Meet Ravi Singh, author of this article, who is recognized as a Principal AI Scientist. He has a specialization in the world of AI for more than 15 years, with hands-on work experience in the field of Artificial Intelligence, Machine Learning, and Deep Learning technologies. The innovative business solution developed successfully by him tackled most of the challenging problems in the landscape of Artificial Intelligence. The knowledge that will be shared by him and insights with multiple Data Science and AI communities help others learn and grow. Ravi Singh, as the author of this article, has thoughtfully analyzed various AI programs and focuses on beginner-friendly courses that guide newcomers with clarity and help them build confidence.
Updated on October 10, 2025
Top 10 Best
AI Courses
For Beginners
When I first stepped into Artificial Intelligence(AI), I was not sure where to begin. That is when I found LogicMojo a perfect launchpad into AI. Their Top 10 beginner friendly AI courses for 2025, designed by experts, gave me hands-on projects and practical skills that boosted my confidence. Completing the program not only prepared me for real job roles but also helped me feel truly job-ready. Today, I am proud to be building my career in one of the most exciting fields in technology. Begin your path to learn Artificial Intelligence with this first step!
The Best AI Course for Beginners: Our #1 Recommendation for 2025
I still remember the days how lost I felt last year by searching or scrolling through endless AI courses. When I first explored AI courses, I didn't know where to start. Discovering the Logicmojo AI & ML course actually changed my game forever. It guided me step by step, making complex topics easy, and offered practical projects with real-world examples. This course gave me confidence, clarity, and the skills to step into a technology career in AI.
When I first explored Artificial Intelligence (AI) I took courses at LogicMojo, which gave me a structured path with step-by-step curriculum, hands-on projects, and expert mentors with real industry experience. The courses were designed for career growth, or a
working professional, whether switching to roles like AI Engineer or GenAI Architect, or upskilling in Machine Learning, Deep Learning, and Generative AI. This guide helped me gain clarity, build confidence, and land in one of the most exciting fields in AI technology. Truly, the perfect starting point for an AI career. Check out our side-by-side comparison and the FAQ to quickly find the perfect AI course that matches your goals and sets you on the path to success.
Top 10 Best AI Courses For Beginners - List
Best Artificial Intelligence & Machine Learning courses for 2025
After evaluation of 500+ courses by the LogicMojo team experts with 15 years of experience in building AI systems at Fortune 500 companies, the LogicMojo AI and ML course stands out as the #1 choice for beginners who want to get into IT in 2025. This program is specially designed for complete beginners with no prior AI experience or coding experience and they want step by step from absolute basics to advanced real world applications of AI.
Why it's a top choice:Logicmojo AI & ML course deserved the #1 spot as the best course for beginners in 2025 because you don't need any prior AI knowledge or experience to complete this course. This course starts with Python fundamentals by assuming you are a complete beginner, then gradually moves to concepts like deep learning, generative AI, agentic AI, and then eventually moves to the deployment part of MLOps with practical hands-on learning. You can directly interact with the mentor 1:1 whenever you're stuck in projects. And this course has a 4.9 rating across all platforms from candidates from different domains.
Best for:Complete beginners, working professionals with no AI background and recent graduates who want a practical, project based learning path to crack high paying AI and ML roles.
Key features:
What you'll learn: It starts from absolute zero and covers Python basics first and statistics fundamentals, then progressively moves to Deep Learning, Gen AI, GPT, ML Algorithms, and production-ready MLOps at a pace beginners can follow.
Personalized Mentorship: They offer 1:1 guidance and code reviews from a senior AI architect who patiently mentors you through each project developed and available whenever you get stuck or confused.
Practical Experience: Focuses on 10+ beginner to advanced industry level projects so you actually build things instead of just watching videos, i feel this is the best way for beginners to learn.
Proven Outcomes: The course has an impressive 87% placement rate, with many complete beginners candidates successfully transitioning to AI and ML roles in top IT companies.
Consideration: The 7 month program is intensive and requires dedicated commitment to weekend classes with real time coding sessions. but the structure is designed for working professionals who are learning AI for the first time while managing their jobs.
AI for Everyone by Andrew Ng is also a good option for non-technical for non technical candidates. What sets it apart is that it requires basic coding skills and little bit of math knowledge. Andrew Ng designed this specifically for business professionals to help them understand AI technologies and use AI tech stack to apply in their organizations. The course is broken into short videos, real-world case studies, and practical frameworks.
