Speaker 1: Okay, let's unpack this. We're diving deep into machine learning today,
Speaker 1: but maybe not in the way you'd expect. We're skipping
Speaker 1: the basic code tutorials. Our mission really is twofold. First,
Speaker 1: clear up that fuzzy line between AI and mL. Second,
Speaker 1: and this is the big one, expose what might be
Speaker 1: the hardest part of building these models. Hint, it's probably
Speaker 1: not the coding. And then we'll look at some high
Speaker 1: level tools, specifically the IBM Watson suite that aim to
Speaker 1: sort of shortcut all that complexity.
Speaker 2: Sound good, sounds great, And yeah, starting with that AI
Speaker 2: versus mL distinction as well. It's essential people use them
Speaker 2: interchangeably all the time, but they're really not the same thing.
Speaker 2: AI Artificial intelligence. That's the really big umbrella term, right.
Speaker 2: It covers anytime a machine does something we normally think
Speaker 2: requires intelligence to achieve a goal.
Speaker 1: Okay, so, like think way back a simple tic tac
Speaker 1: toe game programmed with fixed rules. If it plays to
Speaker 1: win based on those rules, that's AI exactly.
Speaker 2: It's following programmed instructions to simulate intelligence. It's not learning,
Speaker 2: just executing. Machine learning or mL is different. It's a
Speaker 2: subset of AI. This is where the system actually improves
Speaker 2: its performance on a task without being explicitly programmed for
Speaker 2: every single step.
Speaker 1: Ah. So instead of programming the if this then that
Speaker 1: for tick tak.
Speaker 2: Toe right, you'd feed it, say, thousands of recorded games,
Speaker 2: just the data, and the mL algorithm itself works out
Speaker 2: the patterns, the statistics of what moves lead to wins
Speaker 2: or losses. It basically builds its own strategy, its own
Speaker 2: sort of functional equation from experience.
Speaker 1: And Underlying these algorithms are some core mathematical ideas. We
Speaker 1: hear terms like linear regression.
Speaker 2: Yeah, the classic YMX plus C just finding the best
Speaker 2: line through data points, simple but powerful.
Speaker 1: Or things like support vector machines. Those sound more complex.
Speaker 2: They are. Sbms are really good when the boundary between
Speaker 2: your data categories isn't a straight line. I think complex
Speaker 2: patterns are spotting outliers.
Speaker 1: And knear's neighbor. That sounds more intuitive it is.
Speaker 2: Conceptually. kNN is unsupervised, means it doesn't need pre labeled answers.
Speaker 2: It just looks at a new data point and classifies
Speaker 2: it based on well what its nearest neighbors are in
Speaker 2: the data space. Simple distance calculation.
Speaker 1: Essentially, so the common thread is math. They all need
Speaker 1: numerical inputs to crunch the numbers and find that equation.
Speaker 2: Precisely, they're sophisticated calculators at their core, and that numerical
Speaker 2: need leads us right into the thick of it.
Speaker 1: Right Here's where it gets, as you said, really interesting,
Speaker 1: because if the algorithm is the calculator, the data is
Speaker 1: the fuel. And everything we've looked at suggests coding the model.
Speaker 1: Choosing the algorithm, that's often the easier.
Speaker 2: Part, oh absolutely far easier data preparation and something called
Speaker 2: feature engineering. That's where the real time sink is. That's
Speaker 2: the hardest part, easily.
Speaker 1: That seems counterintuitive. Why is wrangling the data so much
Speaker 1: harder than building the prediction engine itself?
Speaker 2: Because real world data is messy, it's incomplete, it's inconsistent,
Speaker 2: it's often in the wrong format. The algorithms, like those
Speaker 2: calculators are actually quite robust once they have clean input,
Speaker 2: but they are incredibly picky about getting that clean input.
Speaker 2: The complexity is in taming the chaos before the math starts.
Speaker 1: Okay, walk us through that taming process. What are the
Speaker 1: key headaches the terms a learner really needs to grasp.
