\nBlock 2 hours this week for Anthropic Academy’s "Claude 101."<\/strong> Don’t "find time" \u2014 book it in your calendar like a meeting. This single course gets you from zero to productive with an AI tool. When your CEO asks "what have you tried?", you’ll have a real answer.<\/p>\n<\/li>\n\nSubscribe to exactly 3 newsletters: The Neuron (daily headlines), Simon Willison (practitioner insights), and one specialist.<\/strong> If you’re in demand planning, pick Konrad Banachewicz. If you’re making AI strategy decisions, pick Gary Marcus. If you want technical depth, pick Sebastian Raschka. Three is enough. More than three and you’ll drown.<\/p>\n<\/li>\n\nAdd one AI podcast to your commute rotation.<\/strong> Practical AI if you want breadth. Latent Space if you want depth. NVIDIA AI Podcast if you want supply chain\u2013adjacent applications. Listen to three episodes, then decide if it stays in your rotation.<\/p>\n<\/li>\n\nFind your nearest Clauders meetup at clauders.com and register.<\/strong> In-person learning is 10x more effective than solo study for building practical AI skills. If there’s no meetup in your city yet, consider starting one \u2014 the Clauders community provides all the resources you need to organize.<\/p>\n<\/li>\n\nPick one real work problem and apply AI to it this week.<\/strong> Not a toy problem \u2014 a real one. Summarize a long supplier contract. Draft an RFQ response. Analyze a demand forecast. The gap between "I took a course" and "I used AI at work" is where the actual learning happens. The R code below generates visualizations to help you map out which resources match your learning goals.<\/p>\n<\/li>\n<\/ol>\n\nShow R Code<\/strong><\/summary>\n# =============================================================================\n# AI Learning Guide \u2014 Image Generation\n# =============================================================================\n# Generates 3 visualizations for the "Best Free Places to Learn AI" post.\n#\n# Required packages: ggplot2, dplyr, tidyr, scales, patchwork, ggrepel\n# Output: Images\/ai_learn_*.png (800px wide, white background)\n# =============================================================================\n\nlibrary(ggplot2)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(scales)\nlibrary(patchwork)\nlibrary(ggrepel)\n\n# --- Theme for all plots ---\ntheme_ai_learn <- theme_minimal(base_size = 13) +\n theme(\n plot.title = element_text(face = "bold", size = 14),\n plot.subtitle = element_text(color = "grey40", size = 11),\n panel.grid.minor = element_blank(),\n legend.position = "bottom"\n )\n\n# --- Color palette (fresh, modern "learning" theme) ---\ncol_academy <- "#0891b2" # Teal\ncol_course <- "#7c3aed" # Purple\ncol_podcast <- "#ea580c" # Orange\ncol_blog <- "#16a34a" # Green\ncol_community <- "#db2777" # Pink\n\ntype_colors <- c(\n "Academy" = col_academy,\n "Course" = col_course,\n "Podcast" = col_podcast,\n "Blog" = col_blog,\n "Community" = col_community\n)\n\n# =============================================================================\n# DATA \u2014 AI Learning Resources\n# =============================================================================\n\nresources <- data.frame(\n name = c(\n "Anthropic Academy", "OpenAI Academy", "Google AI\/Skills", "Microsoft Learn",\n "DeepLearning.AI", "fast.ai", "Hugging Face Learn", "MIT 6.S191",\n "Stanford CS229", "Karpathy Zero to Hero",\n "Latent Space", "Lex Fridman", "NVIDIA AI Podcast", "Practical AI",\n "Last Week in AI", "Cognitive Revolution",\n "Sebastian Raschka", "The Neuron", "Gary Marcus", "Simon Willison", "SemiAnalysis",\n "Clauders"\n ),\n type = c(\n "Academy", "Academy", "Academy", "Academy",\n "Course", "Course", "Course", "Course", "Course", "Course",\n "Podcast", "Podcast", "Podcast", "Podcast", "Podcast", "Podcast",\n "Blog", "Blog", "Blog", "Blog", "Blog",\n "Community"\n ),\n level_min = c(\n 1, 1, 1, 1,\n 1, 2, 2, 