Best for: Business professionals, executives, marketers, team leads and anyone who needs to make AI decisions or lead AI projects without becoming a programmer.
Key features:
What you'll learn: how to build a sustainable AI strategy, real examples of what AI can and cannot do, and how to navigate technological change in your organization without touching code.
Instructor: These classes were recorded by Andrew Ng, founder of DeepLearning.AI and co-founder of Coursera, former chief scientist at Baidu and founding lead of Google Brain. He knows how to explain complex AI concepts in simple terms.
Learning Mode: The course offers video content with captions and audio available in multiple languages, making it accessible globally, with about an hour of video content per week spread across four weeks.
Proven Outcomes: Andrew Ng has changed countless lives through his work in AI education with millions of learners taking his courses. It personally helped me to change my perspective on using AI in my work.
Consideration: This is a high-level conceptual course perfect for understanding AI strategy and decision-making, but if you want to actually build AI models or write code, then you might need to go for other courses.
I took the Generative AI with LLM course from AWS and Deep Learning AI last year when my company needed someone to lead our ChatGPT integration project. It's a very practical LLM course. It start from basics and then go to MLOps real production techniques. The instructors are actually AWS practitioners like Chris Fregly and Shelbee Eigenbrode who build and deploy AI in real business cases. The course is broken into 3 weeks with about 16 hours of content and the hands-on lab run in actual AWS environment where you fine-tune the models with reinforced learning. It's good for the candidates who want to understand the complete deployment process from the beginning in AWS environment for Generative AI applications.
Best for:ML engineers with Python experience and basic machine learning knowledge who want to specialize in building and deploying production level LLM applications..
Key features:
What you'll learn: You wil learn to deploy large language models (LLMs) using AWS tools and Mmaster prompt engineering, reinforcement learning.
Instructor: Taught by AWS Developer and AI practitioners with real world experience who share practical knowledge from actual business deployments Generative AI with LLMs. This course ensuring you learn techniques that work in production, not just in theory..
Practical Experience: Three weeks of content with 16 hours of handson labs hosted by AWS Partner Vocareum in a real AWS environment. you will work on dialogue summarization, fine-tune FLAN-T5 models with reinforcement learning, and experiment with prompt engineering.
Proven Outcomes: This course enhanced my understanding of the Generative AI project lifecycle, particularly architecture and implementation strategies. I learned to integrate generative AI in my application.
Consideration: This is an intermediate course requiring Python coding experience and familiarity with ML basics like supervised learning, loss functions if you have taken the Machine Learning Specialization or Deep Learning Specialization, you will be ready
.
I enrolled in this 2-month program last year to refresh my PyTorch skills. And this is one of the good course to learn the basics of AI and machine learning. Python Fundamentals, NumPy, Pandas, Matplotlib, these all discuss in detail with data work. Along with that, basic statistics library and building neural network from scratch using PyTorch AI programming is discussed in detail. What impressed me was that they don't just teach you to call library functions. They actually make you understand why neural network work. But in some lessons, you might see that it's teaching basics of Python and eventually in the assignments there is a very complex topic is given which has no relation with the problems that is discussed. So, I think Udacity could do better job in bridging those gaps. But overall for the beginners, it's a good course.
Best for:Beginners with some basic programming knowledge who want an intensive, project based bootcamp to quickly gain Python and neural network skills for entry level ML engineer roles.
Key features:
What you'll learn: Python programming from basics to advanced, essential AI libraries (NumPy, Pandas, Matplotlib, PyTorch), linear algebra and calculus foundations for AI and how to design and train deep neural networks , these are the fundamental building blocks of modern AI systems.
Instructor: Udacity community with mentors responds within a day to solve all your issues and answer questions about projects or unclear concepts.
Practical Experience: I developed here Two hands on projects , first using a pre-trained image classifier to identify dog breeds (focusing on Python ML project setup skills) and second building a state-of-the-art image classification application from scratch. i added it in my portfolio.
Proven Outcomes: Graduates report that Udacity helped them to learn job ready skills relevant to the subject and successfully transitioned them into ML engineering roles,with one student stating it helped them land in Google as ML Engineer.
Consideration: What i feel is this course covers only neural networks but indepth also it teaches simple Python basics then jumping to complex 60-step processes involving tons of libraries, making it difficult to follow sometimes.
.
