Module 1: Introduction to Machine Learning

This intermediate-level “Machine Learning with Python” course is designed to equip you with practical machine learning skills using Python, the leading language in the field. Ideal for those starting or advancing their careers in ML, data science, and AI, the course recommends prior knowledge of Python, Pandas, NumPy, and data analysis.

You’ll explore the role of machine learning in various careers, understand the ML lifecycle, and learn how models function. The curriculum focuses on core modeling techniques like classification, regression, and clustering, demonstrating how they fit into supervised and unsupervised learning frameworks using real-world data. You’ll also get a brief introduction to reinforcement learning, deep learning, and artificial intelligence.

The course emphasizes hands-on experience: you’ll build, assess, and validate ML models using Python and popular open-source libraries such as Pandas, NumPy, and Scikit-learn. Learning is reinforced through instructional videos, practical labs, quizzes, and a final project on rainfall prediction. Specific topics include Multiple Linear Regression, Logistic Regression, Prediction, Fraud Detection, KNN, and SVM. Support is available via the discussion forum.

This video introduces two IBM Professional Certificates: the IBM AI Engineering PC and the IBM Data Science PC.

The IBM Data Science PC is for beginners, covering data cleaning, analysis, and predictive modeling using tools like Python, SQL, Pandas, and NumPy to build a job-ready portfolio.

The IBM AI Engineering PC is an advanced program for those with Python and data analysis knowledge, designed for data scientists, machine learning engineers, and software engineers. It focuses on building, training, and deploying various deep learning models, including Large Language Models (LLMs), using Python and libraries such as SciPy, Keras, PyTorch, and TensorFlow. This certificate covers topics from AI and Deep Learning fundamentals to advanced Generative AI and LLM fine-tuning.

Both certifications emphasize hands-on learning with projects and labs to build real-world experience. The AI Engineering PC curriculum includes modules on:

• Machine Learning with Python (linear/logistic regression, decision trees, supervised/unsupervised learning)
• Introduction to Deep Learning & Neural Networks (autoencoders, RBMs, convolutional and recurrent networks with Keras)
• Advanced Deep Learning with Keras and TensorFlow 2.x (custom layers, CNNs, transformers, reinforcement learning)
• Deep Learning with PyTorch (tensors, regression, neural networks, CNNs, handling overfitting)
• AI Capstone Project (applying deep learning to a real-world problem)
• Generative AI and LLMs (types, applications, tokenization, data preparation, Hugging Face)
• Generative AI Foundational Models for NLP (embeddings, Word2Vec, N-gram, sequence-to-sequence models)
• Generative AI Language Modeling with Transformers (GPT, BERT, attention mechanisms)
• Generative AI Engineering and Fine-Tuning Transformers (PEFT, LORA, QLORA, Prompting)
• Generative AI Advance Fine-Tuning for LLMs (human feedback, instruction tuning, RLHF, PPO)
• Fundamentals of AI Agents Using RAG and LangChain (RAG, Prompt Engineering, LangChain tools)
• Project: Generative AI Applications with RAG and LangChain (building a QA bot with Gradio and watsonx)

Each topic includes self-paced modules, instructional videos, labs, quizzes, and projects to help you earn your professional certificate and prepare for a career in AI or data science.

This video introduces Machine Learning (ML) as a subset of Artificial Intelligence (AI), explaining how ML algorithms enable computers to learn from data, identify patterns, and make decisions without explicit programming. It differentiates ML from Deep Learning (DL), noting DL’s use of multi-layered neural networks for automatic feature extraction from complex, unstructured data.

The video outlines various machine learning paradigms:

• Supervised Learning: Trains on labeled data to predict unknown labels.
• Unsupervised Learning: Finds patterns in unlabeled data.
• Semi-supervised Learning: Combines small labeled datasets with iteratively generated labels.
• Reinforcement Learning: Agents learn through interaction with their environment and feedback.

