Удаление [Udemy] Machine Learning Data Science and Deep Learning with Python [ENG-RUS]

Machine Learning, Data Science and Deep Learning with Python
Udemy на русском
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Автор: Frank Kane

Студия: Udemy

Год выпуска: 01.2019

Продолжительность: 12:03:31

Язык: Английский

Программное обеспечение: Python

Спойлер: Оригинальное описание

Чему вы научитесь

• Build artificial neural networks with Tensorflow and Keras
• Make predictions using linear regression, polynomial regression, and multivariate regression
• Classify images, data, and sentiments using deep learning
• Implement machine learning at massive scale with Apache Spark's MLLib
• Understand reinforcement learning - and how to build a Pac-Man bot
• Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
• Use train/test and K-Fold cross validation to choose and tune your models
• Build a movie recommender system using item-based and user-based collaborative filtering
• Clean your input data to remove outliers
• Design and evaluate A/B tests using T-Tests and P-Values

Материалы курса 91 лекция12:03:31

1. Getting Started 53:03
   1. Introduction 02:41
   2. Udemy 101: Getting the Most From This Course 02:10
   3. [Activity] Getting What You Need 02:36
   4. [Activity] Installing Enthought Canopy 05:48
   5. Python Basics, Part 1 [Optional] 15:58
   6. [Activity] Python Basics, Part 2 [Optional] 09:41
   7. Running Python Scripts [Optional] 03:55
   8. Introducing the Pandas Library [Optional] 10:14
2. Statistics and Probability Refresher, and Python Practice 01:39:05
   1. Types of Data 06:58
   2. Mean, Median, Mode 05:26
   3. [Activity] Using mean, median, and mode in Python 08:30
   4. [Activity] Variation and Standard Deviation 11:12
   5. Probability Density Function; Probability Mass Function 03:27
   6. Common Data Distributions 07:45
   7. [Activity] Percentiles and Moments 12:33
   8. [Activity] A Crash Course in matplotlib 13:46
   9. [Activity] Covariance and Correlation 11:31
   10. [Exercise] Conditional Probability 10:16
   11. Exercise Solution: Conditional Probability of Purchase by Age 02:18
   12. Bayes' Theorem 05:23
3. Predictive Models 33:33
   1. [Activity] Linear Regression 11:01
   2. [Activity] Polynomial Regression 08:04
   3. [Activity] Multivariate Regression, and Predicting Car Prices 09:52
   4. Multi-Level Models 04:36
4. Machine Learning with Python 01:17:54
   1. Supervised vs. Unsupervised Learning, and Train/Test 08:57
   2. [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression 05:47
   3. Bayesian Methods: Concepts 03:59
   4. [Activity] Implementing a Spam Classifier with Naive Bayes 08:05
   5. K-Means Clustering 07:23
   6. [Activity] Clustering people based on income and age 05:14
   7. Measuring Entropy 03:09
   8. [Activity] Install GraphViz 00:48
   9. Decision Trees: Concepts 08:43
   10. [Activity] Decision Trees: Predicting Hiring Decisions 09:47
   11. Ensemble Learning 05:59
   12. Support Vector Machines (SVM) Overview 04:27
   13. [Activity] Using SVM to cluster people using scikit-learn 05:36
5. Recommender Systems 49:10
   1. User-Based Collaborative Filtering 07:57
   2. Item-Based Collaborative Filtering 08:15
   3. [Activity] Finding Movie Similarities 09:08
   4. [Activity] Improving the Results of Movie Similarities 07:59
   5. [Activity] Making Movie Recommendations to People 10:22
   6. [Exercise] Improve the recommender's results 05:29
6. More Data Mining and Machine Learning Techniques 52:51
   1. K-Nearest-Neighbors: Concepts 03:44
   2. [Activity] Using KNN to predict a rating for a movie 12:29
   3. Dimensionality Reduction; Principal Component Analysis 05:44
   4. [Activity] PCA Example with the Iris data set 09:05
   5. Data Warehousing Overview: ETL and ELT 09:05
   6. Reinforcement Learning 12:44
7. Dealing with Real-World Data45:38
   1. Bias/Variance Tradeoff 06:15
   2. [Activity] K-Fold Cross-Validation to avoid overfitting 10:55
   3. Data Cleaning and Normalization 07:10
   4. [Activity] Cleaning web log data 10:56
   5. Normalizing numerical data 03:22
   6. [Activity] Detecting outliers 07:00
8. Apache Spark: Machine Learning on Big Data 01:36:53
   1. Warning about Java 11 and Spark 2.4! 00:22
   2. [Activity] Installing Spark - Part 1 07:02
   3. [Activity] Installing Spark - Part 2 13:29
   4. Spark Introduction 09:10
   5. Spark and the Resilient Distributed Dataset (RDD) 11:42
   6. Introducing MLLib 05:09
   7. [Activity] Decision Trees in Spark 16:00
   8. [Activity] K-Means Clustering in Spark 11:07
   9. TF / IDF 06:44
   10. [Activity] Searching Wikipedia with Spark 08:11
   11. [Activity] Using the Spark 2.0 DataFrame API for MLLib 07:57
9. Experimental Design 33:16
   1. A/B Testing Concepts 08:23
   2. T-Tests and P-Values 05:59
   3. [Activity] Hands-on With T-Tests 06:04
   4. Determining How Long to Run an Experiment 03:24
   5. A/B Test Gotchas 09:26
10. Deep Learning and Neural Networks 02:39:12
    1. Deep Learning Pre-Requisites 10:51
    2. The History of Artificial Neural Networks 11:14
    3. [Activity] Deep Learning in the Tensorflow Playground 12:00
    4. Deep Learning Details 09:29
    5. Introducing Tensorflow 12:39
    6. [Activity] Using Tensorflow, Part 1 09:50
    7. [Activity] Using Tensorflow, Part 2 13:27
    8. [Activity] Introducing Keras 13:50
    9. [Activity] Using Keras to Predict Political Affiliations 12:23
    10. Convolutional Neural Networks (CNN's) 11:28
    11. [Activity] Using CNN's for handwriting recognition 08:12
    12. Recurrent Neural Networks (RNN's) 11:02
    13. [Activity] Using a RNN for sentiment analysis 10:01
    14. The Ethics of Deep Learning 11:02
    15. Learning More about Deep Learning 01:44
11. Final Project 16:52
    1. Your final project assignment 06:26
    2. Final project review 10:26
12. You made it! 06:05
    1. More to Explore 02:59
    2. Don't Forget to Leave a Rating! 00:23
    3. Bonus Lecture: Discounts on my Spark and MapReduce courses! 02:43

