Machine Learning, AI and Data Science without programming

Explore how machine learning and big data drive artificial intelligence across manufacturing, healthcare, smart cities, retail, agriculture, and education, including predictive maintenance and adaptive learning for real-world impact.

Examine the dangers of AI, including biased crime prediction, privacy risks, and robotic mishaps that harm people, and discuss the pace of progress toward autonomous weapons and discrimination.

Explore how artificial intelligence will impact your life, jobs, and world, with both opportunities and risks; embrace lifelong learning and creativity to adapt as artificial intelligence advances.

Distinguish machine learning, deep learning, and artificial intelligence, and explain linear and multiple linear regression, neural networks with input, hidden, and output layers, and reinforcement learning.

Compare supervised and unsupervised learning, where supervised predicts outputs from labeled data and unsupervised uses unlabeled data to uncover structure, with clustering and dimensionality reduction for customer segmentation and visualization.

Explore how big data arose from cheap storage and diverse formats, and explain the three v's: volume, variety, and velocity, while highlighting machine learning predictions and data-driven decisions.

Explore data science and data mining to identify trends, applying big data analytics, predictive modeling, and statistics while understanding business knowledge, computer science, and data selection for machine learning.

Explore how the internet of things, machine learning, and artificial intelligence connect devices to the cloud via 5G, enabling processing and self-driving car capabilities in augmented reality and virtual reality.

Explore the singularity concept and how artificial intelligence and supercomputers may surpass human intelligence. Consider Ray Kurzweil's 2045 prediction and AI's potential to improve itself, as echoed by Elon Musk.

Explore the CRISP-DM framework for data mining and its iterative, data-centered approach. Prioritize business understanding, data understanding, and data preparation to ensure data quality and reliable model performance.

Master data preparation as core of machine learning, coordinating data selection, cleaning, integration, feature extraction. Normalize data, one-hot encode categorical variables, handle missing values and outliers, and apply dimensionality reduction.

Explore creating a machine learning model and comparing linear regression, decision trees, and deep neural networks, including how trees handle missing value and outliers.

Understand model evaluation through the training set, validation set, and test set. Compare multiple models, guard against overfitting, and use metrics like accuracy and AUC before deploying in real apps.

Explore how to advance in machine learning without coding, using cloud models for speech, vision, and translation, and learn what it takes to become a data scientist.

Celebrate completing the machine learning, ai and data science without programming course and reflect on the potential while inviting feedback and requests for topics not yet covered.