It then details common machine learning techniques and their applications:

• Classification: Predicting categories (e.g., benign/malignant cells, customer churn).
• Regression/Estimation: Predicting continuous values (e.g., house prices, CO2 emissions).
• Clustering: Grouping similar cases (e.g., patient groups, customer segmentation).
• Association: Finding co-occurring items/events (e.g., frequently bought groceries).
• Anomaly Detection: Discovering unusual cases (e.g., credit card fraud).
• Sequence Mining: Predicting the next event (e.g., website clickstream).
• Dimension Reduction: Reducing data size by minimizing features.
• Recommendation Systems: Suggesting items based on similar preferences.

The video provides practical examples of ML applications, including:

• Medical Diagnosis: Identifying cell types (benign/malignant) from patient data.
• Consumer Behavior: Recommending content/products (Amazon, Netflix) and loan application approval (banks), or predicting customer churn (telecom).
• Computer Vision: Differentiating objects in images (e.g., cats vs. dogs) by learning distinguishing features, contrasting it with rule-based programming.
• Other everyday uses like virtual assistants, facial recognition, and computer gaming.

In essence, the module explains that ML is an algorithmic, feature-engineering subset of AI, with models learning through various approaches. It covers diverse techniques applicable across industries, emphasizing that while ML’s impact grows, human oversight remains crucial.

PODCAST: Machine Learning Unpacked: Your Essential Guide to AI’s Core and Real-World Impact

This video introduces the key processes within a Machine Learning (ML) model’s lifecycle, emphasizing its iterative nature.

The lifecycle comprises five main stages:

1. Define the problem: Clearly state the situation or problem the ML solution aims to solve.
2. Data collection: Gather relevant data from various sources.
3. Data preparation: This stage, often part of an Extract, Transform, and Load (ETL) process, involves cleaning, transforming, and consolidating the collected data for use by the machine learning engineer.
4. Model development and evaluation: Build and rigorously test the ML model.
5. Model deployment: Integrate the trained and evaluated model into a production environment.

It’s crucial to understand that this lifecycle isn’t linear. If issues arise with a deployed model, you might need to revisit earlier stages, even going back to problem definition or data collection, and then re-iterate through the subsequent steps.

Want to dive deeper into each of these stages?

Podcast: The Unseen Journey of AI: Inside the Iterative Machine Learning Lifecycle

This video offers a practical walkthrough of the Machine Learning (ML) Model Lifecycle from the perspective of an ML Engineer tasked with building a product recommendation model for a beauty company. It highlights the importance and demands of each stage and identifies which processes are most time-consuming.

The ML lifecycle, which is iterative and often requires revisiting earlier steps, includes:

• Problem Definition: This crucial first step involves deeply understanding the client’s needs and pain points to ensure the ML solution, in this case, recommending beauty products based on purchase history, aligns with business goals.
• Data Collection & Preparation (ETL): This often-overlapping and highly time-consuming phase involves identifying and gathering diverse data sources (user demographics, purchase history, product inventory, ratings, search history, etc.). The collected data then undergoes extensive cleaning to correct errors, handle missing values, standardize formats, and remove extreme outliers. This stage also includes feature engineering (e.g., calculating transaction durations, identifying product skin issues) and Exploratory Data Analysis (EDA) to visually identify patterns, validate data, and perform correlation analysis. The engineer also determines data splitting strategies for training and testing.
• Model Development: This stage focuses on building the ML model, often leveraging existing frameworks. For product recommendations, the engineer employs a combination of:
  • Content-based filtering: Finding product similarities based on characteristics (e.g., if a user buys a water-based cleanser, recommend a highly moisturizing lotion).
  • Collaborative filtering: Identifying user similarities based on their interactions (e.g., product ratings) to recommend items highly rated by similar users.
• Model Evaluation: After building, the model is rigorously tested. Initial evaluation involves tuning and testing on a held-out dataset. Further validation comes from experimenting with recommendations on user groups and collecting feedback (ratings, click-through rates, purchases).
• Model Deployment & Monitoring: The final step involves integrating the model into production (e.g., a beauty product app/website). Crucially, the deployed model requires continuous monitoring to ensure its performance aligns with business requirements. Future iterations may involve retraining with new data to expand capabilities.

