AI/ML Fundamentals & Online Courses

AI/ML Fundamentals

Key AI/ML fundamental concepts and techniques that you will need to understand are:

1. Supervised learning: This is a type of machine learning where the model is trained using labeled data with the goal of making predictions on new unseen data. The model learns to map inputs to outputs based on the training data.

2. Unsupervised learning: This is a type of machine learning where the model is trained using unlabeled data with the goal of discovering patterns or structure in the data.

3. Neural networks: These are a type of machine learning model that are inspired by the structure and function of the human brain. They consist of layers of interconnected nodes that process input data and generate output predictions.

4. Deep learning: This is a subset of machine learning that uses deep neural networks with many layers. Deep learning has been particularly successful in areas such as computer vision and natural language processing.

5. Data preprocessing: This involves preparing the data for use in machine learning models. This can include tasks such as cleaning the data, removing outliers, and normalizing the data.

6. Model evaluation: Once a machine learning model has been trained, it is important to evaluate its performance on new, unseen data. This involves metrics such as accuracy, precision, and recall.

7. Model selection: There are many different machine learning algorithms and models to choose from and it is important to select the one that is best suited for your specific problem and data.

Online Courses 

Online courses and tutorials can be a great resource for learning AI/ML fundamentals. Here are some popular and highly recommended options:

1. Machine Learning by Andrew Ng on Coursera: This is one of the most popular machine learning courses available online. It covers a wide range of topics from linear regression to neural networks and includes programming assignments to help you apply what you've learned.

2. Introduction to Machine Learning with Python by DataCamp: This course covers the basics of machine learning using Python and popular libraries like scikit-learn and pandas. It includes hands-on exercises to help you apply what you've learned.

3. Applied Data Science with Python by Coursera: This series of courses covers a wide range of topics in data science including data visualization, machine learning, and text mining. It includes programming assignments and a final capstone project.

4. Neural Networks and Deep Learning by deeplearning.ai on Coursera: This course covers the fundamentals of neural networks and deep learning and includes hands-on assignments using TensorFlow and Keras.

5. Machine Learning Crash Course by Google: This is a free online course that covers the basics of machine learning including supervised and unsupervised learning and interactive coding exercises using TensorFlow.

6. Practical Deep Learning for Coders by fast.ai: This is a course that focuses on teaching practical skills for working with deep learning models. It covers topics such as convolutional neural networks, recurrent neural networks, and natural language processing.