This course is created by IBM experts with Ph.D. level expertise. This program takes approximately two months to complete. That includes a guided project throughout each course, plus one capstone project to demonstrate your AI engineering skills. It covers machine learning, deep learning concepts through hands-on implementation with frameworks like TensorFlow, Keras, and PyTorch. This course primarily helped me in my promotions and the new job opportunities, as it built my foundations in the AI and ML. Also, the certification from IBM helps me a lot during the interview process.
Best for:Mid career professionals looking to excel into AI engineering or data scientists role and want to strengthen their machine learning deployment skills. After spending 15 years in the field, I would say this is ideal for folks who already know coding but need structured, hands-on experience with real ML frameworks.
Key features:
What you'll learn: In this course you will learn to take models from experimentation to deployment. The course covers supervised/unsupervised learning, deep neural networks, computer vision, and even touches on recommender systems. What impressed me most was the Kubernetes and ML pipeline content.IBM actually shows you how to operationalize your models. You will work with real datasets and debug model performance issues.
Instructor: this isn't a bootcamp with dedicated mentors. You get community forums and peer reviews, which can resolve your queries.IBM instructors respond occasionally in forums, but don't expect one-on-one guidance.
Practical Experience: I particularly valued the computer vision project using PyTorch and the NLP classification work. The project made me build an end to end recommendation engine. This project helps me in many job interviews.
Proven Outcomes:After completing this, I successfully transitioned into an AI engineering role (previously I was doing more traditional software development). The certificate carries value because it's IBM-branded and covers enterprise tools. Several people from my batch landed ML engineer positions within 3-6 months. This certificate complements your existing skills.
Consideration: Plan for 3 to 4 months if you are working full time. This course is a good choice for beginners to start their career, in especially MLOps roles.
I did UpGrad PGP course in AI in 2022. And it was the structured course that I needed at that time. It has live classes, mentor support, super active Slack group for communication, and regular assignments on a weekly basis with real capstone projects. The pace is fixed and classes happen according to the schedule. The workload in the classes is more because you need to solve all assignments and project work before timeline. Mentorship and TA were the highlights for me as the instructors are of high quality and well qualified. Some content that is discussed in the classes I feel is 2-3 years old, especially in the MLOps part. And then they provide resume and LinkedIn rewrite program, mock interviews, as well as GitHub portfolio update with projects. It is a little bit costly course as compared to others.
Best for:Data Analytics, engineering and want to break into AI/ML roles. So, this is for people genuinely ready to invest time and money into a structured transformation.
Key features:
What you'll learn: This course is a little bit in the advanced level and it covers Python, supervised and unsupervised learning, deep learning, NLP, computer vision, Agentic AI. I create a recommendation system here, chatbots, and dive into the MLOps concept. And this curriculum also includes Cloud Deployment AWS, which is crucial for real-world applications.
Instructor: You get 1: 1 mentorship sessions (usually 2-4 per month) with industry experinced working professionals. My mentor was an ML lead at a fintech unicorn, we debugged my capstone project and discussed career strategy and he even reviewed my GitHub portfolio.
Practical Experience: Every module has coding assignments with mini projects. You will build at least 12-15 projects throughout the program. The capstone project was good (mine was a computer vision system for retail analytics). You work with real datasets.
Proven Outcomes:upGrad publishes placement stats before joining. what I have seen in my alumni groups. Multiple batchmates transitioned from support roles to ML engineer positions and several got 40-60% salary hikes. One guy went from mechanical engineering to AI research in 18 months.
Consideration: They say 10-15 hours/week, but realistically plan for 15-20 if you want to do well. Live sessions are evening-focused (IST), which is great for India. If you are ready to spend 3-4 lakhs, then you can consider this course.
AI Engineer course by SimpliLearn has industry folks from IBM and Microsoft who share the practical stories from the production environment. While there are some instructors from the academic background, you learn with short pre-recorded videos plus optional weekend live classes where the teachers solve your problems in real time. What's generally unique about this course is its approach. There are industry-experienced guests who visit regularly and give hands-on experience on the projects, which helps in your portfolio.
Best for:Best for people looking for recognized certifications from university. After 15 years in AI/ML, I would say this is suited to working professionals who need to learn at their own pace.
Key features:
What you'll learn: You will learn Python, R, SQL, data visualization, statistics, supervised/unsupervised learning, deep learning (TensorFlow, Keras, PyTorch), NLP, computer vision, reinforcement learning, time series, and even IoT applications. They teach AWS and Azure deployment modules, plus ChatGPT and generative AI content they recently added in the syllabus.
