Machine Learning for Mere Mortal

Machine Learning for Mere Mortal

English | MP4 | AVC 1364×768 | AAC 44KHz 2ch | 3h 34m | 2.68 GB


What can you do with machine learning?

Use social media data to put the right ads in front of your users
Predict which customers are going to leave in time to stop them
Real-time product pricing that reacts to competition and demand
Create smart SPAM filters
Read text and numbers from images

If you’re a halfway decent Python programmer, you can learn to do all these things and much more! Machine Learning for Mere Mortals is a practical video course that takes you from clueless beginner into the incredible world of machine learning with Python and Tensorflow!

With machine learning (ML), you can predict outcomes, identify trends, and make on-point recommendations that take the guesswork out of marketing, pricing, and other key business activities. And a quick look through the job boards will tell you that machine learning has become one of the hottest job skills out there. You can’t afford to miss out!

What do we mean by mere mortals? It’s simple! We don’t expect you to know any specialist mathematics or highbrow computer programming. If you passed college stats and you know the basics of Python programming, you’re set. In this course, you’ll start by learning what machine learning is, along with a quick refresher on the math you’ll need, including key ML terms like scalars, vectors, and matrices. Next, you’ll start working with Google’s amazing TensorFlow machine learning library as you take your first steps. In your first major project, you’ll build a smart spam filter. As you explore practical lessons in supervised and unsupervised machine learning, you’ll learn how to fine-tune it to catch exactly what it needs to, every time.

One of the hottest ML topics is Deep Learning with neural networks. That’s where this course goes next, but DON’T PANIC! You’ll find examples and explanations that make this extraordinarily cool topic easy to understand. You’ll build your first network, discover what makes it tick, and apply it to recognize handwriting. Along the way, you’ll start to think like a machine learning developer as you learn how to choose and optimize algorithms and explore other tools you can use beyond TensorFlow.

Expert author Nick Chase brings his experience writing hundreds of articles and tutorials to the world of video, as he carefully guides you through each aspect of machine learning you need to know. He breaks down key concepts and terms so you can discuss this topic with other people in the ML biz using their own language. With this video course and Nick by your side, you’ll be more than ready to develop your own machine learning applications and get real, actionable insight from your data!

+ Table of Contents

1 The basics
2 Machine Learning versus Artificial Intelligence
3 Supervised learning
4 Unsupervised learning
5 Reinforcement learning
6 A quick math refresher
7 Slope of a line
8 Scalars, vectors, and tensors
9 Matrices and matrix arithmetic
10 Set up your computing environment
11 Install Python tools
12 Create virtualenv environment
13 Install Tensorflow
14 The projects
15 Supervised learning
16 Trend lines
17 Cost functions
18 Minimizing cost functions
19 Visualizing data
20 Using linear regression to predict values
21 More complicated functions
22 Working with matrices
23 Letting Tensorflow do the hard work
24 More supervised learning
25 What are features_
26 What makes a good feature_
27 Decision trees
28 K-nearest neighbor
29 Linear classification
30 Making it work in Tensorflow
31 Creating a spam filter
32 Tools and data for email classification
33 Classifying emails
34 How clustering works
35 Clustering algorithms
36 Introducing k-means
37 Assigning Points to a Centroid in K-means)
38 What are neural networks, and how do they work
39 The Tensorflow Playground interface
40 Adding nodes to use multiple models in the TensorFlow Playground
41 What hidden layers are, and how to use them with TensorFlow Playground
42 What is the activation function in a neural network_
43 Using Neural Networks
44 How encoding non-numeric data works
45 One hot encoding
46 How image recognition relates to a neural network
47 Encoding and Representation
48 Numeric representation of data
49 Text representation of data
50 Representation of image data
51 Representation of audio data
52 Analytics, stock prices, and other time series data
53 Preparing data_ finding the data set
54 Preparing data_ Features engineering
55 Principal Component Analysis_ The mathematical way to determine features
56 Feature selection
57 Geometry of the data space and the curse of dimensionality
58 The difference between an algorithm and a model
59 Chaining together models
60 Improving performance in machine learning routines
61 Using parallelization
62 Outliers
63 What should we do with outliers_
64 Robustness and noise
65 Overfitting
66 Regularization