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Development Data Science Machine Learning

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
Rating: 4.6 out of 54.6 (24,776 ratings)
145,629 students
Created by Sundog Education by Frank Kane, Frank Kane
Last updated 4/2021
English
English, Italian [Auto], 
30-Day Money-Back Guarantee

What you'll learn

  • 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
Curated for the Udemy for Business collection

Course content

12 sections • 111 lectures • 14h 20m total length

  • Preview02:41
  • Preview02:10
  • Installation: Getting Started
    00:39
  • [Activity] WINDOWS: Installing and Using Anaconda & Course Materials
    12:37
  • [Activity] MAC: Installing and Using Anaconda & Course Materials
    10:02
  • [Activity] LINUX: Installing and Using Anaconda & Course Materials
    10:57
  • Python Basics, Part 1 [Optional]
    04:59
  • Preview05:17
  • [Activity] Python Basics, Part 3 [Optional]
    02:46
  • [Activity] Python Basics, Part 4 [Optional]
    04:02
  • Introducing the Pandas Library [Optional]
    10:08

  • Preview06:58
  • Mean, Median, Mode
    05:26
  • [Activity] Using mean, median, and mode in Python
    08:21
  • Preview11:12
  • Probability Density Function; Probability Mass Function
    03:27
  • Common Data Distributions (Normal, Binomial, Poisson, etc)
    07:45
  • [Activity] Percentiles and Moments
    12:33
  • [Activity] A Crash Course in matplotlib
    13:46
  • [Activity] Advanced Visualization with Seaborn
    17:30
  • [Activity] Covariance and Correlation
    11:31
  • [Exercise] Conditional Probability
    16:04
  • Exercise Solution: Conditional Probability of Purchase by Age
    02:20
  • Preview05:23

  • Preview11:01
  • Preview08:04
  • [Activity] Multiple Regression, and Predicting Car Prices
    11:26
  • Multi-Level Models
    04:36

  • Supervised vs. Unsupervised Learning, and Train/Test
    08:57
  • [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression
    05:47
  • Bayesian Methods: Concepts
    03:59
  • Preview08:05
  • K-Means Clustering
    07:23
  • [Activity] Clustering people based on income and age
    05:14
  • Measuring Entropy
    03:09
  • [Activity] WINDOWS: Installing Graphviz
    00:22
  • [Activity] MAC: Installing Graphviz
    01:16
  • [Activity] LINUX: Installing Graphviz
    00:54
  • Preview08:43
  • [Activity] Decision Trees: Predicting Hiring Decisions
    09:47
  • Ensemble Learning
    05:59
  • [Activity] XGBoost
    15:29
  • Support Vector Machines (SVM) Overview
    04:27
  • [Activity] Using SVM to cluster people using scikit-learn
    09:29

  • Preview07:57
  • Item-Based Collaborative Filtering
    08:15
  • [Activity] Finding Movie Similarities using Cosine Similarity
    09:08
  • [Activity] Improving the Results of Movie Similarities
    07:59
  • Preview10:22
  • [Exercise] Improve the recommender's results
    05:29

  • K-Nearest-Neighbors: Concepts
    03:44
  • [Activity] Using KNN to predict a rating for a movie
    12:29
  • Dimensionality Reduction; Principal Component Analysis (PCA)
    05:44
  • [Activity] PCA Example with the Iris data set
    09:05
  • Data Warehousing Overview: ETL and ELT
    09:05
  • Preview12:44
  • [Activity] Reinforcement Learning & Q-Learning with Gym
    12:56
  • Understanding a Confusion Matrix
    05:17
  • Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
    06:35

  • Bias/Variance Tradeoff
    06:15
  • [Activity] K-Fold Cross-Validation to avoid overfitting
    10:26
  • Preview07:10
  • [Activity] Cleaning web log data
    10:56
  • Normalizing numerical data
    03:22
  • [Activity] Detecting outliers
    06:21
  • Feature Engineering and the Curse of Dimensionality
    06:03
  • Imputation Techniques for Missing Data
    07:48
  • Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
    05:35
  • Binning, Transforming, Encoding, Scaling, and Shuffling
    07:51

  • Warning about Java 11 and Spark 3!
    00:21
  • Spark installation notes for MacOS and Linux users
    01:28
  • [Activity] Installing Spark - Part 1
    06:59
  • [Activity] Installing Spark - Part 2
    07:20
  • Spark Introduction
    09:10
  • Spark and the Resilient Distributed Dataset (RDD)
    11:42
  • Introducing MLLib
    05:09
  • Introduction to Decision Trees in Spark
    Preview16:15
  • [Activity] K-Means Clustering in Spark
    11:23
  • Preview06:44
  • [Activity] Searching Wikipedia with Spark
    08:21
  • [Activity] Using the Spark 2.0 DataFrame API for MLLib
    08:07

  • Deploying Models to Real-Time Systems
    08:42
  • A/B Testing Concepts
    08:23
  • T-Tests and P-Values
    05:59
  • [Activity] Hands-on With T-Tests
    06:04
  • Determining How Long to Run an Experiment
    03:24
  • Preview09:26

  • Deep Learning Pre-Requisites
    11:43
  • The History of Artificial Neural Networks
    Preview11:14
  • [Activity] Deep Learning in the Tensorflow Playground
    12:00
  • Deep Learning Details
    09:29
  • Introducing Tensorflow
    11:29
  • Important note about Tensorflow 2
    00:23
  • [Activity] Using Tensorflow, Part 1
    13:11
  • [Activity] Using Tensorflow, Part 2
    12:03
  • [Activity] Introducing Keras
    13:33
  • [Activity] Using Keras to Predict Political Affiliations
    12:05
  • Convolutional Neural Networks (CNN's)
    11:28
  • [Activity] Using CNN's for handwriting recognition
    08:02
  • Recurrent Neural Networks (RNN's)
    11:02
  • [Activity] Using a RNN for sentiment analysis
    09:37
  • [Activity] Transfer Learning
    12:14
  • Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
    04:39
  • Deep Learning Regularization with Dropout and Early Stopping
    06:21
  • The Ethics of Deep Learning
    Preview11:02
  • Learning More about Deep Learning
    01:44

Requirements

  • 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.

Description

New! Updated for 2020 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


Who this course is for:

  • 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.

Featured review

Mangesh Jagannath Thorat
Mangesh Jagannath Thorat
5 courses
4 reviews
Rating: 4.5 out of 511 months ago
Excellent course. Precise and well organized presentation. Complete course is filled with lot of learning not only theoretical but also practical examples. Mr.Frank is kind enough to share his practical experiences and actual problems faced by data scientist/ML engineer. The topic on"The ethics of deep learning" is really gold nugget that everyone must follow. Thank you Mr. Frank Kane and Udemy for this wonderful course.

Instructors

Sundog Education by Frank Kane
Founder, Sundog Education. Machine Learning Pro
Sundog Education by Frank Kane
  • 4.5 Instructor Rating
  • 101,306 Reviews
  • 455,190 Students
  • 22 Courses

Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. 

Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Due to our volume of students we are unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.

Frank Kane
Founder, Sundog Education
Frank Kane
  • 4.5 Instructor Rating
  • 97,633 Reviews
  • 410,359 Students
  • 14 Courses

Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.

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