Instructor: Simplilearn's mentorship isn't bad, but it's definitely not better than premium programs. You get access to teaching assistants through discussion forums and can book "expert sessions," but these are somewhat limited maybe 4-6 throughout the entire program depending on your plan.
Practical Experience: Over 25+ hands on projects ranging from beginner to advanced. Early on, you are doing Iris classification. But later projects get interesting. I built a facial recognition system, created a stock price predictor using LSTMs, and developed a sentiment analysis tool for product reviews.
Proven Outcomes:Simplilearn doesn't publish hard placement statistics like upGrad does, which makes me slightly suspicious. From alumni groups and LinkedIn stalking (yeah, I did that), I have seen mixed results.
Consideration: It's best for disciplined learners who want flexibility and multiple credentials without premium program costs. If you need structure, accountability and active mentorship, look elsewhere. If you are self-driven and just need quality resources with some support, this delivers decent ROI.
The instructors are actual AWS Solution Architects and ML specialists. People like Blaine Sundrud and Matt Wood, who build AWS ML infrastructure. So you are getting insight straight from the source, not some random instructor. Classes happen through short videos, around 5 to 10 minutes each. With a demo showing you live AWS console workflow. What's generally unique about this course is AWS first approach. Instead of teaching ML theory and different libraries and tools, they focus on business problems. Here is the specific AWS service that actually solves these business problems. So you got to know the internal architecture of the ML solution using AWS managed services, rather than building everything from scratch.
Best for:Cloud engineers, DevOps folks, or solutions architects who need to understand ML within the AWS ecosystem. This is perfect for people who already live in AWS and need to add ML capabilities to their infrastructure knowledge.
Key features:
What you'll learn: The course covers ML workflows on AWS, data pipeline basics, model training and deployment using SageMaker. This course focuses on cost optimization AI strategies for business problems. You wil understand the difference between AWS's managed AI services and custom model development. What I found valuable was learning about architectural patterns and how to design ML-powered applications using AWS building blocks.
Instructor: No 1:1 sessions, no mentors, no teaching assistants actively monitoring discussions. It's you, the video content from AWS experts and if you need guidance, you'll need to supplement with AWS documentation.
Practical Experience: The labs are demonstrations and walkthroughs but not extensive hands-on projects. You will see AWS instructors showing you how to use services, but you are not necessarily building complex applications yourself during the course.
Proven Outcomes:This is tricky to measure because it's is a foundational course. You are not going to land an "ML Engineer" job based on this 6-hour course, this is not its purpose. What it does do effectively is make you conversational in AWS ML services.
Consideration: perfect for cloud professionals who need to understand what's in the AWS ML toolbox without becoming data scientists. At 6 hours and minimal cost, the ROI is excellent for the right audience.
The instructors are Google Cloud Engineers and AI researchers in this course. People like Lawrence Moroney and folks from Google Brain Team who literally build the tools in Google are actually teaching you in this course. You are getting the designer perspective on TensorFlow, Vertex AI. Classes happen in a mixed mode of lecture videos with whiteboard explaining theory, followed by some Jupyter notebook lab sessions in Google Colab, where you will actually write and run TensorFlow code. Mostly, the focus is on maths behind every machine learning algorithm. So they will teach you the complete explanation before the code. The Colab integration is brilliant. You are coding directly in the browser with pre-configured environment, no local setup required.
Best for:Developers and data professionals who want to understand Google's ML ecosystem, especially if you're already in the Google Cloud world or planning to be be.
Key features:
What you'll learn: You will learn supervised learning fundamentals, how to use TensorFlow and Keras effectively, feature engineering strategies that Google engineers actually use, and the Vertex AI workflow from data prep to model deployment.The curriculum covers model training on Google Cloud, hyperparameter tuning using Vertex AI, ML pipelines with Kubeflow, and how to use pre-trained models from Google's Model Garden.
Instructor:Discussion forums exist, other learners are helpful, but Google engineers aren't personally answering your questions. The course is structured well enough that you shouldn't need much hand-holding if you have basic programming knowledge.
Practical Experience: The labs session are actual hands on coding exercises in Jupyter notebooks, not just watching recording. You will write TensorFlow code, train models on real datasets, deploy to Vertex AI, and see the full workflow in action. Most labs run in Google Colab, which means free GPU access for experimentation.I built a text classification model, trained a computer vision model using transfer learning.