Требования

• You'll need a desktop computer (Windows, Mac, or Linux) capable of running Enthought Canopy 1.6.2 or newer. The course will walk you through installing the necessary free software.
• Some prior coding or scripting experience is required.
• At least high school level math skills will be required.
• This course walks through getting set up on a Microsoft Windows based desktop PC. While the code in this course will run on other operating systems, we cannot provide OS-specific support for them.

Описание

New! Updated for TensorFlow 1.10

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!

If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 80 lectures spanning 12 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including:

• Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras
• Sentiment analysis
• Image recognition and classification
• Regression analysis
• K-Means Clustering
• Principal Component Analysis
• Train/Test and cross validation
• Bayesian Methods
• Decision Trees and Random Forests
• Multivariate Regression
• Multi-Level Models
• Support Vector Machines
• Reinforcement Learning
• Collaborative Filtering
• K-Nearest Neighbor
• Bias/Variance Tradeoff
• Ensemble Learning
• Term Frequency / Inverse Document Frequency
• Experimental Design and A/B Tests

...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster. And you'll also get access to this course's Facebook Group, where you can stay in touch with your classmates.

If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's; the sample code will also run on MacOS or Linux desktop systems, but I can't provide OS-specific support for them.

If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

• "I started doing your course in 2015... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD

Для кого этот курс:

• Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.
• Technologists curious about how deep learning really works
• Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.
• If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.

Страница курса:

Курс зарезервирует переводчик, когда запишутся минимум 10-15 человек