English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 14 Hours | 9.38 GB

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

- 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

**+ Table of Contents**

**Getting Started**

1 Introduction

2 Udemy 101 Getting the Most From This Course

3 Installation Getting Started

4 [Activity] WINDOWS Installing and Using Anaconda & Course Materials

5 [Activity] MAC Installing and Using Anaconda & Course Materials

6 [Activity] LINUX Installing and Using Anaconda & Course Materials

7 Python Basics, Part 1 [Optional]

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

9 [Activity] Python Basics, Part 3 [Optional]

10 [Activity] Python Basics, Part 4 [Optional]

11 Introducing the Pandas Library [Optional]

**Statistics and Probability Refresher, and Python Practice**

12 Types of Data

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] Advanced Visualization with Seaborn

21 [Activity] Covariance and Correlation

22 [Exercise] Conditional Probability

23 Exercise Solution Conditional Probability of Purchase by Age

24 Bayes’ Theorem

**Predictive Models**

25 [Activity] Linear Regression

26 [Activity] Polynomial Regression

27 [Activity] Multiple Regression, and Predicting Car Prices

28 Multi-Level Models

**Machine Learning with Python**

29 Supervised vs. Unsupervised Learning, and TrainTest

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] WINDOWS Installing Graphviz

37 [Activity] MAC Installing Graphviz

38 [Activity] LINUX Installing Graphviz

39 Decision Trees Concepts

40 [Activity] Decision Trees Predicting Hiring Decisions

41 Ensemble Learning

42 [Activity] XGBoost

43 Support Vector Machines (SVM) Overview

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

**Recommender Systems**

45 User-Based Collaborative Filtering

46 Item-Based Collaborative Filtering

47 [Activity] Finding Movie Similarities

48 [Activity] Improving the Results of Movie Similarities

49 [Activity] Making Movie Recommendations to People

50 [Exercise] Improve the recommender’s results

**More Data Mining and Machine Learning Techniques**

51 K-Nearest-Neighbors Concepts

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

53 Dimensionality Reduction; Principal Component Analysis

54 [Activity] PCA Example with the Iris data set

55 Data Warehousing Overview ETL and ELT

56 Reinforcement Learning

57 [Activity] Reinforcement Learning & Q-Learning with Gym

58 Understanding a Confusion Matrix

59 Measuring Classifiers (Precision, Recall, F1, ROC, AUC)

**Dealing with Real-World Data**

60 BiasVariance Tradeoff

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

62 Data Cleaning and Normalization

63 [Activity] Cleaning web log data

64 Normalizing numerical data

65 [Activity] Detecting outliers

66 Feature Engineering and the Curse of Dimensionality

67 Imputation Techniques for Missing Data

68 Handling Unbalanced Data Oversampling, Undersampling, and SMOTE

69 Binning, Transforming, Encoding, Scaling, and Shuffling

**Apache Spark Machine Learning on Big Data**

70 Warning about Java 11 and Spark 3!

71 Spark installation notes for MacOS and Linux users

72 [Activity] Installing Spark – Part 1

73 [Activity] Installing Spark – Part 2

74 Spark Introduction

75 Spark and the Resilient Distributed Dataset (RDD)

76 Introducing MLLib

77 Introduction to Decision Trees in Spark

78 [Activity] K-Means Clustering in Spark

79 TF IDF

80 [Activity] Searching Wikipedia with Spark

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

**Experimental Design ML in the Real World**

82 Deploying Models to Real-Time Systems

83 AB Testing Concepts

84 T-Tests and P-Values

85 [Activity] Hands-on With T-Tests

86 Determining How Long to Run an Experiment

87 AB Test Gotchas

**Deep Learning and Neural Networks**

88 Deep Learning Pre-Requisites

89 The History of Artificial Neural Networks

90 [Activity] Deep Learning in the Tensorflow Playground

91 Deep Learning Details

92 Introducing Tensorflow

93 Important note about Tensorflow 2

94 [Activity] Using Tensorflow, Part 1

95 [Activity] Using Tensorflow, Part 2

96 [Activity] Introducing Keras

97 [Activity] Using Keras to Predict Political Affiliations

98 Convolutional Neural Networks (CNN’s)

99 [Activity] Using CNN’s for handwriting recognition

100 Recurrent Neural Networks (RNN’s)

101 [Activity] Using a RNN for sentiment analysis

102 [Activity] Transfer Learning

103 Tuning Neural Networks Learning Rate and Batch Size Hyperparameters

104 Deep Learning Regularization with Dropout and Early Stopping

105 The Ethics of Deep Learning

106 Learning More about Deep Learning

**Final Project**

107 Your final project assignment

108 Final project review

**You made it!**

109 More to Explore

110 Don’t Forget to Leave a Rating!

111 Bonus Lecture More courses to explore!

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