Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn

Video description

Have you been looking for a course that teaches you effective machine learning in scikit-learn and TensorFlow 2.0? Or have you always wanted an efficient and skilled working knowledge of how to solve problems that can't be explicitly programmed through the latest machine learning techniques?

If you're familiar with pandas and NumPy, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data.

The theory will be underpinned with plenty of practical examples, and code example walk-throughs in Jupyter notebooks. The course aims to make you highly efficient at constructing algorithms and models that perform with the highest possible accuracy based on the success output or hypothesis you've defined for a given task.

By the end of this course, you will be able to comfortably solve an array of industry-based machine learning problems by training, optimizing, and deploying models into production. Being able to do this effectively will allow you to create successful prediction and decisions for the task in hand (for example, creating an algorithm to read a labeled dataset of handwritten digits).

What You Will Learn

  • Fundamentals of machine learning (and introducing the benefits of scikit-learn)
  • Practical implementation with comprehensive examples of canonical machine learning, and supervised and unsupervised machine learning in scikit-learn
  • How to identify a problem, select the right model, and optimize it to get the best desired outcome: insights into data
  • TensorFlow 2.0 for deep learning with neural networks
  • Deep learning and image-classification examples, and time series predictive model examples
  • Reinforcement learning, and how to implement various types with examples
  • Effectively use scikit-learn and TensorFlow in your production system, including framing a task in each task example

Audience

This course is for developers who are familiar with pandas and NumPy concepts and are keen to develop their machine learning methodologies and practices effectively using scikit-learn and TensorFlow 2.0.

Requirement:Prior Python programming knowledge is mandatory for this course.

About The Author

Samuel Holt: Samuel Holt has several years' experience implementing, creating, and putting into production machine learning models for large blue-chip companies and small startups (as well as within his own companies) as a machine learning consultant.

He has machine learning lab experience and holds an MEng in Machine Learning and Software Engineering from Oxford University, where he won four awards for academic excellence.

Specifically, he has built systems that run in production using a combination of scikit-learn and TensorFlow involving automated customer support, implementing document OCR, detecting vehicles in the case of self-driving cars, comment analysis, and time series forecasting for financial data.

Publisher resources

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Table of contents

  1. Chapter 1 : Installing Scikit-Learn and TensorFlow 2.0
    1. Course Overview
    2. Overview of the Anaconda Distribution
    3. Installing the Anaconda Distribution for Scikit-Learn
    4. Installing TensorFlow 2.0 from the Anaconda Distribution
    5. Install Scikit-Learn and Tensorflow 2.0 Manually Through pip
  2. Chapter 2 : ML Fundamentals: Scikit-Learn Introduction
    1. What Is Machine Learning?
    2. First Scikit-Learn Model
    3. Overfitting and Regularization
    4. Probability and Statistics Review
    5. Probability Distribution and Metrics
  3. Chapter 3 : Applied Scikit-Learn: Supervised Learning Models
    1. Supervised Learning and KNN
    2. Logistic Regression
    3. Naïve Bayes
    4. Support Vector Machines
    5. Decision Trees
    6. Ensemble Methods
  4. Chapter 4 : Unsupervised Learning
    1. K-means and Hierarchical Clustering
    2. Connectivity and Density Clustering
    3. Gaussian Mixture Models
    4. Variational Bayesian Gaussian Mixture Models
    5. Decomposing Signals into Components
    6. Signal Decomposition with Factor and Independent Component Analysis
    7. Novelty Detection
    8. Outlier Detection
    9. Locally Linear Embedded Manifolds
    10. Multi-Dimensional Scaling and t-SNE Manifolds
    11. Density Estimation
    12. Restricted Boltzmann Machine
  5. Chapter 5 : TensorFlow 2.0 Essentials for ML
    1. TensorFlow 2.0 Overview
    2. TensorFlow 2.0's Gradient Tape
    3. Working with Neural Networks and Keras
    4. Keras Customization
    5. Custom Networks in Keras
    6. Core Neural Network Concepts
    7. Regression and Transfer Learning
    8. TensorFlow Estimators and TensorBoard
  6. Chapter 6 : Applied Deep Learning for Computer Vision Tasks
    1. Introduction to ConvNets
    2. ConvNets In Keras
    3. Image Classification with Data Augmentation
    4. Convolutional Autoencoders
    5. Denoising and Variational Autoencoders
    6. Custom Generative Adversarial Networks
    7. Semantic Segmentation
    8. Neural Style Transfer
  7. Chapter 7 : Natural Language Processing and Sequential Data
    1. Using Word Embeddings
    2. Text Pipeline with Tokenization for Classification
    3. Sequential Data with Recurrent Neural Networks
    4. Best Practices with Recurrent Neural Networks
    5. Time Series Forecasting
    6. Forecasting with CNNs and RNNs
  8. Chapter 8 : Applied Sequence to Sequence and Transformer Models
    1. NLP Language Models
    2. Generating Text from an LSTM
    3. Sequence to Sequence Models
    4. MT Seq2Seq with Attention
    5. NLP Transformers
    6. Training Transformers and NLP In Practice
  9. Chapter 9 : Working with Reinforcement Learning
    1. Basics of Reinforcement Learning
    2. Training a Deep Q-Network with TF-Agents
    3. TF-agents In Depth
    4. Value and Policy Based Methods
    5. Exploration Techniques and Uncertainty In RL
    6. Imitation Learning and AlphaZero

Product information

  • Title: Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn
  • Author(s): Samuel Holt
  • Release date: June 2020
  • Publisher(s): Packt Publishing
  • ISBN: 9781789959161