Python Step Into The World Of Machine Learning
Last updated 2/2017
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 800.09 MB | Duration: 7h 1m
Apply your existing Python skills to the highly lucrative fields of machine learning and deep learning.
What you'll learn
Explore and use Python's impressive machine learning ecosystem
Understand the different types of machine learning
Learn predictive modeling and apply it to real-world problems
Work with image data and build systems for image recognition and biometric face recognition
Build your own applications using machine learning
Build simple TensorFlow graphs for everyday computations
Requirements
Basic knowledge of Python syntax
Python 3.x installed on your machine
Description
Are you looking at improving and extending the capabilities of your machine learning systems? Or looking for a career in the field of machine learning? If yes, then this course is for you.
ML is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields, such as search engines, robotics, self-driving cars, and more. It is transforming the way businesses operate. Being able to understand the trends and patterns in complex data is critical to success. In a challenging marketplace, it is one of the key strategies for unlocking growth.
The aim of the course is to teach you how to process various types of data, including how and when to apply different machine learning techniques.
We cover a wide range of powerful machine learning algorithms, alongside expert guidance and tips on everything from sentiment analysis to neural networks. You'll soon be able to answer some of the most important questions that you and your organization face.
Why should I choose this course?
This course is a blend of text, videos, code examples, quizzes, and coding challenges which together makes your learning journey all the more exciting and truly rewarding. It includes sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. This helps you learn a range of topics at your own speed and also move towards your goal of learning machine learning.
Testimonials
The source content have been received well by the audience. Here are a couple of reviews
"The author has communicated with clarity for the individual who would like to learn the practical aspects of implementing learning algorithms of today and for the future. Excellent work, up-to-date and very relevant for the applications of the day!"
- Anonymous Customer.
"Very helpful and objective."
- Fabiano Souza
"I would definitely recommend this to people who want to get started with machine learning in Python."
- Spoorthi V.
What is included?
Let's dig into what this course covers. Since you already know the basics of Python, you are no stranger to the fact that it is an immensely powerful language. With the basics in place, this course takes a hands-on approach and demonstrates how you can perform various machine learning tasks on real-world data.
The course starts by talking about various realms in machine learning followed by practical examples. It then moves on to discuss the more complex algorithms, such as Support Vector Machines, Extremely Random Forests, Hidden Markov Models, Sentiment Analysis, and Conditional Random Fields. You will learn how to make informed decisions about the types of algorithm that you need to use and how to implement these algorithms to get the best possible results.
After you are comfortable with machine learning, this course teaches you how to build real-world machine learning applications step by step. Further, we'll explore deep learning with TensorFlow, which is currently the hottest topic in data science. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change the way you look at data. You will also learn how to train your machine to build new models that help make sense of deeper layers within your data.
By the end of this course, you should be able to solve real-world data analysis challenges using innovative and cutting-edge machine learning techniques.
We have combined the best of the following Packt products
Python Machine Learning Cookbook and Python Machine Learning Solutions by Prateek JoshiPython Machine Learning Blueprints and Python Machine Learning Projects by Alexander T. CombsDeep Learning with TensorFlow by Dan Van BoxelGetting Started with TensorFlow by Giancarlo ZacconePython Machine Learning by Sebastian RaschkaBuilding Machine Learning Systems with Python - Second Edition by Luis Pedro Coelho and Willi Richert
Meet your expert instructors
Prateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open Silicon Valley. His tech blog has received more than 1.2 million page views from 200 over countries and has over 6,600+ followers.
Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling.
Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is most well-known for "Dan Does Data", a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas.
Giancarlo Zaccone, a physicist, has been involved in scientific computing projects among firms and research institutions. He currently works in an IT company that designs software systems with high technological content. He currently works in an IT company that designs software systems with high technological content.
Sebastian Raschka has been ranked as the number one most influential data scientist on GitHub by Analytics Vidhya. He has many years of experience with coding in Python and conducted several seminars on the practical applications of data science and machine learning. He has also actively contributed to open source projects and methods that he implemented, which are now successfully used in machine learning competitions, such as Kaggle.
Luis Pedro Coelho is a computational biologist. He analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informatics—the application of machine learning techniques for the analysis of images of biological specimens. He has a PhD from Carnegie Mellon University, one of the leading universities in the world in the area of machine learning. He is the author of several scientific publications.
Willi Richert has a PhD in machine learning/robotics, where he used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Currently, he works for Microsoft in the Core Relevance Team of Bing, where he is involved in a variety of ML areas such as active learning, statistical machine translation, and growing decision trees.
