Learn AI in 2 months

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

Day 1:

Review Python basics: variables, loops, functions, and conditional statements.

Video: https://www.youtube.com/watch?v=rfscVS0vtbw

Day 2:

Learn about Python libraries for AI: NumPy, pandas, and matplotlib.

Video: https://www.youtube.com/watch?v=QUT1VHiLmmI

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

Day 3:

Refresh linear algebra concepts: vectors, matrices, and operations.

Video: https://www.youtube.com/watch?v=kjBOesZCoqc

Article: https://www.analyticsvidhya.com/blog/2017/05/comprehensive-guide-to-linear-algebra/

Day 4:

Refresh calculus concepts: differentiation, integration, and partial derivatives.

Video: https://www.youtube.com/watch?v=WUvTyaaNkzM

Article: https://towardsdatascience.com/essential-calculus-for-machine-learning-8e53915f2f7d

Day 5:

Refresh probability and statistics concepts: probability distributions, descriptive statistics, and inferential statistics.

Video: https://www.youtube.com/watch?v=IYU1yE8jT_A

Article: https://towardsdatascience.com/probability-and-statistics-for-data-science-940035e5d5f5

Week 2: Introduction to Machine Learning

Day 6:

Learn the basics of machine learning, types of learning, and model evaluation metrics.

Video: https://www.youtube.com/watch?v=Gv9_4yMHFhI

Article: https://towardsdatascience.com/introduction-to-machine-learning-672d7b6619dd

Day 7:

Learn about supervised learning algorithms: linear regression and logistic regression.

Video: https://www.youtube.com/watch?v=zPG4NjIkCjc

Article: https://towardsdatascience.com/introduction-to-supervised-learning-algorithms-f9e8959f24d1

Week 3: Supervised Learning Algorithms

Day 8–12:

Study the following supervised learning algorithms and their implementation in Python using scikit-learn:

k-Nearest Neighbors

Decision Trees

Support Vector Machines

Random Forests

Gradient Boosting Machines

Article: https://towardsdatascience.com/10-algorithms-to-solve-before-starting-your-machine-learning-career-954f5f80a5de

Video playlist: https://www.youtube.com/playlist?list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E

Week 4: Unsupervised Learning Algorithms and Model Evaluation

Day 13–16:

Study unsupervised learning algorithms: k-Means, hierarchical clustering, and DBSCAN.

Video: https://www.youtube.com/watch?v=05ONoGfmKvA

Article: https://towardsdatascience.com/unsupervised-learning-with-python-173c51dc7f03

Day 17–18:

Learn about model evaluation techniques: cross-validation, confusion matrix, and ROC curves.

Video: https://www.youtube.com/watch?v=85dtiMz9tSo

Article: https://towardsdatascience.com/model-evaluation-techniques-for-classification-models-6dca5ba5f5f

Day 19:

Practice implementing machine learning algorithms and evaluation techniques on real-world datasets.

Dataset resources: https://archive.ics.uci.edu/ml/index.php, https://www.kaggle.com/datasets

Week 5: Introduction to Deep Learning and Neural Networks

Day 20:

Learn the basics of deep learning and neural networks.

Video: https://www.youtube.com/watch?v=aircAruvnKk

Article: https://towardsdatascience.com/introduction-to-deep-learning-and-neural-networks-9d7720d0a556

Day 21–22:

Study the structure of feedforward neural networks and backpropagation.

Video: https://www.youtube.com/watch?v=Ilg3gGewQ5U

Article: https://towardsdatascience.com/understanding-feedforward-neural-networks-6f49aa44e49c

Week 6: Deep Learning Frameworks and Convolutional Neural Networks

Day 23–24:

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/

Day 25–27:

Learn about convolutional neural networks (CNNs) and their applications.

Video: https://www.youtube.com/watch?v=YRhxdVk_sIs

Article: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53

Week 7: Recurrent Neural Networks and Natural Language Processing

Day 28–30:

Study recurrent neural networks (RNNs), LSTM, and GRU.

Video: https://www.youtube.com/watch?v=WCUNPb-5EYI

Article: https://towardsdatascience.com/understanding-recurrent-neural-networks-rnns-f7c46c8d5bc7

Day 31–33:

Get started with natural language processing (NLP) using Python and NLTK.

Video: https://www.youtube.com/watch?v=X2vAabgKiuM

NLTK tutorial: https://www.nltk.org/book/

Week 8: Reinforcement Learning and AI Ethics

Day 34–36:

Learn about reinforcement learning (RL) and its applications.

Video: https://www.youtube.com/watch?v=2pWv7GOvuf0

Article: https://towardsdatascience.com/introduction-to-reinforcement-learning-rl-part-1-a7d5d5f547ea

Day 37–38:

Study AI ethics, fairness, and responsible AI development.

Video: https://www.youtube.com/watch?v=ZjjTdFZgjS4

Article: https://towardsdatascience.com/ethics-in-artificial-intelligence-eeaaf3a94e9e

Day 39–40:

Review the topics covered and practice implementing AI algorithms on real-world projects.

Project ideas: https://medium.com/@ODSC/20-ai-machine-learning-and-deep-learning-projects-for-beginners-867b84922e16

Dataset resources: https://archive.ics.uci.edu/ml/index.php, https://www.kaggle.com/datasets

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.