Speaker 2: Well, First up, is just inspection. You load the data,
Speaker 2: maybe using a tool like pandas and Python, and you
Speaker 2: look at it. You'd use functions like say DF dot
Speaker 2: info to check for missing value. See that non n
Speaker 2: all count. If it's less than your total rows, you've
Speaker 2: got gaps.
Speaker 1: And you kid just leave gaps key Nope.
Speaker 2: The math breaks down. So you have to decide do
Speaker 2: I fill them in maybe using filnella with the average
Speaker 2: value of that column, or do I just drop those
Speaker 2: roads entirely. That's a judgment call.
Speaker 1: Okay, so handling missing data. What else?
Speaker 2: Then there's accessing the specific data you need, the features
Speaker 2: or columns. You might use methods like DF dot lock
Speaker 2: or just DF column name like DF population. And crucially,
Speaker 2: you often need to normalize or scale features. If one
Speaker 2: feature is age from zero to one hundred and another
Speaker 2: is income from zero to millions, the income scale could
Speaker 2: totally dominate the learning process. Just because the numbers are bigger,
Speaker 2: you need to bring them to a comparable scale.
Speaker 1: Makes sense, But then you hit what you call the
Speaker 1: language barrier, the fact that models only speak math exactly.
Speaker 2: What do you do with categorical data, things like color
Speaker 2: names red, blue, green, or maybe city names or product types.
Speaker 2: These aren't numbers.
Speaker 1: You can't just assign red one, blue, two green three.
Speaker 2: Right, You absolutely cannot, because the algorithm would interpret that
Speaker 2: as green being somehow three times as much as red,
Speaker 2: or blue being more than red. It imposes a false
Speaker 2: mathematical relationship that doesn't exist.
Speaker 1: So that data is useless unless you transform it.
Speaker 2: Completely useless to the algorithm in its raw state. This
Speaker 2: is where we need a technique called one hot in coding.
Speaker 1: One hot in coding. Okay, how does that work? It's clever.
Speaker 2: Actually, instead of one column with red, blue, green, you
Speaker 2: create three new columns. Maybe is red as blue as green?
Speaker 2: For a row that was red, the ies red column
Speaker 2: gets a one, and the other two you get a zero.
Speaker 2: For a blue row is blue gets one, others get zero.
Speaker 2: Now you have purely numerical data, just zeros and ones
Speaker 2: representing the categories, but without that fake ordering problem. The
Speaker 2: algorithm can handle that.
Speaker 1: Got it? So lots of cleaning, filling, gaps, scaling, and
Speaker 1: this one hot encoding for categories. That sounds like a
Speaker 1: lot of steps.
Speaker 2: It is, and it requires careful thought at each stage.
Speaker 2: Get it wrong and your model's predictions will be meaningless.
Speaker 2: No matter how sophisticated the algorithm is.
Speaker 1: Okay. So let's say we've done all that, the data
Speaker 1: is pristine numerical. How does the model actually learn the
Speaker 1: best coefficients those A values in the equation one dollars
Speaker 1: plus a one by one plus dollars.
Speaker 2: It learns through well trial and error, lots of it,
Speaker 2: very quickly. It starts with random guesses for those coefficients
Speaker 2: the A values. It makes a prediction using those random values.
Speaker 2: Then it compares its prediction to the actual known answer
Speaker 2: in the training data. It calculates how wrong it was
Speaker 2: using something called a loss function. A common one is
Speaker 2: means squared error or MSE. It just measures the average
Speaker 2: square difference between prediction.
Speaker 1: And reality, so it measures the OUCH.
Speaker 2: Pretty much, and the goal is to minimize that OUCH.
Speaker 2: Based on the error, the model slightly adjusts its coefficients
Speaker 2: in the direction that should reduce the error.
Speaker 1: Next time.
Speaker 2: It does this over and over again, making predictions, calculating
Speaker 2: error adjusting coefficients each full pass through the entire data set.
