Introduction : Generative AI is a revolutionizing technology in 2024, and it's not just the future but it's the present of humanity to discover untapped potential And opportunities. Professionals draw the strength of generative AI to enhance their performance and improve efficiency. Big corporations start adopting the technology and spend thousands of dollars to leverage its benefits. Start your exciting journey to learn generative AI with us and become a master in this highly demanding field. Building a career in artificial intelligence is very promising and This roadmap is your gateway to becoming an expert in this field, our platform offers the best AI online courses free!
What is Generative AI? Generative AI is a part of AI technology. It is proficient in creating new content like text, images, videos, and graphics in a second with the help of generative models. Generative AI Models were capable of learning itself and replicating human behavior. it learns from the designs and architectures of its input data and generates new data with corresponding attributes. It uses developed machine learning techniques, like deep learning, to analyze and produce imaginative and relatedly applicable content.
What is the Difference Between AI and Generative AI? The difference between AI and Generative AI is that traditional AI is expert in analyzing data and performing specific tasks like spam filtering and medical diagnosis, although generative AI works on creating new content like text, images, and music. Both AI types have different strengths and limitations and are probable to be used together in the future for problem-solving, prediction, and content generation and have the potential to create new capabilities and value for human life.
Job Opportunity After Learning Generative AI After learning artificial intelligence and generative AI, let's dive into the top job opportunities.
1. AI Software Developer AI software developers use Python programming to combine AI algorithms into applications. They develop AI-driven tools, and systems like chatbots, virtual assistants, and other intelligent systems.
2. AI Research Scientist The main responsibility of AI research scientists is designing, creating, and advancing AI through research. They specialize in machine learning and develop new infrastructures to open up new possibilities for AI systems to connect with humanity.
3. Data Scientist Data Scientists are high-level analytical thinkers, they extract huge amounts of data from various data sources and analyze it to understand valuable insights. they use different statistical techniques, machine learning, and data visualization tools to inform decision-making.
4. Robotics Engineer Robotics engineers build and maintain different types of AI-powered robots, Develop new applications, and Research to increase robotics opportunities.
5. Machine Learning Engineer The responsibility of a Machine learning engineer is to research, design, and implement algorithms that promote machines to learn. They create systems for tasks like natural language processing, image recognition, etc
6. Natural Language Processing (NLP) Engineer NLP engineers build systems that can recognize different speech patterns, and human languages so that people effortlessly transmit with machines and technology. They worked on developing new language translation tools, applications, and chatbots.
7. Generative AI Educator/Trainer Generative AI experts can promote training programs and work as instructors or trainers to help others learn about Generative AI technologies.
Generative AI Learning Roadmap To learn Generative AI our platform offers the principal guidance you need without spending a dime with this extensive roadmap and the best AI courses online free!
1. Learn Python Programming Be proficient in Python programming, it’s an essential language for AI development.
2. Learn Statistics and Data Analysis To learn Generative AI, you must Understand statistical concepts, credibility, and data analysis. These build the foundation for AI algorithms and models.
3. Understand Machine Learning Dive deeper into machine learning. Learn the basics of supervised and unsupervised learning, scikit-learn, XGBoost, random forests, linear regression, logistic regression, etc.
4. Dive deeper into Natural Language Processing (NLP) Natural Language Processing (NLP) is essential for chatbots, language translation, and sentiment analysis. study different libraries like NLTK and spaCy.
5. Explore Neural Networks and Deep Learning Explore neural networks, backpropagation, and deep learning structures (e.g., CNNs and RNNs). Learn TensorFlow and PyTorch are essential tools for Generative AI Technology.
6. Explore Generative AI Now you know all the essential hypotheses, it’s time to explore Generative AI tools, models, and basic concepts, like style transfer, text-to-image synthesis, image generation, etc. Learn VAEs, GANs, and prominent Gen AI models.
7. Build a Network with the Gen AI Community Build a Network with the Gen AI Community to stay updated in this field, Read research papers, and newsletters, follow AI ML conferences and seminars, and connect with professionals.
Learning Path by Different Free Courses Buckle up for the fascinating world of generative AI without breaking the bank, visit TopFreeCourse to learn AI for free. Enhance your knowledge with the top-notch AI and ML courses to suit your pace.
Description : This is an introductory-level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine-learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
Description : This free Python course is designed for people with some programming experience. It provides written materials, videos, and code exercises to practice Python coding. The course covers basic Python concepts like strings and lists and progresses to more advanced topics like working with text files and HTTP connections.
Description : In this course, you'll discover how to answer questions like these as you grow your statistical skills and learn how to calculate averages, use scatterplots to show the relationship between numeric values, and calculate the correlation. You'll also tackle probability, the backbone of statistical reasoning, and learn how to use Python to conduct a well-designed study to draw your own conclusions from data.
Description : In the Machine Learning with Python Course, you'll use the TensorFlow framework to build several neural networks and explore more advanced techniques like natural language processing and reinforcement learning. You'll also dive into neural networks, and learn the principles behind how deep, recurrent, and convolutional neural networks work.
Description : In this free NLP course, you'll learn the fundamentals of Natural Language Processing (NLP) and Python, covering data pre-processing, tokenization, stemming, lemmatization, and stopwords. Engage in hands-on sessions implementing these techniques in Python. Explore models like Bag of Words and TF-IDF, understand word embedding, and delve into Machine Learning, logistic regression, and sentiment analysis, including a TextBlob demo. Conclude with insights into U-Net, semantic segmentation, and their demonstrations. Enroll, complete the quiz, and earn a certificate, taking a step towards mastering NLP. Explore Great Learning’s Best AI Courses for more on emerging technologies.
Description : You will learn the foundational concepts of neural networks and deep learning, understand the key technological trends in deep learning, build and apply fully connected deep neural networks, implement efficient (vectorized) neural networks, identify crucial parameters in neural network architecture, and successfully apply deep learning principles to your own applications. This course serves as a gateway to comprehending the capabilities and challenges of deep learning, providing you with the knowledge and skills to contribute to cutting-edge AI technology, advance your technical career, and confidently navigate the world of machine learning.
Description : In this course, you will learn to identify the components of a generative AI application and how to customize a foundation model (FM). Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs. Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications. Describe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embedding models, document loaders, retrievers, and Agents for Amazon Bedrock.
Conclusion AI With the revolution of AI technology in this era, Generative AI gives grounds to the whole ecological system, from hardware to software, and small to big companies and organizations adopting generative AI to leverage its benefits and to handle this they would want professionals in this field and pursuing a career in generative AI is very advantageous. To become a generative AI professional, this comprehensive roadmap leads your learning journey. Our platform provides the best AI courses online for free to enhance your skills and prepare you for the next step. Start your learning adventure today, for Free!
