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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.

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Last Updated

January 6, 2021

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Certification

Course Description

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.

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Course Curriculum

    • The Realm of Supervised Learning 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Array creation in Python 00:00:00
    • Data preprocessing using mean removal 00:00:00
    • Data scaling 00:00:00
    • Normalization 00:00:00
    • Binarization 00:00:00
    • One-hot encoding 00:00:00
    • Label encoding 00:00:00
    • Building a linear regressor 00:00:00
    • Computing regression accuracy 00:00:00
    • Achieving model persistence 00:00:00
    • Building a ridge regressor 00:00:00
    • Building a polynomial regressor 00:00:00
    • Estimating housing prices 00:00:00
    • Computing the relative importance of features 00:00:00
    • Estimating bicycle demand distribution 00:00:00
    • Constructing a Classifier 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Building a simple classifier 00:00:00
    • Building a logistic regression classifier 00:00:00
    • Building a Naive Bayes classifier 00:00:00
    • Splitting a dataset for training and testing 00:00:00
    • Evaluating accuracy using cross-validation metrics 00:00:00
    • Visualizing a confusion matrix 00:00:00
    • Extracting a performance report 00:00:00
    • Evaluating cars based on their characteristics 00:00:00
    • Extracting validation curves 00:00:00
    • Extracting learning curves 00:00:00
    • Estimating the income bracket 00:00:00
    • Predicting the quality of wine 00:00:00
    • Newsgroup trending topics classification 00:00:00
    • Predictive Modeling 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Building a linear classifier using SVMs 00:00:00
    • Building a nonlinear classifier using SVMs 00:00:00
    • Tackling class imbalance 00:00:00
    • Extracting confidence measurements 00:00:00
    • Finding optimal hyperparameters 00:00:00
    • Building an event predictor 00:00:00
    • Estimating traffic 00:00:00
    • Simplifying machine learning workflow using TensorFlow 00:00:00
    • Implementing a stacking method 00:00:00
    • Clustering with Unsupervised Learning 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Clustering data using the k-means algorithm 00:00:00
    • Compressing an image using vector quantization 00:00:00
    • Grouping data using agglomerative clustering 00:00:00
    • Evaluating the performance of clustering algorithms 00:00:00
    • Estimating the number of clusters using the DBSCAN algorithm 00:00:00
    • Finding patterns in stock market data 00:00:00
    • Building a customer segmentation model 00:00:00
    • Using autoencoders to reconstruct handwritten digit images 00:00:00
    • Visualizing Data 00:00:00
    • Technical requirements 00:00:00
    • An introduction to data visualization 00:00:00
    • Plotting three-dimensional scatter plots 00:00:00
    • Plotting bubble plots 00:00:00
    • Animating bubble plots 00:00:00
    • Drawing pie charts 00:00:00
    • Plotting date-formatted time series data 00:00:00
    • Plotting histograms 00:00:00
    • Visualizing heat maps 00:00:00
    • Animating dynamic signals 00:00:00
    • Working with the Seaborn library 00:00:00
    • Building Recommendation Engines 00:00:00
    • Technical requirements 00:00:00
    • Introducing the recommendation engine 00:00:00
    • Building function compositions for data processing 00:00:00
    • Building machine learning pipelines 00:00:00
    • Finding the nearest neighbors 00:00:00
    • Constructing a k-nearest neighbors classifier 00:00:00
    • Constructing a k-nearest neighbors regressor 00:00:00
    • Computing the Euclidean distance score 00:00:00
    • Computing the Pearson correlation score 00:00:00
    • Finding similar users in the dataset 00:00:00
    • Generating movie recommendations 00:00:00
    • Implementing ranking algorithms 00:00:00
    • Building a filtering model using TensorFlow 00:00:00
    • Analyzing Text Data 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Preprocessing data using tokenization 00:00:00
    • Stemming text data 00:00:00
    • Converting text to its base form using lemmatization 00:00:00
    • Dividing text using chunking 00:00:00
    • Building a bag-of-words model 00:00:00
    • Building a text classifier 00:00:00
    • Identifying the gender of a name 00:00:00
    • Analyzing the sentiment of a sentence 00:00:00
    • Identifying patterns in text using topic modeling 00:00:00
    • Parts of speech tagging with spaCy 00:00:00
    • Word2Vec using gensim 00:00:00
    • Shallow learning for spam detection 00:00:00
