effective prompt writing. It also emphasises responsible AI use by teaching learners to identify potential biases and avoid harm. This AI course is accessible to everyone, requiring no prior technical experience, and is structured to fit into busy schedules.

Course Overview

The course comprises five modules: Introduction to AI, Maximising Productivity with AI Tools, Discovering the Art of Prompt Engineering, Using AI Responsibly, and Staying Ahead of the AI Curve. Through a mix of videos, readings, and interactive exercises, participants learn to use generative AI tools, create effective prompts, and select appropriate AI tools for various work needs. The hands-on approach allows learners to immediately apply their newly acquired skills to workplace tasks.

IBM AI Developer Professional Certificate

Course IBM AI Developer Professional Certificate

Price $49 per month on Coursera

Course Length Approximately 6 months

IBM AI Developer Professional Certificate – Best AI Training Courses

The IBM AI Developer Professional Certificate is a comprehensive online programme designed to equip learners with practical skills in AI development. This self-paced course, offered through Coursera, can be completed in about six months with a commitment of 4-10 hours per week. It’s suitable for both beginners and experienced professionals looking to enhance their AI skills.

Overview

This certificate programme covers essential aspects of AI, including machine learning, deep learning, natural language processing, and computer vision. Learners engage in hands-on projects and labs, gaining real-world experience in building AI models and applications. Upon completion, participants receive a professional certificate from IBM, a globally recognised leader in AI and technology.

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DeepLearning.AI’s Deep Learning Specialisation

Course Deep Learning Specialisation

Price $49 per month on Coursera

Course Length Approximately 2–6 months

DeepLearning.AI’s Deep Learning Specialisation – Best AI Training Courses

Deep Learning Specialisation is a comprehensive artificial intelligence course that equips learners with foundational skills in deep learning. This programme, led by AI pioneer Andrew Ng, offers a blend of theoretical knowledge and practical applications. The specialisation comprises five courses, covering neural networks, optimisation techniques, and advanced topics like convolutional and recurrent neural networks.

Specialisation Course Structure

The specialisation is structured into five courses:
1. Neural Networks and Deep Learning
2. Improving Deep Neural Networks
3. Structuring Machine Learning Projects
4. Convolutional Neural Networks
5. Sequence Models
Each course builds upon the previous one, providing a logical progression through the field of deep learning.

Udacity’s Artificial Intelligence Nanodegree

Course Udacity’s Artificial Intelligence Nanodegree

Price $399/month.

Course Length Approximately 3 months.

Udacity’s Artificial Intelligence Nanodegree – Best AI Training Courses

This one is designed to equip learners with essential skills in AI development. This programme offers a blend of theoretical knowledge and practical applications, making it an excellent choice for those looking to start or advance their careers in AI.

Programme Structure

The nanodegree is structured into two terms, each lasting three months. Students are expected to dedicate approximately 15 hours per week to complete the programme successfully. The curriculum

covers a wide range of topics, including machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.

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edX’s Artificial Intelligence Professional Certificate

Course edX’s Artificial Intelligence Professional Certificate

Price $447 total for the certificate program.

Course Length Approximately 6 months.

edX’s Artificial Intelligence Professional Certificate – Best AI Training Courses

It is a course that equips learners with essential skills in AI development. This professional certificate program covers a wide range of topics, from basic concepts to advanced techniques in machine learning.

Curriculum

The curriculum includes courses on supervised machine learning, unsupervised learning, deep learning, and reinforcement learning. Students learn to apply common operations to datasets using Python, explain various learning models, and implement algorithms using Scikit-learn.

Skills Gained

Participants gain practical skills in data preprocessing, plotting, and analysing factors that impact algorithm performance. They also learn to optimise machine learning pipelines, implement clustering techniques, and train deep neural networks for classification and regression tasks.

MIT OpenCourseWare’s Artificial Intelligence

Course MIT OpenCourseWare’s Artificial Intelligence

Price Free

Course Length Self-paced, varies by user

MIT OpenCourseWare’s Artificial Intelligence – Best AI Training Courses

This artificial intelligence course, offered by MIT OpenCourseWare, provides a comprehensive introduction to AI fundamentals. Led by Professor Patrick Henry Winston, it covers essential concepts in knowledge representation, problem-solving, and learning methods.

Course Structure

The course comprises lectures, recitations, and tutorials, offering a well-rounded learning experience. It delves into three major areas: Search, Machine Learning, and Knowledge Representation and Inference. Students engage with topics such as graph search, neural networks, and natural language processing.

Key Concepts

Learners explore AI applications in rule chaining, heuristic search, and constrained search. The course also covers decision trees, SVMs, and other learning paradigms, equipping students with practical skills in developing intelligent systems.

