Machine Learning, Data Science and Deep Learning with Python

Machine Learning, Data Science and Deep Learning with Python
Machine Learning, Data Science and Deep Learning with Python
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 12 Hours | 7.42 GB

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

New! Updated for TensorFlow 1.10

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s interesting work too!

If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 80 lectures spanning 12 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

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, AI, and data mining techniques real employers are looking for, including:

  • Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and Keras
  • Sentiment analysis
  • Image recognition and classification
  • 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

…and much more! There’s also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to “big data” analyzed on a computing cluster. And you’ll also get access to this course’s Facebook Group, where you can stay in touch with your classmates.

If you’re new to Python, don’t worry – the course starts with a crash course. If you’ve done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s; the sample code will also run on MacOS or Linux desktop systems, but I can’t provide OS-specific support for them.

If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for?

What you’ll learn

  • Build artificial neural networks with Tensorflow and Keras
  • Make predictions using linear regression, polynomial regression, and multivariate regression
  • Classify images, data, and sentiments using deep learning
  • Implement machine learning at massive scale with Apache Spark’s MLLib
  • Understand reinforcement learning – and how to build a Pac-Man bot
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
  • Use train/test and K-Fold cross validation to choose and tune your models
  • Build a movie recommender system using item-based and user-based collaborative filtering
  • Clean your input data to remove outliers
  • Design and evaluate A/B tests using T-Tests and P-Values
Table of Contents

Getting Started
1 Introduction
2 Udemy 101 Getting the Most From This Course
3 [Activity] Getting What You Need
4 [Activity] Installing Enthought Canopy
5 Python Basics, Part 1 [Optional]
6 [Activity] Python Basics, Part 2 [Optional]
7 Running Python Scripts [Optional]
8 Introducing the Pandas Library [Optional]

Statistics and Probability Refresher, and Python Practise
9 Types of Data
10 [Exercise] Conditional Probability
11 Exercise Solution Conditional Probability of Purchase by Age
12 Bayes’ Theorem
13 Mean, Median, Mode
14 [Activity] Using mean, median, and mode in Python
15 [Activity] Variation and Standard Deviation
16 Probability Density Function; Probability Mass Function
17 Common Data Distributions
18 [Activity] Percentiles and Moments
19 [Activity] A Crash Course in matplotlib
20 [Activity] Covariance and Correlation

Predictive Models
21 [Activity] Linear Regression
22 [Activity] Polynomial Regression
23 [Activity] Multivariate Regression, and Predicting Car Prices
24 Multi-Level Models

Machine Learning with Python
25 Supervised vs. Unsupervised Learning, and TrainTest
26 [Activity] Decision Trees Predicting Hiring Decisions
27 Ensemble Learning
28 Support Vector Machines (SVM) Overview
29 [Activity] Using SVM to cluster people using scikit-learn
30 [Activity] Using TrainTest to Prevent Overfitting a Polynomial Regression
31 Bayesian Methods Concepts
32 [Activity] Implementing a Spam Classifier with Naive Bayes
33 K-Means Clustering
34 [Activity] Clustering people based on income and age
35 Measuring Entropy
36 [Activity] Install GraphViz
37 Decision Trees Concepts

Recommender Systems
38 User-Based Collaborative Filtering
39 Item-Based Collaborative Filtering
40 [Activity] Finding Movie Similarities
41 [Activity] Improving the Results of Movie Similarities
42 [Activity] Making Movie Recommendations to People
43 [Exercise] Improve the recommender’s results

More Data Mining and Machine Learning Techniques
44 K-Nearest-Neighbors Concepts
45 [Activity] Using KNN to predict a rating for a movie
46 Dimensionality Reduction; Principal Component Analysis
47 [Activity] PCA Example with the Iris data set
48 Data Warehousing Overview ETL and ELT
49 Reinforcement Learning

Dealing with Real-World Data
50 BiasVariance Tradeoff
51 [Activity] K-Fold Cross-Validation to avoid overfitting
52 Data Cleaning and Normalization
53 [Activity] Cleaning web log data
54 Normalizing numerical data
55 [Activity] Detecting outliers

Apache Spark Machine Learning on Big Data
56 Warning about Java 10!
57 [Activity] Searching Wikipedia with Spark
58 [Activity] Using the Spark 2.0 DataFrame API for MLLib
59 [Activity] Installing Spark – Part 1
60 [Activity] Installing Spark – Part 2
61 Spark Introduction
62 Spark and the Resilient Distributed Dataset (RDD)
63 Introducing MLLib
64 [Activity] Decision Trees in Spark
65 [Activity] K-Means Clustering in Spark
66 TF IDF

Experimental Design
67 AB Testing Concepts
68 T-Tests and P-Values
69 [Activity] Hands-on With T-Tests
70 Determining How Long to Run an Experiment
71 AB Test Gotchas

Deep Learning and Neural Networks
72 Deep Learning Pre-Requisites
73 Convolutional Neural Networks (CNN’s)
74 [Activity] Using CNN’s for handwriting recognition
75 Recurrent Neural Networks (RNN’s)
76 [Activity] Using a RNN for sentiment analysis
77 The Ethics of Deep Learning
78 Learning More about Deep Learning
79 The History of Artificial Neural Networks
80 [Activity] Deep Learning in the Tensorflow Playground
81 Deep Learning Details
82 Introducing Tensorflow
83 [Activity] Using Tensorflow, Part 1
84 [Activity] Using Tensorflow, Part 2
85 [Activity] Introducing Keras
86 [Activity] Using Keras to Predict Political Affiliations

Final Project
87 Your final project assignment
88 Final project review

You made it!
89 More to Explore
90 Don’t Forget to Leave a Rating!
91 Bonus Lecture Discounts on my Spark and MapReduce courses!


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