The video concludes by stressing that every step in the ML model lifecycle is vital for a successful solution, and ongoing monitoring and improvement are essential to maintain the quality of the deployed product.

Podcast: From Concept to Cart: Unpacking the Hidden Engineering of Machine Learning Recommendations

Data Scientist vs. AI Engineer: A Generative AI Perspective

Isaac Key, a former data scientist turned AI Engineer at IBM, breaks down the evolving roles of Data Scientists and AI Engineers (specifically Generative AI Engineers), highlighting how the rise of generative AI has created distinct specializations.

The Evolving Landscape of AI

Traditionally, data scientists have used AI models for analysis. However, generative AI has fundamentally shifted the landscape, leading to the emergence of AI Engineering as a specialized field.

Four Key Differences

Isaac outlines four core areas where these roles diverge:

1. Use Cases:
   • Data Scientist: Primarily a data storyteller, translating messy data into insights. They focus on descriptive analytics (e.g., Exploratory Data Analysis, clustering for customer segmentation) and predictive analytics (using ML models like regression for numeric predictions or classification for categorical predictions).
   • AI Engineer: An AI system builder, using foundation models to create generative AI systems that transform business processes. Their work involves prescriptive use cases (e.g., decision optimization, recommendation engines for targeted marketing) and generative use cases (creating intelligent assistants, chatbots for conversational search and summarization).
2. Data:
   • Data Scientist: Primarily works with structured (tabular) data, typically hundreds to hundreds of thousands of observations. This data requires extensive cleaning and preprocessing to train ML models.
   • AI Engineer: Primarily handles unstructured data (text, images, video, audio). For example, Large Language Models (LLMs) are trained on billions to trillions of text tokens, a much larger scale than traditional ML.
3. Underlying Models:
   • Data Scientist: Utilizes a vast toolbox of hundreds of diverse ML models and algorithms. Each use case often requires a specific dataset and a distinct model. These models are generally smaller, less computationally intensive to train, and have a narrower scope, making them less generalizable outside their training domain. Training times range from seconds to hours.
   • AI Engineer: Relies predominantly on foundation models. These revolutionary models are designed to generalize across a wide range of tasks without retraining, offering a much wider scope. They are significantly larger (billions of parameters), require immense computational power (hundreds to thousands of GPUs), and take weeks to months to train.
4. Processes and Techniques:
   • Data Scientist: Follows a process of use case -> data collection/preparation -> model training/validation (using feature engineering, cross-validation, hyperparameter tuning) -> model deployment for real-time inference.
   • AI Engineer: Starts with a use case and leverages pre-trained foundation models (enabled by AI democratization and open-source communities like Hugging Face). They primarily use prompt engineering (natural language instructions) to interact with these models. This process can be combined with frameworks for more complex systems, such as chaining prompts, Parameter-Efficient Fine-Tuning (PEFT), Retrieval-Augmented Generation (RAG) for factual grounding, or creating autonomous agents for multi-step problems. The final step involves embedding the AI into larger systems or workflows (e.g., assistants, UIs, automation).

Overlap and Future Outlook

While distinct, the fields still overlap (e.g., data scientists may work on prescriptive use cases, AI engineers with structured data). Both domains are rapidly evolving with continuous research, new models, and tools emerging daily, indicating a future where creativity with data and AI can unlock limitless possibilities.

Podcast: Data Scientist vs. AI Engineer: The Hottest Jobs of the 21st Century Evolve

Why Data is Crucial for Machine Learning

Data is defined as raw facts, figures, or information used to gain insights, inform decisions, and power advanced technologies. It’s the core of every machine learning algorithm, providing all the information needed to discover patterns and make predictions.