Google’s Machine Learning Crash Course

Course Google’s Machine Learning Crash Course

Price Free

Course Length Approximately 15 hours

Google’s Machine Learning Crash Course – Best AI Training Courses

This one is all about a practical introduction to artificial intelligence and machine learning. This free AI course comprises over 30 exercises, 25 lessons, and takes approximately 15 hours to complete. Taught by Google researchers, it provides real-world case studies and interactive visualisations of algorithms in action.

Crash Course Overview

The course covers fundamental machine learning concepts, including supervised and unsupervised learning, regression, classification, and neural networks. It’s designed for both beginners and those with some programming experience, making it an excellent choice for anyone looking to enhance their AI skills.

Key Topics

Key topics include linear regression, logistic regression, neural networks, and working with numerical and categorical data. The course also delves into advanced subjects like large language models and ML fairness, ensuring learners gain a comprehensive understanding of artificial intelligence and machine learning.

Practical Exercises

Practical exercises run directly in the browser using the Collaboratory platform, allowing learners to apply their knowledge in a hands-on manner. These exercises cover various aspects of machine learning, from basic concepts to more advanced topics, helping students build practical skills in AI development.

End Note

We at StartupTalky believe that staying ahead in the AI game is crucial for success in the 21st century. No matter who you are – a budding entrepreneur or a seasoned professional, these courses offer a chance to level up your skills and stay competitive. And hey, if you’re a founder looking to partner or work with experts in the startup world, don’t hesitate to reach out to the StartupTalky team at shubham@startuptalky.com for all things ‘startups’. When every day is a beginning, so why not start your journey today?

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FAQ

Which is the best course to learn AI?

Here are the best courses to learn AI:
- Google AI Essentials
- IBM AI Developer Professional Certificate
- DeepLearning.AI’s Deep Learning Specialisation
- Udacity’s Artificial Intelligence Nanodegree
- edX’s Artificial Intelligence Professional Certificate
- MIT OpenCourseWare’s Artificial Intelligence
- Google’s Machine Learning Crash Course
Can I learn AI in 3 months?

Yes, you can gain a basic understanding of AI in three months through focused online courses, especially covering foundational topics like machine learning, data science, and neural networks.

Is AI hard to study?

Studying AI can be challenging due to its technical concepts like machine learning, algorithms, and data analysis, but it’s manageable with dedication and consistent practice.
February 10, 2025
AI Firms Say Analysing Use Cases and Robust Data Strategies Key for AI

One phenomenon that knocked the wind out of everyone’s lungs in 2023 was artificial intelligence! Right from chatbots to AI tools for video and content creation, it has caught everyone’s fancy.

India wasn’t far behind, as several companies and apps mushroomed, reflecting the global scenario. A NASSCOM report shows that more than 60 Indian startups started shop in April and June 2023. The optimism surrounding AI has rubbed off on investors, too.

NASSCOM’s India Data Science & AI Skills Report shows spending on AI in India topped $3 billion in 2022 and is expected to jump to $4.2 billion by 2024.

In fact, Goldman Sachs sees investments in the AI sector topping $160 billion globally by 2025, with India having an advantage of “resilient growth and strong demographics,” which could attract investments from global investors and corporations.

“The intricate dance of AI and ML capabilities, coupled with multi-channel integration, has propelled businesses toward a future where agility and scalability are paramount,” said Abhijit Dutta, Chief Strategy Officer of Hostbook, a cloud-based accounting services firm that also offers automated business solutions.

Despite the rapid growth of AI companies, challenges remain when it comes to integrating AI into traditional corporate processes.

In this article, StartupTalky speaks to a few AI consulting companies that shed light on AI integration in India.

AI Awareness
Data Mining Strategy
Identifying Use Cases
AI Training

AI Awareness

The top challenge faced by AI companies is to dispel myths surrounding AI integration, said Agam Chaudhary, founder and CEO of Two99, a collective of agencies with a focus on advanced e-commerce, technology, and marketing.

Explaining how they tackle the problem, Chaudhry said, “We show them it’s not a sci-fi flick but a practical tool that can make their lives easier. We bring out the success stories custom-made for their industry. We lay it all out on the table—the good, the bad, and the ethical considerations. Building trust is crucial. We’re like AI consultants, working hand-in-hand with them, understanding their worries, and customizing solutions that fit like a glove,” Chaudhary said.

Based on client experiences, PwC had listed some myths that clients seemed to express about AI: ‘ businesses don’t need AI, and they are ‘too risky, to name a few.’

Over time, there seems to be a gradual attitudinal shift towards AI. A survey of 54,000 workers conducted by PwC in September showed that a third of respondents believe AI will help increase productivity and efficiency. More than a quarter said it would help them learn valuable new skills.

