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

Resolve the captcha to access the links!