Speaker 2: Doing this is called an.
Speaker 1: Epoch, and it just keeps doing airbox until the error
Speaker 1: is as low as.
Speaker 2: Possible, or until the error stops improving significantly. Yeah, it's
Speaker 2: basically finding the coefficient values that best fit the patterns
Speaker 2: in the data by minimizing that loss function.
Speaker 1: Okay, that makes sense, which brings us to evaluating the
Speaker 1: model once it's trained. Metrics matter you called it, and
Speaker 1: you mentioned earlier. If I brag about ninety five percent accuracy,
Speaker 1: you might be suspicious. Why isn't high accuracy good?
Speaker 2: It can be, but it can also be incredibly misleading,
Speaker 2: especially with what we call skewed data sets.
Speaker 1: Skewed meaning unbalanced.
Speaker 2: Exactly. Imagine you're trying to detect a rare disease that
Speaker 2: only affects one percent of the population. A lazy model
Speaker 2: could just predict no disease for absolutely everyone. It would
Speaker 2: be wrong one percent of the time, but right ninety
Speaker 2: nine percent of the time, so ninety nine percent accuracy.
Speaker 1: So it would be completely useless. It never finds the
Speaker 1: actual disease cases precisely.
Speaker 2: That's why simple accuracy fails on skewed data. It doesn't
Speaker 2: tell you if the model is good at finding the
Speaker 2: thing you actually.
Speaker 1: Care about, So we need smarter metrics. You mentioned precision,
Speaker 1: and recall these involve true positives false positives all that.
Speaker 2: Yes, the confusion matrix terms tp tn fp fm true positive,
Speaker 2: true negative, false positive false negative. Precision asks of all
Speaker 2: the times the model predicted something was positive, like disease
Speaker 2: found how many times was it actually right? The formula
Speaker 2: is ttp, tp plus fp lesh. It's about minimizing the
Speaker 2: false positives predicting something that isn't there.
Speaker 1: Okay. So precision is about the accuracy of the positive predictions.
Speaker 1: What about recall?
Speaker 2: Recall, which is also called sensitivity or true positive rate,
Speaker 2: asks a different question of all the things that actually
Speaker 2: were positive in the real data, how many did the
Speaker 2: model successfully find? The formula is tp tp plus fn double.
Speaker 2: It's about minimizing false negatives, missing things you should have found.
Speaker 1: Ah, Okay, minimizing false positives precision versus minimizing false negatives recall,
Speaker 1: and I guess you can't always maximize both.
Speaker 2: Often there's a trade off. Tuning a model to be
Speaker 2: extremely precise might make it miss some actual positive cases
Speaker 2: lower recall. Tuning for extremely high recall might mean you
Speaker 2: get more false alarms lower precision.
Speaker 1: And the right balance depends entirely on the consequences of
Speaker 1: getting it wrong. Do you give us those examples again?
Speaker 1: They were really clear?
Speaker 2: Sure? Let's take tumor prediction. What's the worst kind of
Speaker 2: error there?
Speaker 1: A false positive, right, telling a healthy patient they have cancer.
Speaker 1: That's psychologically devastating and leads to unnecessary, potentially harmful treatments.
Speaker 2: Exactly. So in that case you need extremely high precision.
Speaker 2: You want to be very very sure when you say cancer.
Speaker 2: You might tolerate slightly lower recall, meaning you might miss
Speaker 2: a few tumors initially a false negative, because hopefully follow
Speaker 2: up tests or screenings will catch those later. The cost
Speaker 2: of a false positive is just too high.
Speaker 1: Okay, high precision for tumors. Now flip it. What about say,
Speaker 1: detecting shoplifters in the store security feed?
Speaker 2: Right, what's the worst error there?
Speaker 1: A false negative missing someone who is shoplifting the store,
Speaker 1: losers merchandise, the crime goes.