1, 3, 1,\n 2, 1, 1, 1, 1, 2,\n 2, 1, 1, 2, 2,\n 1\n ),\n level_max = c(\n 2, 2, 3, 3,\n 3, 2, 2, 2, 3, 3,\n 3, 3, 2, 2, 3, 3,\n 3, 2, 3, 3, 3,\n 3\n ),\n size_metric = c(\n 12, 20, 3000, 100,\n 150, 2, 12, 1, 1, 7,\n 174, 490, 294, 340, 233, 243,\n 150000, 500000, 100000, 50000, 240000,\n 133\n ),\n stringsAsFactors = FALSE\n)\n\n# Midpoint of level range for plotting\nresources <- resources %>%\n mutate(\n level_mid = (level_min + level_max) \/ 2,\n type_factor = factor(type, levels = c("Academy", "Course", "Podcast", "Blog", "Community"))\n )\n\n# =============================================================================\n# CHART 1: AI Learning Resource Landscape \u2014 Faceted Range Chart (800x700)\n# =============================================================================\n\n# Each resource gets its own row, faceted by type \u2014 no collisions\nresources_chart <- resources %>%\n mutate(\n name = factor(name, levels = rev(name)),\n type_label = case_when(\n type == "Academy" ~ "Official Academies",\n type == "Course" ~ "Courses & MOOCs",\n type == "Podcast" ~ "Podcasts",\n type == "Blog" ~ "Blogs & Newsletters",\n type == "Community" ~ "Community"\n ),\n type_label = factor(type_label, levels = c(\n "Official Academies", "Courses & MOOCs", "Podcasts",\n "Blogs & Newsletters", "Community"\n ))\n )\n\np1 <- ggplot(resources_chart, aes(y = name)) +\n geom_segment(\n aes(x = level_min, xend = level_max, yend = name, color = type_factor),\n linewidth = 5, alpha = 0.25, lineend = "round"\n ) +\n geom_point(aes(x = level_min, color = type_factor), size = 3) +\n geom_point(aes(x = level_max, color = type_factor), size = 3) +\n facet_grid(\n type_label ~ ., scales = "free_y", space = "free_y",\n switch = "y"\n ) +\n scale_color_manual(values = type_colors, guide = "none") +\n scale_x_continuous(\n breaks = 1:3,\n labels = c("Beginner", "Intermediate", "Advanced"),\n limits = c(0.7, 3.3),\n position = "top"\n ) +\n labs(\n title = "The AI Learning Resource Landscape",\n subtitle = "22 free resources by skill level \u2014 bars show the range each resource covers",\n x = NULL, y = NULL\n ) +\n theme_ai_learn +\n theme(\n strip.text.y.left = element_text(\n angle = 0, face = "bold", size = 10, color = "grey30", hjust = 1\n ),\n strip.placement = "outside",\n strip.background = element_blank(),\n panel.grid.major.y = element_blank(),\n panel.grid.minor.x = element_blank(),\n panel.spacing = unit(0.4, "lines"),\n axis.text.y = element_text(size = 10, face = "bold"),\n axis.text.x.top = element_text(size = 11, face = "bold"),\n legend.position = "none"\n )\n\nggsave("https:\/\/inphronesys.com\/wp-content\/uploads\/2026\/02\/ai_learn_resource_landscape-1.png", p1,\n width = 8, height = 7, dpi = 100, bg = "white")\n\n# =============================================================================\n# CHART 2: Platform Comparison \u2014 The 4 Official AI Academies (800x500)\n# =============================================================================\n\nacademies <- data.frame(\n platform = c("Google AI \/ Skills", "Microsoft Learn", "OpenAI Academy", "Anthropic Academy"),\n courses = c(3000, 100, 20, 12),\n label_text = c(\n "3,000+ courses\\nHands-on labs\\nBroadest library",\n "100+ AI paths\\n7 certifications\\nAzure integrated",\n "20+ courses\\nSector-specific tracks\\nCertifications coming",\n "12+ courses\\nFree certificates\\n3 learning tracks"\n ),\n brand_color = c("#4285F4", "#00A4EF", "#10a37f", "#d97706"),\n stringsAsFactors = FALSE\n)\n\nacademies <- academies %>%\n mutate(platform = factor(platform, levels = platform))\n\np2 <- ggplot(academies, aes(x = courses, y = platform, fill = platform)) +\n geom_col(width = 0.6, show.legend = FALSE) +\n geom_text(\n data = academies %>% filter(courses >= 1000),\n aes(x = courses * 0.5, label = label_text),\n hjust = 0.5, size = 3, color = "white", fontface = "bold", lineheight = 0.9\n ) +\n geom_text(\n data = academies %>% filter(courses >= 1000),\n aes(label = paste0(comma(courses), "+")),\n hjust = -0.15, size = 4.2, fontface = "bold", color = "grey30"\n ) +\n geom_text(\n data = academies %>% filter(courses < 1000),\n aes(label = paste0(courses, "+")),\n hjust = -0.15, size = 4.2, fontface = "bold", color = "grey30"\n ) +\n geom_text(\n data = academies %>% filter(courses < 1000),\n aes(x = courses + 350, label = label_text),\n hjust = 0, size = 3, color = "grey40", lineheight = 0.9\n ) +\n scale_fill_manual(values = setNames(academies$brand_color, academies$platform)) +\n scale_x_continuous(\n expand = expansion(mult = c(0, 0.45)),\n labels = comma_format()\n ) +\n labs(\n title = "The 4 Official AI Academies Compared",\n subtitle = "Number of courses and resources \u2014 each platform has a distinct strength",\n x = "Number of Courses \/ Resources",\n y = NULL\n ) +\n theme_ai_learn +\n theme(\n panel.grid.major.y = element_blank(),\n axis.text.y = element_text(face = "bold", size = 12)\n )\n\nggsave("https:\/\/inphronesys.com\/wp-content\/uploads\/2026\/02\/ai_learn_platform_comparison-1.png", p2,\n width = 8, height = 5, dpi = 100, bg = "white")\n\n# =============================================================================\n# CHART 3: Suggested Learning Paths (800x500)\n# =============================================================================\n\npath_data <- data.frame(\n path = c(\n rep("Quick Start\\n(2 weeks)", 4),\n rep("Deep Dive\\n(3 months)", 5),\n rep("Stay Current\\n(ongoing)", 5)\n ),\n step = c(\n 1, 2, 3, 4,\n 1, 2, 3, 4, 5,\n 1, 2, 3, 4, 5\n ),\n resource = c(\n "Google AI\\nEssentials", "Anthropic\\nClaude 101", "ChatGPT Prompt\\nEngineering", "The Neuron\\nDaily Digest",\n "MIT\\n6.S191", "Karpathy\\nZero to Hero", "fast.ai", "Hugging Face\\nLearn", "Latent Space\\nPodcast",\n "Last Week\\nin AI", "The\\nNeuron", "Simon\\nWillison", "Sebastian\\nRaschka", "Clauders\\nMeetups"\n ),\n stringsAsFactors = FALSE\n)\n\npath_colors <- c(\n "Quick Start\\n(2 weeks)" = "#0891b2",\n "Deep Dive\\n(3 months)" = "#7c3aed",\n "Stay Current\\n(ongoing)" = "#ea580c"\n)\n\npath_y_map <- c("Quick Start\\n(2 weeks)" = 3, "Deep Dive\\n(3 months)" = 2, "Stay Current\\n(ongoing)" = 1)\n\npath_data <- path_data %>%\n mutate(\n x = step,\n y = path_y_map[path],\n path_factor = factor(path, levels = names(path_y_map))\n )\n\nsegments_data <- path_data %>%\n group_by(path) %>%\n arrange(step) %>%\n mutate(\n x_end = lead(x),\n y_end = lead(y)\n ) %>%\n filter(!is.na(x_end)) %>%\n ungroup()\n\np3 <- ggplot() +\n geom_segment(\n data = segments_data,\n aes(x = x + 0.18, xend = x_end - 0.18, y = y, yend = y_end, color = path_factor),\n linewidth = 1.8,\n arrow = arrow(length = unit(0.2, "cm"), type = "closed"),\n show.legend = FALSE\n ) +\n geom_point(\n data = path_data,\n aes(x = x, y = y, color = path_factor),\n size = 12, shape = 16, alpha = 0.12, show.legend = FALSE\n ) +\n geom_point(\n data = path_data,\n aes(x = x, y = y, fill = path_factor),\n size = 7, shape = 21, color = "white", stroke = 2\n ) +\n geom_text(\n data = path_data,\n aes(x = x, y = y - 0.28, label = resource, color = path_factor),\n size = 2.6, fontface = "bold", lineheight = 0.85,\n vjust = 1, show.legend = FALSE\n ) +\n annotate("text", x = 0.25, y = 3, label = "Quick Start\\n2 weeks",\n hjust = 1, size = 3.5, fontface = "bold", color = path_colors[1], lineheight = 0.9) +\n annotate("text", x = 0.25, y = 2, label = "Deep Dive\\n3 months",\n hjust = 1, size = 3.5, fontface = "bold", color = path_colors[2], lineheight = 0.9) +\n annotate("text", x = 0.25, y = 1, label = "Stay Current\\nongoing",\n hjust = 1, size = 3.5, fontface = "bold", color = path_colors[3], lineheight = 0.9) +\n scale_color_manual(values = path_colors, name = "Learning Path") +\n scale_fill_manual(values = path_colors, name = "Learning Path") +\n scale_x_continuous(limits = c(-0.5, 5.7), expand = c(0, 0)) +\n scale_y_continuous(limits = c(0.2, 3.5), expand = c(0, 0)) +\n labs(\n title = "3 Learning Paths to AI Fluency",\n subtitle = "From quick start to deep expertise \u2014 pick the path that fits your schedule"\n ) +\n theme_ai_learn +\n theme(\n axis.text = element_blank(),\n axis.title = element_blank(),\n panel.grid = element_blank(),\n legend.position = "none"\n )\n\nggsave("https:\/\/inphronesys.com\/wp-content\/uploads\/2026\/02\/ai_learn_learning_paths-1.png", p3,\n width = 8, height = 5, dpi = 100, bg = "white")\n<\/code><\/pre>\n<\/details>\nReferences<\/h2>\n\n- Anthropic. (2025). Anthropic Academy<\/em>. Retrieved from https:\/\/anthropic.skilljar.com<\/li>\n
- OpenAI. (2024). OpenAI Academy \u2014 AI Education for Everyone<\/em>. Retrieved from https:\/\/academy.openai.com<\/li>\n
- Google. (2025). Learn AI Skills \u2014 Google AI<\/em>. Retrieved from https:\/\/ai.google\/learn-ai-skills<\/li>\n
- Microsoft. (2026). AI Learning Hub \u2014 Microsoft Learn<\/em>. Retrieved from https:\/\/learn.microsoft.com\/ai<\/li>\n
- Ng, A. (2025). DeepLearning.AI Course Catalog<\/em>. Retrieved from https:\/\/www.deeplearning.ai\/courses<\/li>\n
- Howard, J. (2025). Practical Deep Learning for Coders \u2014 fast.ai<\/em>. Retrieved from https:\/\/course.fast.ai<\/li>\n
- Hugging Face. (2025). Hugging Face Learn<\/em>. Retrieved from https:\/\/huggingface.co\/learn<\/li>\n
- Karpathy, A. (2024). Neural Networks: Zero to Hero<\/em> [Video series]. YouTube.<\/li>\n
- Lex Fridman. (2026). Lex Fridman Podcast \u2014 Episode Archive<\/em>. Retrieved from https:\/\/lexfridman.com\/podcast<\/li>\n
- Wang, S. & Fanelli, A. (2025). Latent Space \u2014 The AI Engineer Podcast<\/em>. Retrieved from https:\/\/www.latent.space<\/li>\n
- Raschka, S. (2025). Ahead of AI \u2014 Newsletter Archive<\/em>. Retrieved from https:\/\/magazine.sebastianraschka.com<\/li>\n
- Willison, S. (2025). Simon Willison’s Weblog<\/em>. Retrieved from https:\/\/simonwillison.net<\/li>\n
- Marcus, G. (2025). Gary Marcus \u2014 Substack<\/em>. Retrieved from https:\/\/garymarcus.substack.com<\/li>\n
- Clauders. (2026). Global Claude Code Meetup Community<\/em>. Retrieved from https:\/\/clauders.com<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"
Your CEO just asked about your AI strategy. Here are 29 free resources \u2014 from Anthropic’s academy to Karpathy’s YouTube \u2014 organized by how you like to learn.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[139,140],"tags":[141,144,143,145,148,142,147,146,149],"class_list":["post-1163","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","category-professional-development","tag-ai-learning","tag-ai-newsletters","tag-ai-podcasts","tag-anthropic-academy","tag-clauders","tag-free-ai-courses","tag-google-ai","tag-openai-academy","tag-supply-chain-ai"],"_links":{"self":[{"href":"https:\/\/inphronesys.com\/index.php?rest_route=\/wp\/v2\/posts\/1163","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/inphronesys.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/inphronesys.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/inphronesys.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/inphronesys.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1163"}],"version-history":[{"count":1,"href":"https:\/\/inphronesys.com\/index.php?rest_route=\/wp\/v2\/posts\/1163\/revisions"}],"predecessor-version":[{"id":1165,"href":"https:\/\/inphronesys.com\/index.php?rest_route=\/wp\/v2\/posts\/1163\/revisions\/1165"}],"wp:attachment":[{"href":"https:\/\/inphronesys.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1163"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/inphronesys.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1163"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/inphronesys.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1163"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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