Understanding Machine Learning Tools and Languages

Machine learning tools offer functionalities for entire machine learning pipelines, including data preprocessing, model building, evaluation, optimization, and implementation. These tools simplify complex tasks like handling big data, performing statistical analyses, and making predictions.

Machine Learning Programming Languages: These languages are used to build models and uncover hidden patterns in data.

• Python: Widely used due to its extensive libraries for data analysis and processing, and ease of machine learning model development.
• R: Popular for statistical learning, offering many libraries for data exploration and machine learning.
• Julia: A high-performance language with support for parallel and distributed numerical computing.
• Scala: Scalable and used for processing big data and building machine learning pipelines.
• Java: A multi-purpose language supporting scalable machine learning applications in production.
• JavaScript: Used for running machine learning models in web browsers for client-side applications.

Categories of Machine Learning Tools

Machine learning tools serve various purposes, from data storage and retrieval to visualization and model development. They are categorized as follows:

• Data Processing and Analytics Tools:
  • PostgreSQL: An open-source object-relational database system based on SQL.
  • Hadoop: An open-source, scalable solution for storing and batch-processing massive data.
  • Spark: A distributed, in-memory data processing framework for real-time big data, faster than Hadoop.
  • Apache Kafka: A distributed streaming platform for big data pipelines and real-time analytics.
  • Pandas: A popular Python library for data exploration and wrangling, featuring DataFrames for tabular data.
  • NumPy: A Python library providing mathematical functions, random number generators, and linear algebra routines.
• Data Visualization Tools:
  • Matplotlib: A foundational Python library for customizable plots and interactive visualizations.
  • Seaborn: A Matplotlib-based library for attractive and informative statistical graphics.
  • ggplot2: An open-source data visualization package in R, allowing layered graphic construction.
  • Tableau: A business intelligence tool for interactive data visualization dashboards.
• Machine Learning Tools:
  • NumPy: Provides foundational support with efficient numerical computations.
  • Pandas: Used for data analysis, visualization, cleaning, and preparation.
  • SciPy: Built on NumPy, used for scientific computing with modules for optimization, integration, and linear regression.
  • Scikit-learn: For building classical machine learning models, offering algorithms for classification, regression, clustering, and dimensionality reduction.
• Deep Learning Tools: Frameworks for designing, training, and testing neural network models.
  • TensorFlow: An open-source library for numerical computing and large-scale machine learning.
  • Keras: An easy-to-use deep learning library for implementing neural networks.
  • Theano: For efficiently defining, optimizing, and evaluating mathematical expressions involving arrays.
  • PyTorch: An open-source library for deep learning applications, computer vision, and NLP, allowing for experimentation.
• Computer Vision Tools: Used for tasks like object detection, image classification, and facial recognition. All deep learning tools can be adapted for computer vision.
  • OpenCV (Open Source Computer Vision Library): For real-time computer vision applications.
  • Scikit-Image: Offers image processing algorithms like filters, segmentation, and feature extraction.
  • TorchVision: Part of PyTorch, it provides datasets, image loading, pre-trained architectures, and transformations for computer vision.
• Natural Language Processing (NLP) Tools: Help develop applications that understand, interpret, and generate human language.
  • NLTK (Natural Language Toolkit): A comprehensive library for text processing, tokenization, and stemming.
  • TextBlob: A library for tasks like part-of-speech tagging, sentiment analysis, and translation.
  • Stanza: An NLP library from the Stanford NLP Group with accurate pre-trained models for various NLP tasks.
• Generative AI Tools: Leverage AI to generate new content such as text, images, music, or code.
  • Hugging Face Transformers: A library of transformer models for NLP tasks like text generation and translation.
  • ChatGPT: A powerful language model for text generation and chatbots.
  • DALL-E: A tool from OpenAI for generating images from text.
  • PyTorch: Used to create generative models like Generative Adversarial Networks (GANs) and Transformers.