Speaker 2: Unaddressed, precisely, So here you need high recall. You want
Speaker 2: to catch as many actual incidents as possible. You might
Speaker 2: tolerate a few false positives and maybe flagging an innocent
Speaker 2: shopper occasionally who then gets quickly cleared by security. That's
Speaker 2: annoying for the customer, sure, but it's often seen as
Speaker 2: less costly than letting actual theft happen repeatedly. High recall
Speaker 2: is the priority.
Speaker 1: That really drives it home. It's not just about the math,
Speaker 1: it's about the real world impact of different kinds of errors.
Speaker 1: So tools like psych learns, precision recall curve, we're looking
Speaker 1: at ROC curves and AUC scores. They help you find
Speaker 1: that sweet spot exactly.
Speaker 2: They visualize the trade off and help you choose a
Speaker 2: model threshold that balances precision and recall appropriately for your
Speaker 2: specific problem. There's no single best score. It depends on
Speaker 2: the context.
Speaker 1: Which is a great transition. We've talked about the pain
Speaker 1: of data prep the nuances of metrics. Now let's talk
Speaker 1: about making it easier.
Speaker 2: Yes, knowledge is great, but applying it efficiently is key.
Speaker 2: Given how much manual effort goes into cleaning, tuning, and testing,
Speaker 2: Let's look at the tools designed to abstract that away.
Speaker 2: The IBM Watson suite is a prime example.
Speaker 1: Here, right the automation aspect, Let's start with optimizing the
Speaker 1: model itself. Traditionally, after data cleaning, you face this huge
Speaker 1: task of trying different models right decision trees, random forests,
Speaker 1: boosted trees.
Speaker 2: Dozens of them potentially, and for each model type you
Speaker 2: have to do hyper parameter tuning hyper parameters.
Speaker 1: Those are the knobs and dials inside the algorithm itself,
Speaker 1: like how deep a decision tree should grow max depth,
Speaker 1: or how many trees a random forest should use.
Speaker 2: Estimators exactly, and finding the best combination of these settings
Speaker 2: is crucial for performance. The traditional way is often brute force,
Speaker 2: like grid search cross validation. You define a grid of
Speaker 2: possible values for each hyper parameter, and the computer systematically
Speaker 2: tries every single combination. It can take hours, even days,
Speaker 2: depending on the data and the model complexity.
Speaker 1: Okay, so that sounds incredibly tedious and computationally expensive. How
Speaker 1: does something like AUTOAI shortcut this?
Speaker 2: AUTOAI is designed specifically for this structured data optimization problem.
Speaker 2: It's pretty remarkable. You essentially give it your clean data set,
Speaker 2: tell which column you want to predict, like medium house
Speaker 2: value or MEDV, and a housing data set, and then
Speaker 2: it just goes. It analyzes the data, It intelligently selects
Speaker 2: and applies data transformations. It builds multiple candidate pipelines using
Speaker 2: various algorithms. It performs sophisticated hyperparameter optimization automatically, far beyond
Speaker 2: simple grid search, and then it ranks all all the
Speaker 2: tested pipelines based on metrics relevant to your problem, like
Speaker 2: RMSC root means squared error.
Speaker 1: And the key part is you don't write the modeling code.
Speaker 2: Not a single line for the model training and tuning part.
Speaker 2: It automates what used to be weeks of a data
Speaker 2: scientist's iterative work, presenting you with the best performing models
Speaker 2: ready to go.
Speaker 1: Wow. Okay, that tackles structured tabular data. But what about
Speaker 1: the really messy stuff unstructured text images? We know? Traditional
Speaker 1: natural language processing NLP is a beast. You have to
Speaker 1: scrape text clean it, filter out common stop words like
Speaker 1: the and A, convert words to numbers using complex methods
Speaker 1: like word embedding. It's a whole field in itself, it
Speaker 1: really is.
Speaker 2: Building a good NLP pipeline from scratch can take months
Speaker 2: or even years of specialized effort. This is where something
Speaker 2: like Watson Discovery comes in. It aims to bypass almost
Speaker 2: all of that initial heavy lifting for text analysis.
