1. Machine Learning for Beginner’s
This course is designed to provide a comprehensive roadmap to mastering machine learning in 2025. It covers essential skills, foundational concepts, and practical techniques required for a career in machine learning. The course is ideal for those starting their journey, offering both theoretical and hands-on experience through case studies and practical applications.
Key Highlights of the Course:
Foundational Concepts and Skills
- Understand the must-have skills for a career in machine learning, including basics like the bias-variance trade-off, overfitting, and regularization.
- Gain insights into common career paths in machine learning.
Regression Models and Case Studies
- Explore the statistical and theoretical foundations of linear and logistic regression.
- Work on a practical case study using linear regression to solidify your understanding.
Data Preparation and Analysis
- Learn how to load, explore, clean, and preprocess data, including techniques like InterQuartileRange for outlier detection and correlation analysis.
- Practice data splitting using sklearn and descriptive statistics for better visualization and analysis.
Hands-on Application and Evaluation
- Perform linear regression for both causal analysis and predictive analytics.
- Evaluate models by checking OLS assumptions and refining predictions.
Course Link
2. Machine Learning Crash Course by Google
Since its launch in 2018, the Machine Learning Crash Course (MLCC) has helped millions worldwide grasp the fundamentals of machine learning and its applications. The updated version introduces recent advances in AI and places a greater emphasis on interactive learning experiences. Watch the introduction video to discover the exciting enhancements in the revamped MLCC!
https://developers.google.com/machine-learning/crash-course
Core Machine Learning Fundamentals
- Understand the foundational concepts of machine learning, including supervised and unsupervised learning.
Recent AI Advances
- Explore the latest developments in AI, integrated into the course to reflect the current landscape.
Interactive Learning
- Engage with hands-on exercises, quizzes, and real-world scenarios to solidify your understanding.
Practical Applications
- Learn how to apply machine learning techniques to solve problems in various industries.
3. Machine Learning in Python with Scikit-learn
This online course, hosted on the FUN platform, provides a hands-on introduction to machine learning using the scikit-learn library. Developed and delivered by the creators and core developers of scikit-learn, the course aims to make machine learning accessible to anyone with basic Python skills. It offers practical insights into the strengths and limitations of machine learning, empowering learners to apply these techniques effectively in real-world scenarios.
Key Highlights:
- Expert-Led Learning
- Gain knowledge directly from the authors and developers of the scikit-learn library.
- Accessible Content
- Designed for learners with basic Python programming skills, making machine learning approachable for beginners.
- Practical Understanding
- Develop a clear grasp of both the capabilities and limitations of machine learning through hands-on exercises and real-world examples.
- Comprehensive Resources
- Access a variety of materials, including tutorials, projects, and quizzes, to reinforce your learning journey.
4. Building Systems with the ChatGPT API
This short course from DeepLearning.AI introduces practical techniques to automate workflows using the ChatGPT API. Taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI), the course enables learners to build and integrate advanced language model capabilities into real-world systems. Designed for efficiency, this one-hour course includes hands-on examples and interactive Jupyter notebooks to help you experiment with the concepts.
Key Highlights:
- Automate Complex Workflows
- Learn to chain prompts that build upon previous completions and integrate Python code for dynamic interactions.
- Build Real-World Systems
- Develop a customer service chatbot and apply techniques like multi-step reasoning, safety evaluation, and query classification.
- Hands-On Practice
- Experiment with built-in Jupyter notebooks to explore coding examples and prompts seamlessly.
- Expert Instruction
- Benefit from guidance by AI pioneers Isa Fulford and Andrew Ng, building on concepts from the popular ChatGPT Prompt Engineering for Developers.
5.LangChain for LLM Application Development
This short course from DeepLearning.AI equips learners with the skills to leverage the LangChain framework for developing advanced language model applications. Taught by LangChain creator Harrison Chase and Andrew Ng, this one-hour course offers a hands-on approach to expanding LLM use cases and creating robust applications in a short time.
Key Highlights:
- Master Core LLM Concepts
- Learn how to call LLMs, craft effective prompts, and parse responses for practical applications.
- Leverage Memories
- Explore how to use memory features to store conversations and manage limited context efficiently.
- Build Chains for Automation
- Create sequences of operations to automate and enhance complex workflows.
- Apply LLMs to Proprietary Data
- Implement question-answering systems tailored to your specific data and use case requirements.
- Develop Reasoning Agents
- Discover the potential of LLMs as reasoning agents to handle advanced tasks.
6. Natural Language Processing (NLP) with Hugging Face
This free, ad-free course teaches natural language processing (NLP) using Hugging Face libraries — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, 🤗 Accelerate — and the Hugging Face Hub. Designed for developers with basic Python knowledge, the course covers everything from foundational concepts to advanced applications of Transformer models.
Key Highlights:
- Learn Transformer Fundamentals
- Understand how Transformer models work, use pre-trained models from the Hugging Face Hub, fine-tune them on datasets, and share results.
- Tackle NLP Tasks
- Master essential NLP tools like 🤗 Datasets and 🤗 Tokenizers to address common NLP challenges with confidence.
- Explore Beyond NLP
- Discover how to use Transformer models for speech processing, computer vision, and building optimized, production-ready demos.
- Accessible Learning Path
- No prior knowledge of PyTorch or TensorFlow required, though familiarity helps. It’s ideal for learners who have completed an introductory deep learning course.
- Comprehensive Coverage
- The course spans classic NLP problems and cutting-edge Transformer applications, preparing you to handle nearly any ML task with Hugging Face tools.
7. Machine Learning Specialization (Coursera)
#BreakIntoAI with the Machine Learning Specialization, a beginner-friendly, 3-course program led by AI pioneer Andrew Ng. This program equips learners with fundamental AI concepts and hands-on machine learning skills, offering a comprehensive introduction to the field of AI and ML.
Key Highlights:
- Foundational Machine Learning Models
- Learn to build models using NumPy and scikit-learn, and train supervised models for prediction and binary classification, such as linear and logistic regression.
- Neural Networks and Deep Learning
- Develop and train neural networks with TensorFlow for multi-class classification and explore decision trees and ensemble methods.
- Unsupervised Learning Techniques
- Master clustering and anomaly detection while applying best practices for ML development.
- Advanced Applications
- Build recommender systems using collaborative filtering and deep learning approaches, and create a deep reinforcement learning model.