An AI survey by Uplekha found that 61% of Indian companies feel AI will make work more efficient.

Employee Attitudes on AI by PwC

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Data Mining Strategy

Data mining is the cornerstone of AI and ML. AI heavily leans on vast sets of existing data and statistics to come up with a near-perfect AI model of content or analysis, which can then be applied to the problems in question.

According to E&Y India Chairman and CEO Rajiv Memani, India is the second largest generator of data after China, which is an added advantage for training AI models.

Yet, the availability of clean data sets has been an issue.

“All three aspects, i.e., clean data, relevant data, and a sufficient amount of data, are important. The models need sufficient data, and for financial risk use cases, they should cover historical data from at least 1-2 economic cycles. In the absence of such data, these AI and ML models produce suboptimal results and end up losing user confidence in using these models,” said Abhinava Bajpai, Co-founder and Head, Acies TechWorks.

The government is in the process of developing India’s comprehensive AI strategy, which involves building an India Dataset Platform and an AI Compute Platform.

Information and Technology Minister Rajeev Chandrasekhar recently elaborated on these, saying that the Indian dataset platform will be one of the largest and most diverse collections of anonymized datasets to train multi-parameter AI models. Meanwhile, the India Compute Platform will create a substantial graphic processing unit (GPU) capacity for enterprises to train AI models under a public-private partnership.

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Identifying Use Cases

In a bid to jump onto the AI bandwagon, companies are struggling to adopt specific use cases, AI experts said.

“I would say that you know, if organizations are looking at adopting AI, look at some of those achievable use cases, which they can then take right and partner with companies to achieve those,” said Rohit Yadava, Chief Operating Officer, MSys Technologies, which offers digitalization services to companies.

Echoing this view, Sairam Vedam, Chief Marketing Officer of Cigniti Technologies, says, “Our approach has always been to understand the existing data strategy of the company. Also, what is the existing automation strategy of the company because we are a born-testing quality engineering company? So, we look at AI applications through those two lenses. And then, as I said, educate, experiment, experiment in the sense of experiment on use cases.”

Another report by Deloitte outlined the use cases of AI across six major industries: consumer, energy, resources, and industrial; financial services; government and public services; life sciences and health care; technology; media; and telecommunications.

For instance, use cases within the consumer goods segment could include aiding content generation, trade promotions, creating new product prototypes, creating an immersive marketing experience, market intelligence through data access, on-demand customer support, and shopping assistants.

“Performing cost-benefit analysis of AI and ML models before implementing them is important for the continuous and persistent use of such models… The size of the business and revenue and cost impacts need to be considered before implementing AI and ML models,” said Bajpai from Acies Consulting.

Global Data Science and AI Installed Talent

AI Training

Training staff with AI know-how has now become imperative. This is apparent from the rise in demand for AI training and AI-related upskilling courses.

“It has become essential for executives to learn AI. Data science training is specifically helpful to train aspirants in AI, and by ensuring the holistic development of executives, these programs can become a game-changer in helping industries realise the true potential of AI,” said Piyush Arora, senior director of business strategy at AI-based learning platform Edvancer.

Edvancer has seen a 4x rise in applications for AI courses and a 100% increase in interview opportunities for students with data science and AI qualifications.

A NASSCOM report shows India is currently ranking 2nd in training and hiring AI talent in the world.

“A major portion of the future talent demand will come from the existing tech workforce through upskilling; learning curves are becoming shorter, and skills are becoming redundant in 18 months,” NASSCOM said, adding that “design thinking” is a key skill to implement AI and not merely the ability to build and run complex algorithms.

However, all this training comes at a heavy cost.

A study conducted by the Boston Consulting Group and the Indian Institute of Management-Ahmedabad estimated that just the top 500 Indian companies would require “at least one million hours of training.”

“Organizations must invest in significant upskilling of mid- and senior-level management on the business aspects of AI, digital transformation, ‘agile’ ways of working, and more. Companies have a choice to prioritize AI and adopt it or perish—and the nature of this technology is such that either scenario would come about very quickly,” the report said.

The top 500 listed companies would need at least 25,000–30,000 advanced practitioners of AI and ML in the next 3–5 years, including AI professionals, data scientists, data engineers, and enterprise architects, the report said.

Conclusion

AI maturity has been a buzzword in 2023, given the boom in AI and its peripheries. According to AI watchers and experts, this maturity will accrue over a period of time with enough use cases to innovate, experiment, and smartly apply collated data. However, the large boom in AI within the country has laid bare the talent and skills gap. Hence, training and upskilling pertinent AI skills must become a priority for companies going forward.

December 30, 2023