Speaker 1: How so, what does it do?
Speaker 2: It provides powerful preprocessing out of the box, things like
Speaker 2: optical character recognition OCR to pull text from scanned documents,
Speaker 2: automatic text extraction from various file types. But the real
Speaker 2: magic is in the enrichments. Instead of you training a
Speaker 2: model for months just to recognize names or places, Discovery
Speaker 2: comes pre loaded with enrichment's like entity extraction, finding people,
Speaker 2: companies' locations, concept tagging, identifying key ideas, sentiment analysis, positive negative, tone,
Speaker 2: and more. You get deep insights almost instantly, So.
Speaker 1: It's like having a pre trained NLP expert ready to
Speaker 1: analyze huge volumes of documents.
Speaker 2: That's a good way to put it. And you can
Speaker 2: query these analyzed collections using the Discovery Query Language or DQL.
Speaker 2: You use simple operators like dot for an exact match
Speaker 2: or boff for contains to pinpoint specific information across potentially
Speaker 2: millions of documents without writing complex and LP code.
Speaker 1: Okay, that's text. What about images?
Speaker 2: Simpler idea very similar principle with Watson Visual recognition. Image analysis,
Speaker 2: especially using deep learning, is another complex field. Visual recognition
Speaker 2: offers pre built capabilities. You can use it for image classification,
Speaker 2: like telling the difference between a photo of a husky
Speaker 2: and a photo of a beagle, or for object detection
Speaker 2: finding and maybe even counting specific things within an image,
Speaker 2: like identifying all the cars or people in a street scene. Again,
Speaker 2: it abstracts away the need to build and train those
Speaker 2: complex deep learning models yourself.
Speaker 1: It seems like a recurring theme abstracting the complexity of
Speaker 1: the underlying mL. It's such a one more automation piece
Speaker 1: building chatbots or conversational interfaces with what it's an assistant?
Speaker 1: How does that simplify things?
Speaker 2: It uses a fairly intuitive structure. You define the user's
Speaker 2: intents what they're trying to achieve, often marked with a
Speaker 2: hash like halftag order pizza. Then you define entities the
Speaker 2: specific pieces of information relevant to those intents, marked within
Speaker 2: at like at pizza size or at topping.
Speaker 1: So intent is the goal, entity is the detail. How
Speaker 1: do they connect through dialogues?
Speaker 2: You build a flow chart essentially that defines the conversation.
Speaker 2: If the user expresses the hashtag order pizza intent, the
Speaker 2: dialogue might then ask for the site at pizza size
Speaker 2: and at topping entities.
Speaker 1: How does it remember what the user already said, Like
Speaker 1: if I say I want a large pizza and then
Speaker 1: later say pepperoni.
Speaker 2: That's handled by features like slots and context variables. Slots
Speaker 2: are defined within an intent to make sure the bot
Speaker 2: gathers all necessary entities if it needs size and topping,
Speaker 2: and you only gave the size A slot can prompt
Speaker 2: for the topping. Context variables are like the bot's short
Speaker 2: term memory. It can store the fact that pizza size
Speaker 2: lurgs in a context variable. So when you just say pepperoni,
Speaker 2: it knows you mean pepperoni for the large pizza you're
Speaker 2: already mentioned. It maintains the state of the conversation.
Speaker 1: Okay, so we've got these powerful, often automated ways to
Speaker 1: build specialized mL models and services using tools like Watson,
Speaker 1: AUTOAI for structured data, discovery for text, visual recognition for images,
Speaker 1: Assistant for conversations. So what does this all mean? How
Speaker 1: do these things actually get used? How do we move
Speaker 1: from these tools to a live application?
Speaker 2: Good question. You need to get them into a production
Speaker 2: environment where users or other systems can interact with them.
