What is Machine Learning?

IBM Technology
14 Jul 202108:23
EducationalLearning
32 Likes 10 Comments

TLDRThe video explains key AI concepts like machine learning and deep learning, focusing on machine learning techniques. It covers supervised learning using labeled data for classification and regression models, unsupervised learning to analyze unlabeled data and discover patterns like customer segmentation, and reinforcement learning to train systems iteratively through rewards and penalties like with self-driving cars.

Takeaways
  • 😀 AI mimics human decision making; ML is a subset focused on self-learning algorithms; deep learning automates feature extraction
  • 👍 Supervised learning uses labeled data to train algorithms to classify or predict
  • 💡 Regression builds equations to estimate output values using input values and weights
  • 😯 Unsupervised learning analyzes unlabeled data to discover patterns and groupings
  • 🔎 Clustering groups similar customers for targeted marketing
  • 🔬 Dimensionality reduction removes redundant input variables
  • 🤖 Reinforcement learning teaches systems via reward and punishment
  • 🚘 Self-driving cars use reinforcement learning to avoid collisions and obey traffic laws
  • 📚 Many machine learning techniques briefly covered; encourage further research
  • 💻 IBM Cloud Labs provide free Kubernetes labs to grow skills
Q & A

  • What is the difference between artificial intelligence, machine learning, and deep learning?

    -Artificial intelligence is focused on mimicking human decision making and problem solving. Machine learning is a subset of AI that uses algorithms to derive knowledge from data to make predictions. Deep learning is a subset of machine learning that automates feature extraction to enable the use of very large datasets.

  • What are the two main types of supervised machine learning?

    -The two main types of supervised machine learning are classification and regression. Classification recognizes and groups data into predefined categories. Regression generates estimates for an output value based on input values and their weights.

  • How can supervised machine learning help with customer retention?

    -By using historical customer data labeled as churned or not churned, a classification model can be built to identify customers likely to churn. This allows companies to take action to retain those customers.

  • How do airlines use regression in machine learning?

    -Airlines use input factors like days to departure, day of week, origin, and destination to predict ticket prices that will maximize revenue.

  • What is clustering in unsupervised machine learning?

    -Clustering groups unlabeled data into buckets of similar items. For customer segmentation, it can group customers based on purchase history, web activity etc. to improve marketing.

  • What is reinforcement learning?

    -In reinforcement learning, an agent takes actions in an environment and is rewarded or punished. Through iterations, the agent learns to perform a task optimally, like a self-driving car avoiding collisions.

  • What are some real-world applications of machine learning?

    -Some real-world applications are customer retention and churn prediction, targeted marketing and sales, predictive analytics, fraud detection, medical diagnosis, spam filtering, and more.

  • What tools and algorithms are used in machine learning?

    -Common machine learning tools and algorithms include Python scikit-learn, R, Apache Spark MLlib, TensorFlow, PyTorch, logistic regression, random forests, neural networks, support vector machines, and more.

  • How can companies benefit from machine learning?

    -Companies can use machine learning to become more data-driven, automate tasks, achieve cost reductions, unlock hidden insights, improve accuracy of predictions and decision making, develop innovative products and services, and gain a competitive advantage.

  • What skills are required to work in machine learning?

    -Key skills include mathematics/statistics, programming (Python, R), data modeling, data visualization, machine learning theory and algorithms, cloud platforms, and critical thinking for problem solving and evaluation of models.

Outlines
00:00
🎤 Introducing Machine Learning Concepts

The speaker introduces himself as a data solutions engineer at IBM. He provides definitions and differences between AI, machine learning, and deep learning. He states machine learning uses algorithms that derive knowledge from data to make predictions. He then outlines three main types of machine learning - supervised learning, unsupervised learning, and reinforcement learning.

05:05
📊 Supervised & Unsupervised Learning Use Cases

The speaker provides real-world examples of supervised learning for classification (predicting customer churn) and regression (airline ticket pricing). For unsupervised learning, he discusses using clustering for customer segmentation and dimensionality reduction to eliminate redundant variables.

Mindmap
Keywords
💡Artificial Intelligence
Artificial Intelligence (AI) refers to the capability of machines to mimic human-like problem solving and decision making abilities. It is the broadest term that encompasses all automated intelligence demonstrated by machines. The video explains that machine learning is a subset within AI that focuses more specifically on algorithms that can learn from data.
💡Machine Learning
Machine Learning is a subset field within AI that focuses on the development of algorithms that can learn from data and make predictions. The video discusses supervised, unsupervised, and reinforcement learning as three main types of machine learning. Key abilities enabled by machine learning are classification, regression, clustering, and more.
💡Supervised Learning
Supervised learning is a type of machine learning where algorithms are trained on labeled datasets to classify data points into categories or predict target values. It allows teaching the machine using correctly annotated examples. The video provides classification and regression as two main supervised learning techniques.
💡Unsupervised Learning
Unsupervised learning is the machine learning approach of finding patterns and structure in unlabeled datasets without human guidance or intervention. Key unsupervised learning techniques mentioned are clustering, to find groupings within data, and dimensionality reduction to simplify representations.
💡Reinforcement Learning
Reinforcement learning is a machine learning approach based on an agent taking actions in an environment and receiving feedback in terms of rewards and punishments, allowing it to learn tasks. The video explains how reinforcement learning enables capabilities like autonomous driving by enabling an agent to develop driving skills.
💡Classification
Classification is a common supervised machine learning technique to categorize or classify input data points into one of the predefined classes or categories. The video illustrates customer churn prediction for retention as an example business use case of classification.
💡Regression
Regression is a supervised machine learning technique to estimate continuous number outputs based on input data. It works by learning the weights for each input factor from training data. The video explains that airlines use regression models to predict flight ticket prices based on different input variables.
💡Clustering
Clustering is an unsupervised machine learning technique to group data points such that points within a cluster are more similar than those from other clusters. The video mentions customer segmentation for targeted marketing as a key business application of clustering algorithms.
💡Dimensionality Reduction
Dimensionality reduction techniques simplify the representation of dataset by reducing the number of variables under consideration. They identify and focus only on the most significant contributing factors within a dataset.
💡Customer Churn
Customer churn refers to customers discontinuing business with a company. It is an important business metric that machine learning models can help predict so that appropriate customer retention programs can be targeted.
Highlights

AI leverages computers to mimic human problem-solving and decision-making capabilities

Machine learning is a subset of AI focused on using algorithms that derive knowledge from data to predict outcomes

Deep learning automates feature extraction and enables using very large datasets

Supervised learning uses labeled datasets to train algorithms to classify data or predict outcomes

Classification models recognize and group objects into predefined categories like identifying customers likely to churn

Regression builds equations using input values weighted by impact to estimate output values like flight ticket pricing

Unsupervised learning analyzes unlabeled datasets to discover hidden patterns and groupings

Clustering groups similar customers for targeted marketing based on purchase history, activity data etc

Dimensionality reduction removes redundant input variables from datasets

Reinforcement learning teaches systems tasks like driving through reward and punishment iterations

Self-driving cars use reinforcement learning to avoid collisions, follow speed limits etc

Many machine learning techniques barely scratched the surface, encourage diving deeper

Common machine learning algorithms and data science applications in links below

Grow skills with free IBM Cloud Labs interactive Kubernetes labs

Like and subscribe for more machine learning videos

Transcripts

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