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Home » Technology » AI / Machine Learning » Machine Learning

Course Skill Level:

Foundational

Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    ML0000L21E09

Who should attend & recommended skills:

Those with Python experience and basic IT & Linux skills looking to build programs that implement algorithms

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who wish to learn flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. This course introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.
  • Skill-level: Foundation-level machine learning skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Python: Basics (1-2 years’ experience) helpful
  • Machine Learning: Not required
  • Statistical Processing: Not required
  • No prior experience with .
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them.

About this course

A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you’ll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You’ll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:
  • Use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse.
  • The concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.
  • A no-nonsense introduction
  • Examples showing common ML tasks
  • Everyday data analysis
  • Implementing classic algorithms like Apriori and Adobos

Course breakdown / modules

  • What is machine learning?
  • Key terminology
  • Key tasks of machine learning
  • How to choose the right algorithm
  • Steps in developing a machine learning application
  • Why Python?
  • Getting started with the NumPy library

  • Classifying with distance measurements
  • Example: improving matches from a dating site with kNN
  • Example: a handwriting recognition system

  • Tree construction
  • Plotting trees in Python with Matplotlib annotations
  • annotations
  • Testing and storing the classifier
  • Example: using decision trees to predict contact lens type

  • Classifying with Bayesian decision theory
  • Conditional probability
  • Classifying with conditional probabilities
  • Document classification with Naïve Bayes
  • Classifying text with Python
  • Example: classifying spam email with Naive Bayes
  • Example: using Naive Bayes to reveal local attitudes from personal ads

  • Classification with logistic regression and the sigmoid function: -: a tractable step function
  • Using optimization to find the best regression coefficients
  • Example: estimating horse fatalities from colic

  • Separating data with the maximum margin
  • Finding the maximum margin
  • Efficient optimization with the SMO algorithm
  • Speeding up optimization with the full Platt SMO
  • Using kernels for more complex data
  • Example: revisiting handwriting classification

  • Classifiers using multiple samples of the dataset
  • Train: improving the classifier by focusing on errors
  • Creating a weak learner with a decision stump
  • Implementing the full AdaBoost algorithm
  • Test: classifying with AdaBoost
  • Example: AdaBoost on a difficult dataset
  • Classification imbalance

  • Finding best-fit lines with linear regression
  • Locally weighted linear regression
  • Example: predicting the age of an abalone
  • Shrinking coefficients to understand our data
  • The bias/variance tradeoff
  • Example: forecasting the price of LEGO sets

  • Locally modeling complex data
  • Building trees with continuous and discrete features
  • Using CART for regression
  • Tree pruning
  • Model trees
  • Example: comparing tree methods to standard regression
  • Using Tkinter to create a GUI in Python

  • The k-means clustering algorithm
  • Improving cluster performance with postprocessing
  • Bisecting k-means
  • Example: clustering points on a map

  • Association analysis
  • The Apriori principle
  • Finding frequent itemset with the Apriori algorithm
  • Mining association rules from frequent item sets
  • Example: uncovering patterns in congressional voting
  • Example: finding similar features in poisonous mushrooms

  • FP-trees: an efficient way to encode a dataset
  • Build an FP-tree
  • Mining frequent items from an FP-tree
  • Example: finding co-occurring words in a Twitter feed
  • Example: mining a clickstream from a news site

  • Dimensionality reduction techniques
  • Principal component analysis
  • Example: using PCA to reduce the dimensionality of semiconductor -manufacturing data

  • Applications of the SVD
  • Matrix factorization
  • SVD in Python
  • Collaborative filtering based recommendation engines
  • Example: a restaurant dish recommendation engine
  • Example: image compression with the SVD

  • MapReduce: a framework for distributed computing
  • Hadoop Streaming
  • Running Hadoop jobs on Amazon Web Services
  • Machine learning in MapReduce
  • Using mrjob to automate MapReduce in Python
  • Example: the Pegasos algorithm for distributed SVMs
  • Vector machines with mrjob
  • Do you really need MapReduce?

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