Machine Learning for Beginners (ML for Beginners)

Topics covered

• •Understand what machine learning is and how it relates to AI, deep learning, and data science.
• •Learn the key milestones in ML history and the lessons they teach us.
• •Recognize the challenges of fairness and responsible AI.

Topics covered

• •Get comfortable with the tools: Jupyter notebooks, Pandas, Matplotlib, Scikit-learn.
• •Learn to clean and visualize data before training a model.
• •Build linear, polynomial, and logistic regression, and evaluate their quality.

Topics covered

• •Understand how classification differs from regression, and the types of classification (binary, multiclass).
• •Master the core Scikit-learn classifiers: logistic regression, KNN, SVM, decision trees, and ensembles.
• •Learn to compare models using metrics and pick the right algorithm for the task.

Topics covered

• •Understand the essence of unsupervised learning and how it differs from supervised learning.
• •Master the K-Means algorithm and ways to choose the number of clusters.
• •Learn to prepare and visualize data for clustering.

Topics covered

• •Understand the goals and history of NLP: from the Turing Test and ELIZA to modern approaches.
• •Master basic text operations: tokenization, part-of-speech tagging, phrase extraction, n-grams.
• •Learn to detect the sentiment of text and apply translation using library tools.

Topics covered

• •Understand what makes time series data special and how it differs from "regular" tabular data.
• •Learn the concepts of trend, seasonality, stationarity and autocorrelation.
• •Learn to build forecasts with the ARIMA model and with SVR.

Topics covered

• •Understand the reinforcement learning paradigm: agent, environment, states, actions, rewards.
• •Master Q-Learning: the Q-table, the Bellman equation, and the exploration/exploitation balance.
• •Learn to work with standard environments (Gym/CartPole) and continuous state spaces.

Topics covered

• •See how the techniques you've learned are applied across real industries.
• •Learn to map a type of business problem to a class of ML methods.
• •Master the basics of debugging and auditing models from a responsible AI perspective.