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Python Machine Learning Cookbook

  • Course Code: Data Science - Python Machine Learning Cookbook
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 4 Days Audience: This course is geared for those who wants to Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch.

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Python Machine Learning Cookbook skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our Python Machine Learning Cookbook expert instructor, students will learn about and explore: 

  • Learn and implement machine learning algorithms in a variety of real-life scenarios 
  • Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques 
  • Find easy-to-follow code solutions for tackling common and not-so-common challenges 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Use predictive modeling and apply it to real-world problems 
  • Explore data visualization techniques to interact with your data 
  • Learn how to build a recommendation engine 
  • Understand how to interact with text data and build models to analyze it 
  • Work with speech data and recognize spoken words using Hidden Markov Models 
  • Get well versed with reinforcement learning, automated ML, and transfer learning 
  • Work with image data and build systems for image recognition and biometric face recognition 
  • Use deep neural networks to build an optical character recognition system 

Audience & Pre-Requisites 

This course is designed for developers wants to Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. The Realm of Supervised Learning 
  • The Realm of Supervised Learning 
  • 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 
  1. Constructing a Classifier 
  • Constructing a Classifier 
  • 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 
  1. Predictive Modeling 
  • Predictive Modeling 
  • 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 
  1. Clustering with Unsupervised Learning 
  • Clustering with Unsupervised Learning 
  • 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 
  1. Visualizing Data 
  • Visualizing Data 
  • 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 
  1. Building Recommendation Engines 
  • Building Recommendation Engines 
  • 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 
  1. Analyzing Text Data 
  • Analyzing Text Data 
  • 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 
  1. Speech Recognition 
  • Speech Recognition 
  • 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 
  1. Dissecting Time Series and Sequential Data 
  • Dissecting Time Series and Sequential Data 
  • 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 
  1. Analyzing Image Content 
  • Analyzing Image Content 
  • 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 
  1. Biometric Face Recognition 
  • Biometric Face Recognition 
  • 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 
  1. Reinforcement Learning Techniques 
  • Reinforcement Learning Techniques 
  • 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 
  1. Deep Neural Networks 
  • Deep Neural Networks 
  • 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 
  • Building an optical character recognizer using neural networks 
  • Implementing optimization algorithms in ANN 
  1. Unsupervised Representation Learning 
  • Unsupervised Representation Learning 
  • Technical requirements 
  • Introduction 
  • Using denoising autoencoders to detect fraudulent transactions 
  • Generating word embeddings using CBOW and skipgram representations 
  • Visualizing the MNIST dataset using PCA and t-SNE 
  • Using word embedding for Twitter sentiment analysis 
  • Implementing LDA with scikit-learn 
  • Using LDA to classify text documents 
  • Preparing data for LDA 
  1. Automated Machine Learning and Transfer Learning 
  • Automated Machine Learning and Transfer Learning 
  • Technical requirements 
  • Introduction 
  • Working with Auto-WEKA 
  • Using AutoML to generate machine learning pipelines with TPOT 
  • Working with Auto-Keras 
  • Working with auto-sklearn 
  • Using MLBox for selection and leak detection 
  • Convolutional neural networks with transfer learning 
  • Transfer learning with pretrained image classifiers using ResNet-50 
  • Transfer learning using feature extraction with the VGG16 model 
  • Transfer learning with pretrained GloVe embedding 
  1. Unlocking Production Issues 
  • Unlocking Production Issues 
  • Technical requirements 
  • Introduction 
  • Handling unstructured data 
  • Deploying machine learning models 
  • Keeping track of changes into production 
  • Tracking accuracy to optimize model scaling 

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