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

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 12 Hours | 3.07 GB

Go hands-on with the neural network, artificial intelligence, and machine learning techniques employers are seeking!

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 and data mining techniques real employers are looking for, including:

- Deep Learning / Neural Networks (MLP's, CNN's, RNN's)
- 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

**Table of Contents**

**Getting Started**

1 Introduction

2 [Activity] Getting What You Need

3 [Activity] Installing Enthought Canopy

4 Python Basics_ Part 1

5 [Activity] Python Basics_ Part 2

6 Running Python Scripts

7 Introducing the Pandas Library

**Statistics and Probability Refresher_ and Python Practise**

8 Types of Data

9 Mean_ Median_ Mode

10 [Activity] Using mean_ median_ and mode in Python

11 [Activity] Variation and Standard Deviation

12 Probability Density Function; Probability Mass Function

13 Common Data Distributions

14 [Activity] Percentiles and Moments

15 [Activity] A Crash Course in matplotlib

16 [Activity] Covariance and Correlation

17 [Exercise] Conditional Probability

18 Exercise Solution_ Conditional Probability of Purchase by Age

19 Bayes' Theorem

**Predictive Models**

20 [Activity] Linear Regression

21 [Activity] Polynomial Regression

22 [Activity] Multivariate Regression_ and Predicting Car Prices

23 Multi-Level Models

**Machine Learning with Python**

24 Supervised vs_ Unsupervised Learning_ and Train_Test

25 [Activity] Using Train_Test to Prevent Overfitting a Polynomial Regression

26 Bayesian Methods_ Concepts

27 [Activity] Implementing a Spam Classifier with Naive Bayes

28 K-Means Clustering

29 [Activity] Clustering people based on income and age

30 Measuring Entropy

31 [Activity] Install GraphViz

32 Decision Trees_ Concepts

33 [Activity] Decision Trees_ Predicting Hiring Decisions

34 Ensemble Learning

35 Support Vector Machines (SVM) Overview

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

**Recommender Systems**

37 User-Based Collaborative Filtering

38 Item-Based Collaborative Filtering

39 [Activity] Finding Movie Similarities

40 [Activity] Improving the Results of Movie Similarities

41 [Activity] Making Movie Recommendations to People

42 [Exercise] Improve the recommender's results

**More Data Mining and Machine Learning Techniques**

43 K-Nearest-Neighbors_ Concepts

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

45 Dimensionality Reduction; Principal Component Analysis

46 [Activity] PCA Example with the Iris data set

47 Data Warehousing Overview_ ETL and ELT

48 Reinforcement Learning

**Dealing with Real-World Data**

49 Bias_Variance Tradeoff

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

51 Data Cleaning and Normalization

52 [Activity] Cleaning web log data

53 Normalizing numerical data

54 [Activity] Detecting outliers

**Apache Spark_ Machine Learning on Big Data**

55 Warning about Java 9!

56 [Activity] Installing Spark - Part 1

57 [Activity] Installing Spark - Part 2

58 Spark Introduction

59 Spark and the Resilient Distributed Dataset (RDD)

60 Introducing MLLib

61 [Activity] Decision Trees in Spark

62 [Activity] K-Means Clustering in Spark

63 TF _ IDF

64 [Activity] Searching Wikipedia with Spark

65 [Activity] Using the Spark 2_0 DataFrame API for MLLib

**Experimental Design**

66 A_B Testing Concepts

67 T-Tests and P-Values

68 [Activity] Hands-on With T-Tests

69 Determining How Long to Run an Experiment

70 A_B Test Gotchas

**Deep Learning and Neural Networks**

71 Deep Learning Pre-Requisites

72 The History of Artificial Neural Networks

73 [Activity] Deep Learning in the Tensorflow Playground

74 Deep Learning Details

75 Introducing Tensorflow

76 [Activity] Using Tensorflow_ Part 1

77 [Activity] Using Tensorflow_ Part 2

78 [Activity] Introducing Keras

79 [Activity] Using Keras to Predict Political Affiliations

80 Convolutional Neural Networks (CNN's)

81 [Activity] Using CNN's for handwriting recognition

82 Recurrent Neural Networks (RNN's)

83 [Activity] Using a RNN for sentiment analysis

84 The Ethics of Deep Learning

85 Learning More about Deep Learning

**Final Project**

86 Your final project assignment

87 Final project review

**You made it!**

88 More to Explore

89 Don't Forget to Leave a Rating!

90 Bonus Lecture_ Discounts on my Spark and MapReduce courses!

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