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Course Skill Level:

Foundational to Intermediate

Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    PYMLCBL21E09

Who should attend & recommended skills:

Developers with Python experience

Who should attend & recommended skills

  • This course is geared for developers who want to discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch.
  • Skill-level: Foundation-level Python Machine Learning Cookbook skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

This eagerly anticipated edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The course will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the course will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding lessons, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this course, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.

Skills acquired & topics covered

  • Learning and implementing machine learning algorithms in a variety of real-life scenarios
  • Covering a range of tasks catering to supervised, unsupervised and reinforcement learning techniques
  • Easy-to-follow code solutions for tackling common and not-so-common challenges
  • Using predictive modeling and apply it to real-world problems
  • Data visualization techniques to interact with your data
  • How to build a recommendation engine
  • How to interact with text data and build models to analyze it
  • Working with speech data and recognize spoken words using Hidden Markov Models
  • Getting well versed with reinforcement learning, automated ML, and transfer learning
  • Working with image data and build systems for image recognition and biometric face recognition
  • Using deep neural networks to build an optical character recognition system

Course breakdown / modules

  • Technical requirements
  • Introduction
  • Array creation in Python
  • Data preprocessing using mean removal
  • Data scaling
  • Normalization
  • Binarization
  • One-hot encoding
  • Label encoding
  • Building a linear regressor
  • Computing regression accuracy
  • Achieving model persistence
  • Building a ridge regressor
  • Building a polynomial regressor
  • Estimating housing prices
  • Computing the relative importance of features
  • Estimating bicycle demand distribution

  • Technical requirements
  • Introduction
  • Building a simple classifier
  • Building a logistic regression classifier
  • Building a Naive Bayes classifier
  • Splitting a dataset for training and testing
  • Evaluating accuracy using cross-validation metrics
  • Visualizing a confusion matrix
  • Extracting a performance report
  • Evaluating cars based on their characteristics
  • Extracting validation curves
  • Extracting learning curves
  • Estimating the income bracket
  • Predicting the quality of wine
  • Newsgroup trending topics classification

  • Technical requirements
  • Introduction
  • Building a linear classifier using SVMs
  • Building a nonlinear classifier using SVMs
  • Tackling class imbalance
  • Extracting confidence measurements
  • Finding optimal hyperparameters
  • Building an event predictor
  • Estimating traffic
  • Simplifying machine learning workflow using TensorFlow
  • Implementing a stacking method

  • Technical requirements
  • Introduction
  • Clustering data using the k-means algorithm
  • Compressing an image using vector quantization
  • Grouping data using agglomerative clustering
  • Evaluating the performance of clustering algorithms
  • Estimating the number of clusters using the DBSCAN algorithm
  • Finding patterns in stock market data
  • Building a customer segmentation model
  • Using autoencoders to reconstruct handwritten digit images

  • Technical requirements
  • An introduction to data visualization
  • Plotting three-dimensional scatter plots
  • Plotting bubble plots
  • Animating bubble plots
  • Drawing pie charts
  • Plotting date-formatted time series data
  • Plotting histograms
  • Visualizing heat maps
  • Animating dynamic signals
  • Working with the Seaborn library

  • Technical requirements
  • Introducing the recommendation engine
  • Building function compositions for data processing
  • Building machine learning pipelines
  • Finding the nearest neighbors
  • Constructing a k-nearest neighbors classifier
  • Constructing a k-nearest neighbors regressor
  • Computing the Euclidean distance score
  • Computing the Pearson correlation score
  • Finding similar users in the dataset
  • Generating movie recommendations
  • Implementing ranking algorithms
  • Building a filtering model using TensorFlow

  • Technical requirements
  • Introduction
  • Preprocessing data using tokenization
  • Stemming text data
  • Converting text to its base form using lemmatization
  • Dividing text using chunking
  • Building a bag-of-words model
  • Building a text classifier
  • Identifying the gender of a name
  • Analyzing the sentiment of a sentence
  • Identifying patterns in text using topic modeling
  • Parts of speech tagging with spaCy
  • Word2Vec using gensim
  • Shallow learning for spam detection

  • Technical requirements
  • Introducing speech recognition
  • Reading and plotting audio data
  • Transforming audio signals into the frequency domain
  • Generating audio signals with custom parameters
  • Synthesizing music
  • Extracting frequency domain features
  • Building HMMs
  • Building a speech recognizer
  • Building a TTS system

  • Technical requirements
  • Introducing time series
  • Transforming data into a time series format
  • Slicing time series data
  • Operating on time series data
  • Extracting statistics from time series data
  • Building HMMs for sequential data
  • Building CRFs for sequential text data
  • Analyzing stock market data
  • Using RNNs to predict time series data

  • Technical requirements
  • Introducing computer vision
  • Operating on images using OpenCV-Python
  • Detecting edges
  • Histogram equalization
  • Detecting corners
  • Detecting SIFT feature points
  • Building a Star feature detector
  • Creating features using Visual Codebook and vector quantization
  • Training an image classifier using Extremely Random Forests
  • Building an object recognizer
  • Using Light GBM for image classification

  • Technical requirements
  • Introduction
  • Capturing and processing video from a webcam
  • Building a face detector using Haar cascades
  • Building eye and nose detectors
  • Performing principal component analysis
  • Performing kernel principal component analysis
  • Performing blind source separation
  • Building a face recognizer using a local binary patterns histogram
  • Recognizing faces using the HOG-based model
  • Facial landmark recognition
  • User authentication by face recognition

  • Technical requirements
  • Introduction
  • Weather forecasting with MDP
  • Optimizing a financial portfolio using DP
  • Finding the shortest path
  • Deciding the discount factor using Q-learning
  • Implementing the deep Q-learning algorithm
  • Developing an AI-based dynamic modeling system
  • Deep reinforcement learning with double Q-learning
  • Deep Q-network algorithm with dueling Q-learning

  • Technical requirements
  • Introduction
  • Building a perceptron
  • Building a single layer neural network
  • Building a deep neural network
  • Creating a vector quantizer
  • Building a recurrent neural network for sequential data analysis
  • Visualizing the characters in an OCR database