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!
Resolve the captcha to access the links!