Proven Outcomes:This single course won't make you an ML engineer. It's a foundation. But the practical skills are immediately applicable if you work in GCP. The people who succeed afterward are those who continue building projects, contribute to Kaggle competitions.
Consideration: The free Colab based labs are fantastic in this course, the TensorFlow focus is industry relevant, and you will actually write code rather than just watching videos. This course is perfect for developers who want to understand ML from first principles while learning Google's implementation, but it is challenging if you are new to programming.
The instructor in this course are actual AI product leaders from companies like Google and Amazon and IBM. People likeLuis Serrano (former PM at Google) who developed a real AI product at scale, are taking the classes. The classes happens through small video sessions (5 to 15 minutes of videos) mostly focused on frameworks and case studies. You will develop five comprehensive PM projects that will be reviewed by the experts from the team. You will learn to identify the business problems. You are getting trained to be the translator between business stakeholders, who don't understand AI and engineers, who are mostly doing the data cleaning and deployment.
Best for:Udacity's AI Product Manager Nanodegree is one of the best designed programs for the specific niche of PM. it's neither too technical (like ML engineering courses) nor too easy (like AI awareness workshops).
.
Key features:
What you'll learn: You will learn how to evaluate AI opportunities, scope ML projects realistically (most PMs over-promise here). The curriculum covers the entire AI product lifecycle: identifying use cases, data strategy (what data you need, how to get it, privacy concerns), model selection (when to use supervised vs. unsupervised learning, deep learning vs. traditional ML), creating product and deployment.
Instructor:You get access to mentors through their platform not available always, but you can book sessions when stuck. Response times in the knowledge hub (their Q&A platform) are usually within a few hours, and mentors are typically people with actual PM experience at tech companies.
Practical Experience: You will create a business proposal for an AI product (with full business case and ROI) and build a dataset annotation strategy for a computer vision product and design a conversational AI experience. My favorite project was building a complete product brief for adding AI capabilities to an existing product. You have to justify costs, timeline, team requirements, success metrics, and risk mitigation.
Proven Outcomes:The nanodegree carries decent weight, not as much as an MBA or Stanford degree, but recruiters recognize Udacity certification, especially in tech IT Companies. Within 3-6 months of completing this. One former colleague transitioned from traditional SaaS PM to leading ML product initiatives at a fintech company.
Consideration: The free Colab based labs are fantastic in this course, the TensorFlow focus is industry relevant, and you will actually write code rather than just watching videos. This course is perfect for developers who want to understand ML from first principles while learning Google's implementation, but it is challenging if you are new to programming.
When I chose my AI career, I really wanted something that was much more practical and more reliable. LogicMojo's six-step review process really stood out to me. So, every course here is checked by industry experts, AI professionals, and even beginners to ensure it is easy to learn, practical, and powerful. This gave me the confidence to gain real skills and launch my career in AI technology.
Expert Verified
Beginner Tested
Continuously Updated
Our 3-Step Review Process
1
Discovery & Screening
At LogicMojo, we review, identify, and search the latest handpicked AI courses from the top education platform, mainly focusing on beginner-friendly content, easy to follow, and highly relevant to real-world industry needs. By reviewing each course in detail, we make sure that you will get the best learning experience that will help you build practical skills and confidence to succeed.
2
Expert Analysis
At LogicMojo, every course is reviewed by experienced AI experts with professional experience and educators. We review how accurate the content is, how easy it is to understand, and how well you can apply what you learn in real life. This expert analysis process makes sure that you gain the practical skills with confidence.
3
Beginner Testing
At Logicmojo, we have developed a unique way to review the courses. To make sure our courses are truly beginner-friendly, we have a complete newcomer's test for each program in order to check for accessibility, clarity, and real learning outcomes without the need for any prior experience. This way, we will ensure that every learner, no matter where they start, can easily follow along and succeed.
Our 6 Evaluation Criteria
In our 6 evaluation Criteria, we go through a careful review across key areas from content quality to teaching style and learning effectiveness. This evaluation process will ensure that what you learn is not only accurate but also easy to understand by building a strong foundation to grow your career in AI.
Beginner Accessibility
We carefully test how well complete beginners will be able to understand the basics of AI concepts, machine learning fundamentals, and neural networks. LogicMojo courses have been designed so that you do not need any prior coding or advanced mathematical knowledge. Everything explained here is clear, practical, and beginner-friendly from the very beginning.