In summary, data is fundamental to machine learning, and a wide array of tools and programming languages are available to simplify complex tasks across the entire machine learning pipeline, from data processing and visualization to building and deploying advanced AI models.

Podcast: The AI Toolkit: Decoding Machine Learning’s Essential Tools

Understanding the Machine Learning Ecosystem

The ML ecosystem is a network of interconnected tools, frameworks, libraries, platforms, and processes that help in the development, deployment, and management of machine learning models. Python is a dominant language in this ecosystem, offering a wide array of open-source libraries that are essential for various ML tasks, including data collection, preprocessing, model training, evaluation, deployment, and monitoring.

Key Python libraries that form a significant part of this ecosystem include:

• NumPy: Provides foundational support for efficient numerical computations on large, multidimensional data arrays.
• Pandas: Built on NumPy and Matplotlib, it’s used for data analysis, visualization, cleaning, and preparing data for machine learning, often utilizing versatile data frames.
• SciPy: Built on NumPy, it’s used for scientific computing and offers modules for optimization, integration, and linear regression.
• Matplotlib: Built on NumPy, it provides an extensive and customizable set of visualization tools.
• Scikit-learn: Built on NumPy, SciPy, and Matplotlib, this library is specifically designed for building classical machine learning models.

Features and Workflow of Scikit-learn

Scikit-learn is a free, open-source Python library widely recognized for its comprehensive and up-to-date selection of algorithms for classification, regression, clustering, and dimensionality reduction. It’s designed to seamlessly integrate with other Python numerical and scientific libraries like NumPy and SciPy. Its robust documentation and large community support network make it an invaluable resource.

Many tasks within a machine learning pipeline are already implemented in Scikit-learn, making it easy to use with just a few lines of Python code. These tasks include:

• Data preprocessing: Data cleaning, scaling, feature selection, and feature extraction.
• Train/test splitting: Dividing datasets for model training and evaluation.
• Model setup and fitting: Instantiating and training machine learning models.
• Hyperparameter tuning: Optimizing model parameters using cross-validation.
• Prediction and evaluation: Generating predictions and assessing model accuracy using metrics like confusion matrices.
• Model export: Saving trained models for production use.

A typical machine learning workflow using Scikit-learn involves:

1. Data preparation: Using preprocessing tools to scale or transform data.
2. Data splitting: Dividing the dataset into training and testing sets (e.g., 33% for testing).
3. Model instantiation: Creating a model object (e.g., a Support Vector Classification algorithm).
4. Model training: Fitting the model to the training data.
5. Prediction: Using the trained model to make predictions on the test data.
6. Evaluation: Assessing model accuracy using various metrics.
7. Model persistence: Saving the trained model (e.g., as a pickle file) for future use.

In essence, Scikit-learn simplifies the end-to-end machine learning process, from data preparation to model deployment, making it a cornerstone of the Python ML ecosystem.

Podcast: Unpacking the ML Ecosystem: Your Scikit-learn Guide to Practical Machine Learning

A: Machine learning teaches computers to learn from data, identify patterns, and make decisions without receiving explicit instructions from a human being.

A: Build a data pipeline for deploying a machine learning model.

Define the problem, collect the data, preprocess the data, and develop, evaluate, and deploy a model.

Correct

These are the primary stages in the machine learning model lifecycle. This process is often iterative, meaning you may need to revisit earlier steps, like data collection or problem definition, and repeat subsequent steps.

Scikit-learn

Correct

Scikit-learn is a free Python machine learning library designed for building models in classification, regression, clustering, and dimensionality reduction. It is central to Python’s open-source machine learning ecosystem and works seamlessly with NumPy and SciPy.

Pandas

Correct

Pandas is a powerful Python library commonly used for data analysis, visualization, cleaning, and preprocessing to prepare data for downstream tasks, including machine learning modeling.