Essential Machine Learning and AI with Python and Jupyter Notebook

1 Essential Machine Learning and AI with Python and Jupyter Notebook – Introduction
2 Learning objectives
3 1.1 Use IPython, Jupyter, and Python REPL
4 1.2 Write procedural statements
5 1.3 Use strings and string formatting
6 1.5 Interact with data structures
7 1.6 Write and run scripts
8 1.7 Summary
9 Learning objectives
10 2.1 Write functions
11 2.2 Utilize functional programming concepts
12 2.3 Utilize lazy evaluated functions
13 2.4 Utilize decorators
14 2.5 Make classes behave like functions
15 2.6 Apply a function to a Pandas DataFrame
16 2.7 Use Python lambdas
17 2.8 Summary
18 Learning objectives
19 3.1 Create loops
20 3.2 Use if_else_break_continue_pass statements
21 3.3 Understand try_except
22 3.4 Understand generator expressions
23 3.5 Understand list comprehensions
24 3.6 Understand sorting
25 3.7 Understand Python regular expressions
26 3.8 Summary
27 Learning objectives
28 4.1 Write and use libraries in Python
29 4.2 Use pipenv, pip, virtualenv and conda
30 4.3 Deploy Python code to production
31 4.4 Summary
32 Learning objectives
33 5.2 Make and interact with simple objects
34 5.3 Understand class inheritance
35 5.4 Interact with special class methods
36 5.5 Create metaclasses
37 5.6 Summary
38 Learning objectives
39 6.1 Use write file operations
40 6.2 Use read file operations
41 6.3 Use serialization techniques
42 6.5 Use Google Sheets with Pandas DataFrames
43 6.6 Use concurrency methods in Python
44 6.7 Summary
45 Learning objectives
46 7.2 Use git and Github to manage changes
47 7.3 Use CircleCI and AWS Code Build to build and test a project sourced from Github
48 7.4 Use static analysis and testing tools – pylint, pytest, and coverage
49 7.5 Test Jupyter Notebooks
50 7.6 Summary
51 Learning objectives
52 8.1 Make a project layout
53 8.2 Lay out a Makefile for a project
54 8.3 Create a command-line tool for Pandas aggregation
55 8.4 Make plugins to pass to Pandas
56 8.5 Write the Flask API
57 8.6 Integrate Swagger documentation
58 8.7 Benchmark Python projects
59 8.8 Integrate testing and linting
60 Learning objectives
61 9.1 Data Collection of Social Media Data
62 9.2 Import and merge DataFrames in Pandas
63 9.3 Understand correlation heatmaps and pairplots
64 9.4 Use linear regression in Python
65 9.5 Use ggplot in Python
66 9.6 Use k-means clustering
67 9.7 Use PCA with scikit-learn
68 9.8 Use ML classification prediction with scikit-learn
69 9.9 Use ML regression prediction with scikit-learn
70 9.10 Use Plotly for interactive data visualization
71 9.11 Summary
72 Learning objectives
73 10.1 Overview of AI, Machine Learning and Deep Learning
74 10.2 Big Data
75 10.3 Working with recommendation systems
76 10.4 Summary
77 Learning objectives
78 11.1 Use AWS Web Services
79 11.2 Use Boto
80 11.3 Use AWS Lambda development with Chalice
81 11.4 Use AWS DynamoDB
82 11.5 Use AWS Step functions
83 11.6 Use AWS Batch for ML jobs
84 11.7 Use AWS Sagemaker
85 11.8 Use AWS Comprehend for NLP
86 11.9 Use AWS Rekognition API
87 11.10 Summary
88 Learning objectives
89 12.1 Perform Colaboratory basics
90 12.2 Use Advanced Colab Features
91 12.3 Perform Datalab basics
92 12.4 Use TPUS for deep learning
93 12.5 Use Google Big Query
94 12.6 Use Google Machine Learning Services
95 12.8 Use Google Computer Vision API
96 12.9 Summary
97 13.1 Walk through Spot Price Machine Learning
98 13.2 Walk through DevML
99 13.3 Summary
100 Lesson 14 – Datascience – Case Study Social Power in the NBA
101 14.1 Datascience – Case Study Social Power in the NBA
102 Essential Machine Learning and AI with Python and Jupyter Notebook – Summary