**Machine Learning, Data Science and Deep Learning with Python**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 12 Hours | 7.42 GB

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

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?

What you’ll learn

- 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

**Table of Contents**

**Getting Started**

1 Introduction

2 Udemy 101 Getting the Most From This Course

3 [Activity] Getting What You Need

4 [Activity] Installing Enthought Canopy

5 Python Basics, Part 1 [Optional]

6 [Activity] Python Basics, Part 2 [Optional]

7 Running Python Scripts [Optional]

8 Introducing the Pandas Library [Optional]

**Statistics and Probability Refresher, and Python Practise**

9 Types of Data

10 [Exercise] Conditional Probability

11 Exercise Solution Conditional Probability of Purchase by Age

12 Bayes’ Theorem

13 Mean, Median, Mode

14 [Activity] Using mean, median, and mode in Python

15 [Activity] Variation and Standard Deviation

16 Probability Density Function; Probability Mass Function

17 Common Data Distributions

18 [Activity] Percentiles and Moments

19 [Activity] A Crash Course in matplotlib

20 [Activity] Covariance and Correlation

**Predictive Models**

21 [Activity] Linear Regression

22 [Activity] Polynomial Regression

23 [Activity] Multivariate Regression, and Predicting Car Prices

24 Multi-Level Models

**Machine Learning with Python**

25 Supervised vs. Unsupervised Learning, and TrainTest

26 [Activity] Decision Trees Predicting Hiring Decisions

27 Ensemble Learning

28 Support Vector Machines (SVM) Overview

29 [Activity] Using SVM to cluster people using scikit-learn

30 [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression

31 Bayesian Methods Concepts

32 [Activity] Implementing a Spam Classifier with Naive Bayes

33 K-Means Clustering

34 [Activity] Clustering people based on income and age

35 Measuring Entropy

36 [Activity] Install GraphViz

37 Decision Trees Concepts

**Recommender Systems**

38 User-Based Collaborative Filtering

39 Item-Based Collaborative Filtering

40 [Activity] Finding Movie Similarities

41 [Activity] Improving the Results of Movie Similarities

42 [Activity] Making Movie Recommendations to People

43 [Exercise] Improve the recommender’s results

**More Data Mining and Machine Learning Techniques**

44 K-Nearest-Neighbors Concepts

45 [Activity] Using KNN to predict a rating for a movie

46 Dimensionality Reduction; Principal Component Analysis

47 [Activity] PCA Example with the Iris data set

48 Data Warehousing Overview ETL and ELT

49 Reinforcement Learning

**Dealing with Real-World Data**

50 BiasVariance Tradeoff

51 [Activity] K-Fold Cross-Validation to avoid overfitting

52 Data Cleaning and Normalization

53 [Activity] Cleaning web log data

54 Normalizing numerical data

55 [Activity] Detecting outliers

**Apache Spark Machine Learning on Big Data**

56 Warning about Java 10!

57 [Activity] Searching Wikipedia with Spark

58 [Activity] Using the Spark 2.0 DataFrame API for MLLib

59 [Activity] Installing Spark – Part 1

60 [Activity] Installing Spark – Part 2

61 Spark Introduction

62 Spark and the Resilient Distributed Dataset (RDD)

63 Introducing MLLib

64 [Activity] Decision Trees in Spark

65 [Activity] K-Means Clustering in Spark

66 TF IDF

**Experimental Design**

67 AB Testing Concepts

68 T-Tests and P-Values

69 [Activity] Hands-on With T-Tests

70 Determining How Long to Run an Experiment

71 AB Test Gotchas

**Deep Learning and Neural Networks**

72 Deep Learning Pre-Requisites

73 Convolutional Neural Networks (CNN’s)

74 [Activity] Using CNN’s for handwriting recognition

75 Recurrent Neural Networks (RNN’s)

76 [Activity] Using a RNN for sentiment analysis

77 The Ethics of Deep Learning

78 Learning More about Deep Learning

79 The History of Artificial Neural Networks

80 [Activity] Deep Learning in the Tensorflow Playground

81 Deep Learning Details

82 Introducing Tensorflow

83 [Activity] Using Tensorflow, Part 1

84 [Activity] Using Tensorflow, Part 2

85 [Activity] Introducing Keras

86 [Activity] Using Keras to Predict Political Affiliations

**Final Project**

87 Your final project assignment

88 Final project review

**You made it!**

89 More to Explore

90 Don’t Forget to Leave a Rating!

91 Bonus Lecture Discounts on my Spark and MapReduce courses!

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