Machine Learning, Data Science and Deep Learning with Python

4.5 (22407)
Обучение платное
14.5 часов курса
Курс от Udemy
Чему вы научились?
Build artificial neural networks with Tensorflow and Keras
Classify images, data, and sentiments using deep learning
Make predictions using linear regression, polynomial regression, and multivariate regression
Data Visualization with MatPlotLib and Seaborn
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
О курсе

New! Updated for Winter 2019 with extra content on feature engineering, regularization techniques, and tuning neural networks - as well as Tensorflow 2.0!

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 100 lectures spanning 14 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
  • Data Visualization in Python with MatPlotLib and Seaborn
  • Transfer Learning
  • 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
  • Multiple 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
  • Feature Engineering
  • Hyperparameter Tuning

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, Linux desktops, and Macs.

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

Getting Started
Get a working scientific Python environment set up, and understand how to use this course.
What to expect in this course, who it's for, and the general format we'll follow.
Udemy 101: Getting the Most From This Course
Installation: Getting Started
[Activity] WINDOWS: Installing and Using Anaconda & Course Materials
[Activity] MAC: Installing and Using Anaconda& Course Materials
[Activity] LINUX: Installing and Using Anaconda& Course Materials
Python Basics, Part 1 [Optional]
In a crash course on Python and what's different about it, we'll cover the importance of whitespace in Python scripts, and how to import Python modules.
[Activity] Python Basics, Part 2 [Optional]
In part 2 of our Python crash course, we'll cover Python data structures including lists, tuples, and dictionaries.
[Activity] Python Basics, Part 3 [Optional]
In this lesson, we'll see how functions work in Python.
[Activity] Python Basics, Part 4 [Optional]
We'll wrap up our Python crash course covering Boolean expressions and looping constructs.
Introducing the Pandas Library [Optional]
Pandas is a library we'll use throughout the course for loading, examining, and manipulating data. Let's see how it works with some examples, and you'll have an exercise at the end too.
  • You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 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.
Sundog Education by Frank Kane
Sundog Education by Frank Kane
Founder, Sundog Education. Machine Learning Pro
Frank Kane
Frank Kane
Founder, Sundog Education
Курсы Udemy подойдут для профессионального развития. Платформа устроена таким образом, что эксперты сами запускают курсы. Все материалы передаются в пожизненный доступ. На этой платформе можно найти курс, без преувеличений, на любую тему – начиная от тьюториала по какой-то камере и заканчивая теоретическим курсом по управлению финансовыми рисками. Язык и формат обучения устанавливается преподавателем, поэтому стоит внимательно изучить информацию о курсе перед покупкой.
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