Zero PrerequisitesClear Explanations
Hands-On Projects
Your Portfolio starts here! Every course recommended at LogicMojo comes with Hands-On AI Projects, Coding Exercises, and real-world applications. These are not just for your practice; we evaluate and test these projects with expert input. By completing these hands-on projects, you can proudly add them to your professional portfolio, which builds expertise and confidence along with the skills you acquire.
5+ ProjectsPortfolio Ready
Content Depth & Structure
At LogicMojo, we always evaluate the Content of the courses, which should be well-organized and easy to follow for beginners. We follow a step-by-step knowledge path in covering the AI topics, which includes neural networks, deep learning, computer vision, natural language processing, and AI ethics. All these courses have been well structured in a clear and logical order, which helps you to build knowledge.
8 Core TopicsProgressive Learning
Instructor Expertise
We strictly evaluate and select the Instructors to teach the course for you. LogicMojo picks AI experts who are recognized researchers, Industry professionals, and experienced educators with a strong history of proven track records and success. Because of this, you can feel confident about the quality of your learning journey.
PhD/Industry LeaderTeaching Experience
Community & Support
We evaluate how strong our community and support system really are. This will be done by looking at the active learning communities, how responsive instructors are, the quality of the peer collaboration, and the active participation in the Q&A resources. By this evaluation, we ensure that every course truly supports the learners and no student feels left behind.
24h ResponseActive Community
Career Impact & Outcomes
LogicMojo, just do not look at what a course provides; we evaluate the outcomes and success stories of the verified student. From success stories and career transition to job placements, salary growth, and long-term return on investment. Every student's numbers, and every story will reflect your career impact and outcome, where your learning should build the future that you truly deserve.
85% Success RateCareer Tracking
Our Evaluation by the Numbers
Comprehensive data from our rigorous course evaluation process
750+Courses Reviewed
50+Hours Per Course
25+Expert Reviewers
98%Student Satisfaction
Our Ranking System is Trusted By
Featured in leading publications and referenced by universities.
Find Your Perfect AI Course Match For Beginner
Not sure where to start? Our smart quiz looks at your background, goals, budget, and schedule to guide you toward the best AI courses for your situation which truly fits your journey. Takes less than 60 second!
Best AI Courses for Beginners – Detailed Curriculum
Use our interactive explorer to discover the
Top 10 Best AI Courses for Beginners in 2025. Get insights into course content, placement support, success stories, and compare your favorites — all in one place
Filter the course based on your requirements
10 courses found
ACCELERATED LEARNING PATH
AI Roadmap for Beginners in 7 Months
From Python basics to production-ready AI systems. A structured,
hands-on journey designed by industry experts.
Your Learning Timeline
Each module builds on the previous one, creating a comprehensive
foundation for AI mastery.
This helps us gauge the right starting point for you.
🚀
Complete Beginner
No programming or DS experience
💻
Some Programming
Basic Python but new to Data Science
🛠️
Experienced Developer
Strong programming, some DS exposure
What's your primary career goal?
This helps us find a course aligned with your ambitions.
🔄
Career Transition
Switch to Data Science from another field
📈
Skill Enhancement
Add DS skills to your current role
👔
Business Leadership
Lead data-driven decisions in your company
What's your budget range?
We have courses for every investment level.
❤️
Low Budget
Under ₹80,000
💰
Moderate Budget
₹80,000 - ₹2,00,000
💎
Premium Investment
Above ₹2,00,000
How much time can you dedicate weekly?
This helps us match you with a program that fits your schedule.
🕰️
Part-time Learner
2-5 hours/week
🗓️
Weekend Warrior
5-10 hours/week (weekends)
🔥
Full Commitment
20+ hours/week
🏆
Your Personalized Recommendations
Based on your answers, here are the best courses for you:
About the Author
Ravi Singh
I am a Data Science and AI expert with over 15 years of experience in the IT industry. I’ve worked with leading tech giants like Amazon and WalmartLabs as an AI Architect, driving innovation through machine learning, deep learning, and large-scale AI solutions. Passionate about combining technical depth with clear communication, I currently channel my expertise into writing impactful technical content that bridges the gap between cutting-edge AI and real-world applications.
Get answers to the most common questions about AI courses, career transitions, and the learning path from our team of experts.
Which AI course is best for a complete beginner?
Beginner2 min read
Quick Answer
For complete beginners, we recommend three standout options based on different learning preferences and budgets.