Speaker 2: A common approach is to build a back end application,
Speaker 2: maybe using a Python web framework like flask. This flask
Speaker 2: app acts as a middleman. It receives requests maybe from
Speaker 2: a web page or mobile app, figures out what needs
Speaker 2: to happen, calls the relevant walks in API like discovery
Speaker 2: or assistant, gets the result and sends it.
Speaker 1: Back and deploying that Flask gap is that complex too, it.
Speaker 2: Can be, but platform as a service offerings like IBM
Speaker 2: Cloud with Cloud Foundry really simplify it. Often it's as
Speaker 2: simple as navigating to your project directory and the command
Speaker 2: line and typing CF push. The platform handles provisioning servers,
Speaker 2: load balancing, all the infrastructure stuff. It can be incredibly fast.
Speaker 1: So the path to production can be streamlined too. Are
Speaker 1: there other useful utility services that often plug into these systems?
Speaker 1: You mentioned a couple, Yeah, A couple.
Speaker 2: Of really useful ones come to mind. First, the Tone Analyzer.
Speaker 2: This service specifically analyzes texts, but not just for what
Speaker 2: is said, but how it's said. It uses NLP to
Speaker 2: detect emotional and language tones, well kind of tones. It
Speaker 2: breaks them down. They're emotional tones, things like anger, fear,
Speaker 2: joy sadness, and then language tones analytical, tentative, confident.
Speaker 1: I could see how that would be useful, like monitoring
Speaker 1: customer support chats or reviews.
Speaker 2: Absolutely understanding the tone helps companies gauge customer sentiment, identify
Speaker 2: urgent issues, or even tailor responses dynamically. And the other utility,
Speaker 2: text to speech or TTS, exactly the kind of technology
Speaker 2: needed to voice a script like this one. Actually, it
Speaker 2: takes written text and converts it into natural sounding speech.
Speaker 2: Modern TTS services offer various high quality voices different languages,
Speaker 2: and you can even customize the output using SSML. That's
Speaker 2: Speech Synthesis Markup Language. It lets you control pronunciation, pauses, emphasis, pitch,
Speaker 2: making the synthesized speech sound much less robotic.
Speaker 1: Right bringing it full circle. So to recap, we've seen
Speaker 1: that while algorithms are key, the real bear in mL
Speaker 1: is often data preparation and feature engineering. We learn that
Speaker 1: simple accuracy can lie, and we need metrics like precision
Speaker 1: and recall balance carefully based on the real world consequences
Speaker 1: of errors. And then we saw how suites like IBM
Speaker 1: Watson provide powerful shortcuts AUTOAI for optimizing models on structured
Speaker 1: data without coding, Discovery and visual recognition for extracting insights
Speaker 1: from unstructured text and images, and assistant for building conversational interfaces.
Speaker 2: Plus utilities like tone analyzer and text to speech to
Speaker 2: add further capabilities, all deployable relatively easily via cloud platforms.
Speaker 1: Okay, so you the listener should now have a much
Speaker 1: clearer picture of both the deep challenges in mL, data quality,
Speaker 1: metric choice, and also the sophisticated tools emerging to automate
Speaker 1: and abstract away a lot of that complexity.
Speaker 2: And we saw specifically how tools like AUTOAI can take
Speaker 2: over complex tasks like model selection and hyper parameter tuning,
Speaker 2: things that used to be purely the domain of the
Speaker 2: expert coder. Which leads to, I think a really interesting
Speaker 2: final thought for you to chew on. As these incredibly
Speaker 2: powerful tools increasingly automate the how now, the coding, the tuning,
Speaker 2: the model selection itself, where should the modern data learner
Speaker 2: focus their energy next? Is the most valuable skill becoming
Speaker 2: in even deeper mastery of the underlying code and mathematics,
Speaker 2: or is it shifting towards mastering the data itself, its quality,
Speaker 2: its nuances, its preparation, and ultimately the interpretation of what
Speaker 2: the automated tools tell us. Where does the essential human
Speaker 2: expertise lie now? Something to think about