English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 21h 27m | 5.36 GB
In this program, you’ll master valuable machine learning skills that are in demand across countless industries. Investment levels in this space continue to rise, thousands of highly-valued startups have entered the field, and demand for machine learning talent shows no signs of leveling. Program graduates emerge uniquely prepared to excel in machine learning roles.
Part 01 : Machine Learning Foundations
Welcome to the Machine Learning Engineer Nanodegree program! In this first part, you’ll meet the instructors and the career services team. You’ll also learn about the program structure and get your first lesson on the basics of machine learning. Join the ML community!
Part 02 : Model Evaluation and Validation
Part 03 : Supervised Learning
Learn how Supervised Learning models such as Decision Trees, SVMs, etc. are trained to model and predict labeled data.
Part 04 : Unsupervised Learning
Learn how to find patterns and structures in unlabeled data, perform feature transformations and improve the predictive performance of your models.
Part 05 : Deep Learning
In this section, we will learn about TensorFlow, Neural Networks and Convolutional Neural Networks.
Part 06 : Reinforcement Learning
Use Reinforcement Learning algorithms like Q-Learning to train artificial agents to take optimal actions in an environment.
Part 07 : Machine Learning Capstone
Have an idea of a problem in the real world that can be solved using machine learning? Here you have the opportunity to do just that using a dataset of your choice.
Part 08 (Career): Career: Job Search Strategies
Opportunity can come when you least expect it, so when your dream job comes along, you want to be ready.
Part 09 (Career): Career: Networking
Networking is a very important component to a successful job search. In the following lesson, you will learn how tell your unique story to recruiters in a succinct and professional but relatable way.
Part 10 (Career): Career: Machine Learning Interview Practice
Now that you’ve practiced your skills through your project work, learn how you can present your knowledge in an interview.
Part 11 (Elective): Deep Learning – Tensorflow
Table of Contents
1 Welcome to Machine Learning
2 What is Machine Learning
3 Introductory Practice Project
4 Career Services Available to You
5 Training and Testing Models
6 Evaluation Metrics
7 Model Selection
8 NumPy and pandas Assessment
9 Model Evaluation and Validation Assessment
10 Predicting Boston Housing Prices
11 Linear Regression
12 Perceptron Algorithm
13 Decision Trees
14 Naive Bayes
15 Support Vector Machines
16 Ensemble Methods
17 Supervised Learning Assessment
18 Supervised Learning Project
19 Clustering
20 Clustering Mini-Project
21 Hierarchical and Density-based Clustering
22 Gaussian Mixture Models and Cluster Validation
23 Feature Scaling
24 PCA
25 PCA Mini-Project
26 Random Projection and ICA
27 Unsupervised Learning Assessment
28 Creating Customer Segments
29 Neural Networks
30 Cloud Computing
31 Deep Neural Networks
32 Convolutional Neural Networks
33 Deep Learning for Cancer Detection with Sebastian Thrun
34 Deep Learning Assessment
35 Deep Learning Project
36 Introduction to RL
37 The RL Framework The Problem
38 The RL Framework The Solution
39 Dynamic Programming
40 Monte Carlo Methods
41 Temporal-Difference Methods
42 Solve OpenAI Gym’s Taxi-v2 Task
43 RL in Continuous Spaces
44 Deep Q-Learning
45 Policy-Based Methods
46 Actor-Critic Methods
47 Teach a Quadcopter How to Fly
48 Reinforcement Learning Assessment
49 Writing up a Capstone Proposal
50 Machine Learning Capstone Project
51 Conduct a Job Search
52 Refine Your Entry-Level Resume
53 Refine Your Career Change Resume
54 Refine Your Prior Industry Experience Resume
55 Craft Your Cover Letter
56 Develop Your Personal Brand
57 LinkedIn Review
58 Udacity Professional Profile
59 GitHub Review
60 Ace Your Interview
61 Practice Behavioral Questions
62 Interview Fails
63 Land a Job Offer
64 Interview Practice
65 Introduction and Efficiency
66 List-Based Collections
67 Searching and Sorting
68 Maps and Hashing
69 Trees
70 Graphs
71 Case Studies in Algorithms
72 Technical Interview – Python
73 Software and Tools
74 Deep Learning
75 Intro to TensorFlow
76 Intro to Neural Networks
77 Deep Neural Networks
78 Convolutional Neural Networks
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