🏆
Best Overall for Beginners
LogicMojo's Advanced Data Science & AI
Structured weekend classes perfect for Beginners.
Dual Purdue + IBM certification for strong credibility.
Beginner-friendly curriculum that builds from the ground up.
💰
Best Budget Option
AI for Everyone (Coursera)
Highly affordable monthly subscription model.
Self-paced learning, ideal for students.
Industry-recognized Coursara certification.
How long does it take to become job-ready in AI?
All Levels1 min read
The time to get job-ready depends on your background and the program's intensity. Here's a general guideline for Bangalore's tech market:
For Beginners (No Tech Background)
3-6 months of dedicated learning in a structured bootcamp is typically required to be ready for entry-level Data Analyst or Junior AI Engineer roles.
For Professionals (With Tech Background)
6-8 months in an advanced program is ideal to target mid-level to senior AI/ML Engineer roles, as this allows for a deeper dive into specialized topics and portfolio building.
What salary can I expect after completing a course?
All Levels4 min read
Salaries are competitive and depend on your skills and prior experience. Here are some typical ranges:
Entry-Level (0-2 years exp)
After a foundational course, expect ₹6 LPA to ₹12 LPA at service-based companies and startups.
Mid-Senior Level (3+ years exp)
After an advanced or specialization course, salaries often range from ₹18 LPA to ₹35 LPA+ at top product-based companies.
Do I need a powerful computer for these AI courses?
Tech Specs2 min read
While a high-end gaming PC isn't necessary, a decent modern laptop is recommended. Most of the heavy computation is handled via cloud platforms like Google Colab.
Minimum Recommendations
Processor: Intel i5 (8th Gen or newer) or AMD Ryzen 5
RAM: 8GB (16GB is highly recommended)
Storage: 256GB SSD
Which programming language is commonly used in AI?
Languages3 min read
The most important labguages need to know as AI specialist are listed.
Python: The Language of AI:
Python : is the most popular language for AI due to its easy syntax and powerful libraries
Tensorflow: TensorFlow is a software library for machine learning and artificial intelligence.
Pytorch: pytorch and tensorflow are librairies that let you write differentiable algeabra in python.
Scikit-learn:scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language.
🎓
Which AI course is best for absolute beginners?
Beginner Friendly3 min read
For those with no programming or AI background, we recommend these top-rated courses:
1 Logicmojo AI & ML Course
Designed from the ground up for beginners, with live classes and strong mentorship. Tags: Live Classes, 1:1 Mentorship, Job Support
2 Coursera’s "AI for Everyone"
A non-technical course perfect for understanding AI concepts without coding. Tags: No Coding, Andrew Ng, 4 Weeks
3 Google AI Essentials
A short, practical course on how to use AI tools effectively in your work. Tags: Google Certified, Practical, Quick Start
💡
These courses start with the basics and don’t assume prior technical knowledge.
🧮
Do I need to be a math expert to learn AI?
Math Requirements3 min read
Quick Answer: No, you don't need to be a math expert!
Basic Level: High school algebra and stats (2-4 weeks prep)
Intermediate Level: Linear algebra, probability (1-2 months prep)
Advanced (Optional): Required only for AI research (6+ months)
📚 Most of the math is handled by tools like:
TensorFlow – Neural Networks
PyTorch – Deep Learning
Scikit-learn – Machine Learning
NumPy – Mathematical Computing
🧠
What is the difference between AI, Machine Learning, and Deep Learning?
Core Concepts3 min read
Think of them as nested concepts:
AI: Any system mimicking human intelligence
Machine Learning: A subset of AI that learns from data
Deep Learning: A specialized form of ML using neural networks
Example:
AI: Chess playing program
ML: Spam filter that learns patterns
DL: Voice assistants like Siri
💰
What salary can I expect after completing an AI course?
Career Growth3 min read
Salaries vary based on your role, experience, and geography. Here's a typical progression:
Fresher / AI Trainee: ₹5–9L (0–1 year)
ML Engineer: ₹10–20L (1–3 years)
Senior AI Scientist: ₹25–45L (3–5 years)
AI Architect / Lead: ₹45L – ₹1Cr+ (5+ years)
📈
Career changers often see a salary hike of 150–300% after transitioning into AI roles.
Quora
Reddit
Trustpilot
Medium
switchup
Youtube
Shiksha
GitHub
Linkedin
Glassdoor
Course Report