Overview
Section 1: Getting Started with Python Machine Learning
Lecture 1 Course Introduction
Lecture 2 An Introduction to Machine Learning
Section 2: The Realm of Supervised Learning
Lecture 3 Preprocessing data using different techniques
Lecture 4 Label encoding
Lecture 5 Building a linear regressor
Lecture 6 Computing regression accuracy and achieving model persistence
Lecture 7 Building a ridge regressor
Lecture 8 Building a polynomial regressor
Lecture 9 Estimating housing prices
Lecture 10 Computing the relative importance of features
Lecture 11 Estimating bicycle demand distribution
Section 3: Constructing a Classifier
Lecture 12 Building a logistic regression classifier
Lecture 13 Building a Naive Bayes classifier
Lecture 14 Splitting the dataset for training and testing
Lecture 15 Evaluating the accuracy using cross-validation
Lecture 16 Visualizing the confusion matrix
Lecture 17 Extracting the performance report
Lecture 18 Evaluating cars based on their characteristics
Lecture 19 Extracting validation curves
Lecture 20 Extracting learning curves
Lecture 21 Estimating the income bracket
Section 4: Predictive Modeling
Lecture 22 Building a linear classifier using Support Vector Machine (SVMs)
Lecture 23 Building a nonlinear classifier using SVMs
Lecture 24 Tackling class imbalance
Lecture 25 Extracting confidence measurements
Lecture 26 Finding optimal hyperparameters
Lecture 27 Building an event predictor
Lecture 28 Estimating traffic
Section 5: Clustering with Unsupervised Learning
Lecture 29 Clustering data using the k-means algorithm
Lecture 30 Compressing an image using vector quantization
Lecture 31 Building a Mean Shift clustering model
Lecture 32 Grouping data using agglomerative clustering
Lecture 33 Evaluating the performance of clustering algorithms
Lecture 34 Automatically estimating the number of clusters using DBSCAN algorithm
Lecture 35 Finding patterns in stock market data
Lecture 36 Building a customer segmentation model
Section 6: Building Recommendation Engines
Lecture 37 Building function compositions for data processing
Lecture 38 Building machine learning pipelines
Lecture 39 Finding the nearest neighbors
Lecture 40 Constructing a k-nearest neighbors classifier and regressor
Lecture 41 Computing the Euclidean distance score
Lecture 42 Computing the Pearson correlation score
Lecture 43 Finding similar users in the dataset
Lecture 44 Generating movie recommendations
Lecture 45 Building a simple classifier
Section 7: Analyzing Text Data
Lecture 46 Preprocessing data using tokenization
Lecture 47 Stemming text data
Lecture 48 Converting text to its base form using lemmatization
Lecture 49 Dividing text using chunking
Lecture 50 Building a bag-of-words model
Lecture 51 Building a text classifier
Lecture 52 Identifying the gender
Lecture 53 Analyzing the sentiment of a sentence
Lecture 54 Identifying patterns in text using topic modeling
Section 8: Speech Recognition
Lecture 55 Reading and plotting audio data
Lecture 56 Generating audio signals with custom parameters
Lecture 57 Synthesizing music
Lecture 58 Extracting frequency domain features
Lecture 59 Building Hidden Markov Models
Lecture 60 Building a speech recognizer
Lecture 61 Transforming audio signals into the frequency domain
Section 9: Dissecting Time Series and Sequential Data
Lecture 62 Transforming data into the time series format
Lecture 63 Slicing time series data
Lecture 64 Operating on time series data
Lecture 65 Extracting statistics from time series data
Lecture 66 Building Hidden Markov Models for sequential data
Lecture 67 Building Conditional Random Fields for sequential text data
Lecture 68 Analyzing stock market data using Hidden Markov Models
Section 10: Image Content Analysis
Lecture 69 Operating on images using OpenCV-Python
Lecture 70 Detecting edges
Lecture 71 Histogram equalization
Lecture 72 Detecting corners and SIFT feature points
Lecture 73 Building a Star feature detector
Lecture 74 Creating features using visual codebook and vector quantization
Lecture 75 Training an image classifier using Extremely Random Forests
Lecture 76 Building an object recognizer
Section 11: Biometric Face Recognition
Lecture 77 Capturing and processing video from a webcam
Lecture 78 Building a face detector using Haar cascades
Lecture 79 Building eye and nose detectors
Lecture 80 Performing Principal Components Analysis
Lecture 81 Performing Kernel Principal Components Analysis
Lecture 82 Performing blind source separation
Lecture 83 Building a face recognizer using Local Binary Patterns Histogram
Section 12: Visualizing Data
Lecture 84 Plotting 3D scatter plots
Lecture 85 Plotting and animating bubble plots
Lecture 86 Drawing pie charts
Lecture 87 Plotting date-formatted time series data
Lecture 88 Plotting histograms
Lecture 89 Visualizing heat maps
Lecture 90 Animating dynamic signals
Section 13: Building Your First App using Machine Learning
Lecture 91 Build an app to find underpriced apartments
Lecture 92 Your Coding Challenge
Section 14: Forecasting the Stock Market with Machine Learning
Lecture 93 What does research tell us about the stock market?
Lecture 94 Developing a trading strategy
Lecture 95 Building a model and evaluating its performance
Lecture 96 Modeling with dynamic time warping
Section 15: Building a Chatbot
Lecture 97 The design of chatbots
Lecture 98 Building a chatbot
Lecture 99 Your Coding Challenge
Section 16: Deep Learning with TensorFlow
Lecture 100 An introduction to deep learning and TensorFlow
Lecture 101 Installing TensorFlow
Lecture 102 Simple computations
Lecture 103 Logistic regression model building
Lecture 104 Logistic regression training
Section 17: Deep Neural Networks
Lecture 105 Basic neural nets
Lecture 106 Single hidden layer model
Lecture 107 Single hidden layer explained
Lecture 108 Multiple hidden layer model
Lecture 109 Multiple hidden layer results
Section 18: Convulation Neural Networks
Lecture 110 Convolutional layer motivation
Lecture 111 Convolutional layer application
Lecture 112 Pooling layer motivation
Lecture 113 Pooling layer application
Lecture 114 Deep CNN
Lecture 115 Deeper CNN
Lecture 116 Wrapping up deep CNN
Section 19: Recurrent Neural Network
Lecture 117 Introducing Recurrent Neural Networks
Lecture 118 skflow
Lecture 119 RNNs in skflow
Section 20: Wrapping Up
Lecture 120 Research evaluation
Lecture 121 The future of TensorFlow
This course is for Python programmers, developers, and data scientists looking to use machine learning algorithms and techniques to create real-world applications,Some familiarity with Python programming will certainly be helpful to play around with the code,If you want to become a machine learning practitioner, a better problem solver, or maybe even consider a career in machine learning research, then this course is for you.
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