Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies that are revolutionizing various industries. So In this blog, we present a collection of insightful courses on AI and ML that cater to beginners and individuals with no prior coding experience. These courses offer a broad introduction to modern machine learning, starting from the basics and gradually building up practical expertise. Whether you are a non-technical individual or an engineer, these courses provide a solid foundation for understanding key AI concepts and developing practical skills

1. AI for everyone by Andrew Ng on Coursera

Level: Beginner

Duration: About 10 hours

Fee: Free to audit, Upgrade for certificates with Financial aid available

What you’ll learn: Workflow of Machine Learning projects, AI terminology, AI strategy, Workflow of Data Science projects

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About the course: This course is one of my favorite as it is taught by one of the best industry expert in the field of AI. In this course, you'll gain knowledge on various AI concepts, such as neural networks, machine learning, deep learning, and data science, along with their underlying meanings. You'll understand the practical capabilities and limitations of AI, enabling you to identify potential areas for applying AI solutions within your organization. Moreover, you'll delve into the experience of developing machine learning and data science projects.

Furthermore, the course emphasizes the importance of teamwork and provides insights into building an effective AI strategy within your company. It also addresses the ethical and societal dimensions associated with AI, fostering a comprehensive understanding of its broader impact.

While primarily designed for non-technical individuals, engineers can also benefit from taking this course, as it offers valuable insights into the business aspects of AI.

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2. Artificial Intelligence A-Z™ 2023: Build an AI with ChatGPT4 by Udemy

Level: Beginner

Duration: About 17 hours

Fee: Paid

What you’ll learn: Build an AI, Make a virtual Self Driving cars, Q-learning, Deep Convolutional Q-Learning, Make an AI to beat games, Deep Q-Learning, A3C

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About the course: In this course you’ll quickly gain key AI concepts and coding intuition using Python, even without prior coding experience. You’ll Merge AI with OpenAI Gym for effective learning and optimize AI for real-world applications. This course offers complete beginner to expert AI skills, hands-on coding from scratch, downloadable code templates, intuition tutorials, and real-world problem-solving with 3 practical projects. The good thing is, no dense theory, just a deep understanding for better results in building adaptable AI. If you get a good deal you should start practicing and unlock unlimited potential.

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3. Generative AI by Google

Level: Beginner

Duration: Approx 10 days (1-2 hours/day)

Fee: Free to access content but few course may require subscription/credit

What you’ll learn: Introduction to Generative AI, LLMs, Responsible AI,AI Fundamentals, Image generation, Encoder-Decoder generator, Attention mechanism, Transformer models and BERT, Image captioning models, Generative AI studio

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About the course: Google introduced a learning path on generative AI products and technologies on Google Cloud, covering the fundamentals, use cases, and techniques of generative AI. The learning path includes:

1. Introduction to Generative AI, explaining what generative AI is, how it differs from traditional machine learning, and how to use prompt tuning to enhance large language models (LLM).
2. Introduction to Large Language Models, exploring the benefits and applications of LLM for natural language processing and generation.
3. Introduction to Responsible AI, defining what responsible AI is, why it is important, and how Google implements it in their products, using their 7 AI principles.
4. Diffusion Models, introducing a family of machine learning models inspired by physics that can generate realistic images.
5. Encoder-Decoder Architecture, describing the main components and advantages of the encoder-decoder architecture for sequence-to-sequence tasks.
6. Attention Mechanism, learning how attention works and how it can be used to improve neural networks for image generation.
7. Transformer Architecture and BERT Model, learning about the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model, and how they can be used for natural language processing and generation.
8. Image Captioning, teaching how to create an image captioning model using deep learning, and how to train and evaluate it.
9. Generative AI Studio, introducing a product on Vertex AI that helps users prototype and customize generative AI models for their applications.

4.  Data Science: Machine Learning by Harvard University on edx

Level: Beginner

Duration: 8 weeks (2-4/week)

Fee: Free to audit, upgrade for certificate

What you’ll learn: Basic ML, Cross Validation, Popular ML algorithms, Regularization etc.

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About the course: This course explores popular data science methodologies, focusing on machine learning. Unlike other decision processes, machine learning utilizes data to build prediction algorithms. You'll delve into various machine learning algorithms, principal component analysis, and regularization while constructing a movie recommendation system. Learn to identify potentially predictive relationships from training data and apply algorithms to make predictions for future datasets. Additionally, understand essential concepts like overtraining and how to prevent it through techniques like cross-validation. Master these fundamental skills in machine learning.

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5. Machine Learning Specialization by Andrew Ng on Coursera

Level: Beginner

Duration: About 3 months (9 hours/week)

Fee:  Free to audit, Upgrade for certificates with Financial aid available

What you’ll learn: Decision Trees, Artificial Neural Network, Logistic Regression, Recommender Systems, Linear Regression, Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Logistic Regression for Classification, Xgboost, Tensorflow, Tree Ensemble

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About the course: Another great course series by Professor Andrew, This Machine learning specialization consists of 3 courses namely

1. Supervised Machine Learning: Regression and Classification

2. Advanced Learning Algorithms

3. Unsupervised Learning, Recommenders, Reinforcement Learning.

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This comprehensive Specialization offers a modern introduction to machine learning, covering supervised (linear regression, logistic regression, neural networks, decision trees) and unsupervised learning (clustering, dimensionality reduction, recommender systems). Learn Silicon Valley's best practices for AI and ML innovation, including model evaluation, tuning, and data-centric approaches to improve performance. By completion, you'll master key concepts and gain practical expertise to effectively apply machine learning to real-world challenges. Whether starting an AI journey or pursuing a machine learning career, this Specialization is the ideal starting point.

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Conclusion

Whether you are a non-technical individual seeking to comprehend AI's business aspects or an engineer aspiring to build cutting-edge AI models, these courses offer a diverse range of opportunities for growth. With continuous learning, practice, and exploration of advanced topics, learners can advance their expertise and embark on a fulfilling journey in the world of AI and ML.

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