    • Speech Recognition 00:00:00
    • Technical requirements 00:00:00
    • Introducing speech recognition 00:00:00
    • Reading and plotting audio data 00:00:00
    • Transforming audio signals into the frequency domain 00:00:00
    • Generating audio signals with custom parameters 00:00:00
    • Synthesizing music 00:00:00
    • Extracting frequency domain features 00:00:00
    • Building HMMs 00:00:00
    • Building a speech recognizer 00:00:00
    • Building a TTS system 00:00:00
    • Dissecting Time Series and Sequential Data 00:00:00
    • Technical requirements 00:00:00
    • Introducing time series 00:00:00
    • Transforming data into a time series format 00:00:00
    • Slicing time series data 00:00:00
    • Operating on time series data 00:00:00
    • Extracting statistics from time series data 00:00:00
    • Building HMMs for sequential data 00:00:00
    • Building CRFs for sequential text data 00:00:00
    • Analyzing stock market data 00:00:00
    • Using RNNs to predict time series data 00:00:00
    • Analyzing Image Content 00:00:00
    • Technical requirements 00:00:00
    • Introducing computer vision 00:00:00
    • Operating on images using OpenCV-Python 00:00:00
    • Detecting edges 00:00:00
    • Histogram equalization 00:00:00
    • Detecting corners 00:00:00
    • Detecting SIFT feature points 00:00:00
    • Building a Star feature detector 00:00:00
    • Creating features using Visual Codebook and vector quantization 00:00:00
    • Training an image classifier using Extremely Random Forests 00:00:00
    • Building an object recognizer 00:00:00
    • Using Light GBM for image classification 00:00:00
    • Biometric Face Recognition 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Capturing and processing video from a webcam 00:00:00
    • Building a face detector using Haar cascades 00:00:00
    • Building eye and nose detectors 00:00:00
    • Performing principal component analysis 00:00:00
    • Performing kernel principal component analysis 00:00:00
    • Performing blind source separation 00:00:00
    • Building a face recognizer using a local binary patterns histogram 00:00:00
    • Recognizing faces using the HOG-based model 00:00:00
    • Facial landmark recognition 00:00:00
    • User authentication by face recognition 00:00:00
    • Reinforcement Learning Techniques 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Weather forecasting with MDP 00:00:00
    • Optimizing a financial portfolio using DP 00:00:00
    • Finding the shortest path 00:00:00
    • Deciding the discount factor using Q-learning 00:00:00
    • Implementing the deep Q-learning algorithm 00:00:00
    • Developing an AI-based dynamic modeling system 00:00:00
    • Deep reinforcement learning with double Q-learning 00:00:00
    • Deep Q-network algorithm with dueling Q-learning 00:00:00
    • Deep Neural Networks 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Building a perceptron 00:00:00
    • Building a single layer neural network 00:00:00
    • Building a deep neural network 00:00:00
    • Creating a vector quantizer 00:00:00
    • Building a recurrent neural network for sequential data analysis 00:00:00
    • Visualizing the characters in an OCR database 00:00:00
    • Building an optical character recognizer using neural networks 00:00:00
    • Implementing optimization algorithms in ANN 00:00:00
    • Unsupervised Representation Learning 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Using denoising autoencoders to detect fraudulent transactions 00:00:00
    • Generating word embeddings using CBOW and skipgram representations 00:00:00
    • Visualizing the MNIST dataset using PCA and t-SNE 00:00:00
    • Using word embedding for Twitter sentiment analysis 00:00:00
    • Implementing LDA with scikit-learn 00:00:00
    • Using LDA to classify text documents 00:00:00
    • Preparing data for LDA 00:00:00
    • Automated Machine Learning and Transfer Learning 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Working with Auto-WEKA 00:00:00
    • Using AutoML to generate machine learning pipelines with TPOT 00:00:00
    • Working with Auto-Keras 00:00:00
    • Working with auto-sklearn 00:00:00
    • Using MLBox for selection and leak detection 00:00:00
    • Convolutional neural networks with transfer learning 00:00:00
    • Transfer learning with pretrained image classifiers using ResNet-50 00:00:00
    • Transfer learning using feature extraction with the VGG16 model 00:00:00
    • Transfer learning with pretrained GloVe embedding 00:00:00
    • Unlocking Production Issues 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Handling unstructured data 00:00:00
    • Deploying machine learning models 00:00:00
    • Keeping track of changes into production 00:00:00
    • Tracking accuracy to optimize model scaling 00:00:00

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