What is "How To Learn Artificial Intelligence (AI)?"

The resource titled "How To Learn Artificial Intelligence (AI)?" is a comprehensive educational roadmap and introductory guide hosted by Applied AI Course, a prominent ed-tech platform that was acquired by Scaler (InterviewBit) in 2021. While the specific URL serves as a free, high-level guide for beginners, it acts as the gateway to the platform’s "Applied Machine Learning Course," which has gained a reputation for being one of the most rigorous and practical programs in the industry.

Founded by Srikanth Varma Chekuri, a former Senior Applied Scientist at Amazon, the platform was built on the philosophy of "learning by doing." Unlike many academic courses that focus heavily on abstract theory, this tool emphasizes the application of AI to real-world business problems. It bridges the gap between complex mathematical foundations and production-level coding, making it a favorite for those serious about transitioning into roles like Data Scientist, Machine Learning Engineer, or AI Researcher.

The curriculum outlined in the guide follows a logical progression: starting with the absolute basics of Python programming and essential mathematics (Linear Algebra, Statistics, and Probability), moving into classical Machine Learning algorithms, and culminating in advanced Deep Learning and Computer Vision. Since its integration with Scaler, the content has been further refined to include live mentorship and a more structured career-tracking system, though the core "Applied AI" DNA remains focused on deep technical intuition.

Key Features

Structured Learning Roadmap: The guide provides a clear, week-by-week or month-by-month path. It eliminates the "analysis paralysis" many beginners face by telling them exactly what to learn and in what order, starting from Python basics to complex neural networks.
Intuitive Mathematical Foundations: One of the standout features of the Applied AI approach is how it teaches math. Instead of just memorizing formulas, students are taught the "why" behind Linear Algebra and Statistics, specifically how these concepts help a model learn from data.
Case Study-Based Learning: The platform uses over 30+ real-world case studies. Students don’t just build "toy" models; they work on problems like predicting taxi trip durations, detecting cancer from medical images, and building recommendation engines similar to those used by Netflix or Amazon.
Focus on Production and Deployment: Unlike many MOOCs that stop at a Jupyter Notebook, this tool teaches students how to deploy models using tools like Flask, AWS, and Docker. This ensures that the learner understands the full lifecycle of an AI project.
Live Mentorship and Doubt Clearing: Under the Scaler umbrella, the tool now offers robust support, including 1-on-1 mentorship sessions with industry experts and a dedicated "Help" ecosystem where technical queries are typically resolved within hours.
Interview Preparation and Portfolio Building: The guide and subsequent course emphasize building a GitHub portfolio. It includes specific modules on "Machine Learning System Design," which is a critical component of high-level AI interviews at top tech companies.

Pricing

The pricing for "How To Learn Artificial Intelligence (AI)?" and its associated programs has evolved significantly since the acquisition by Scaler. It is important to distinguish between the free guide and the paid professional programs:

Free Resources: The roadmap provided at the URL is free to access. Applied AI also offers various free preview videos and introductory modules on their YouTube channel and blog to help students get a feel for the teaching style.
Self-Paced Course (Legacy): Historically, the standalone Applied AI Course was priced around $350 to $400 (approx. ₹25,000 - ₹30,000). While this specific self-paced version is less prominently advertised now, it occasionally remains available for those who do not require live classes.
Scaler Data Science & ML Program: Most students are now directed toward the full Scaler program. This is a premium, long-term bootcamp (8-12 months) that includes live classes and career placement. The pricing for this program typically ranges from $3,000 to $4,500 (approx. ₹2.5 Lakhs to ₹3.5 Lakhs), often with EMI options and "pay-after-placement" structures in specific regions.

Pros and Cons

Pros

Exceptional Teaching Quality: Srikanth Varma is widely praised for his ability to simplify complex topics. His "intuition-first" approach makes even the most daunting algorithms accessible to those without a Ph.D. in Mathematics.
Practical Rigor: The assignments are notoriously difficult and require genuine effort. This "trial by fire" ensures that those who complete the course actually possess the skills they claim to have.
End-to-End Coverage: It covers everything from data cleaning and exploratory data analysis (EDA) to hyperparameter tuning and model deployment.
Strong Community & Alumni Network: With thousands of former students now working at companies like Google, Amazon, and Microsoft, the networking potential is significant.

Cons

Significant Time Commitment: This is not a "crash course." To get the most out of it, learners need to dedicate 15–20 hours a week for several months.
High Cost (Post-Acquisition): The transition to the Scaler model has made the full program significantly more expensive than many other online alternatives like Coursera or Udemy.
Math Intensity: While the math is explained well, there is a lot of it. Learners who are purely looking for "low-code" or "no-code" AI solutions will find this course overwhelming.
Pace of Updates: Some users have noted that while the core ML/DL content is evergreen, some of the specific library versions in the recorded videos can occasionally feel a bit dated compared to the very latest releases in the fast-moving AI field.

Who Should Use How To Learn Artificial Intelligence (AI)??

This tool is not for the casual hobbyist looking for a 2-hour overview of ChatGPT. Instead, it is designed for:

Aspiring Data Scientists: Individuals who want a structured, deep-dive education that will take them from zero to job-ready.
Software Engineers: Developers who want to move into the AI space and need to understand the underlying mechanics of the models they are calling via APIs.
Career Switchers with a STEM Background: Those who have some comfort with logic and numbers but need a guided path to apply those skills to the field of Machine Learning.
Students Preparing for Technical Interviews: The focus on "Machine Learning System Design" and "Intuition" makes it an excellent resource for anyone targeting roles at "Big Tech" (MAANG) companies.

Verdict

The "How To Learn Artificial Intelligence (AI)?" guide and the subsequent Applied AI Course remain some of the most respected resources in the global AI community. Its greatest strength lies in its refusal to cut corners; it demands that you understand the math, write the code from scratch, and solve real problems.

If you are looking for a quick certification to add to your LinkedIn profile with minimal effort, this is not the tool for you. However, if you are willing to put in the work and are looking for a "masterclass" level of education that prioritizes deep understanding over superficial knowledge, Applied AI is a top-tier choice. While the newer Scaler pricing model may be a barrier for some, the quality of the content and the resulting career opportunities often justify the investment for those serious about a long-term career in Artificial Intelligence.