I asked ChatGPT for a training plan for someone that I know. He wanted to learn AI programming over a 2-month period. Here is a detailed plan generated by ChatGPT and it’s pretty good — something that most people can use.
Given that the person has basic Python programming experience and decent math knowledge, a 2-month detailed day-wise plan to get stronger in AI can be structured as follows:
Week 1: Python and Math Refresher
Review Python basics: variables, loops, functions, and conditional statements.
Learn about Python libraries for AI: NumPy, pandas, and matplotlib.
NumPy tutorial: https://numpy.org/doc/stable/user/quickstart.html
pandas tutorial: https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html
matplotlib tutorial: https://matplotlib.org/stable/tutorials/introductory/pyplot.html
Refresh linear algebra concepts: vectors, matrices, and operations.
Refresh calculus concepts: differentiation, integration, and partial derivatives.
Refresh probability and statistics concepts: probability distributions, descriptive statistics, and inferential statistics.
Week 2: Introduction to Machine Learning
Learn the basics of machine learning, types of learning, and model evaluation metrics.
Learn about supervised learning algorithms: linear regression and logistic regression.
Week 3: Supervised Learning Algorithms
Study the following supervised learning algorithms and their implementation in Python using scikit-learn:
Support Vector Machines
Gradient Boosting Machines
Week 4: Unsupervised Learning Algorithms and Model Evaluation
Study unsupervised learning algorithms: k-Means, hierarchical clustering, and DBSCAN.
Learn about model evaluation techniques: cross-validation, confusion matrix, and ROC curves.
Practice implementing machine learning algorithms and evaluation techniques on real-world datasets.
Week 5: Introduction to Deep Learning and Neural Networks
Learn the basics of deep learning and neural networks.
Study the structure of feedforward neural networks and backpropagation.
Week 6: Deep Learning Frameworks and Convolutional Neural Networks
Get started with TensorFlow and Keras for deep learning.
TensorFlow tutorial: https://www.tensorflow.org/tutorials/quickstart/beginner
Keras tutorial: https://keras.io/guides/sequential_model/
Learn about convolutional neural networks (CNNs) and their applications.
Week 7: Recurrent Neural Networks and Natural Language Processing
Study recurrent neural networks (RNNs), LSTM, and GRU.
Get started with natural language processing (NLP) using Python and NLTK.
NLTK tutorial: https://www.nltk.org/book/
Week 8: Reinforcement Learning and AI Ethics
Learn about reinforcement learning (RL) and its applications.
Study AI ethics, fairness, and responsible AI development.
Review the topics covered and practice implementing AI algorithms on real-world projects.
After completing this 2-month plan, the person should have a solid understanding of various AI concepts and techniques, including machine learning, deep learning, and reinforcement learning. To continue advancing their AI skills, they can explore more advanced topics, such as generative adversarial networks (GANs), transformer models, and reinforcement learning algorithms like Q-learning and policy gradients. Engaging in real-world projects, participating in online competitions, and staying up-to-date with AI research and development will also help them further strengthen their AI expertise.
Of course, some people will say this plan is too simplistic and you cannot learn a topic like linear regression in just 2 days. I agree — this plan is not the be-all and end-all of it.
- It assumes you are learning only 5 days a week whereas most people will be finding time only on weekends. Accordingly, the plan would have to be extended.
- This plan can be extended for a 6-month period also by either asking GPT for that time period or by simply extending the given timeline. The links, articles, etc. are the key resources that can be reused.