English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 14 Hours | 9.38 GB
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
New! Updated for Winter 2019 with extra content on feature engineering, regularization techniques, and tuning neural networks – as well as Tensorflow 2.0!
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 100 lectures spanning 14 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
- Data Visualization in Python with MatPlotLib and Seaborn
- Transfer Learning
- 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
- Multiple 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
- Feature Engineering
- Hyperparameter Tuning
…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, Linux desktops, and Macs.
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
- Classify images, data, and sentiments using deep learning
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Data Visualization with MatPlotLib and Seaborn
- 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 Installation Getting Started
4 [Activity] WINDOWS Installing and Using Anaconda & Course Materials
5 [Activity] MAC Installing and Using Anaconda & Course Materials
6 [Activity] LINUX Installing and Using Anaconda & Course Materials
7 Python Basics, Part 1 [Optional]
8 [Activity] Python Basics, Part 2 [Optional]
9 [Activity] Python Basics, Part 3 [Optional]
10 [Activity] Python Basics, Part 4 [Optional]
11 Introducing the Pandas Library [Optional]
Statistics and Probability Refresher, and Python Practice
12 Types of Data
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] Advanced Visualization with Seaborn
21 [Activity] Covariance and Correlation
22 [Exercise] Conditional Probability
23 Exercise Solution Conditional Probability of Purchase by Age
24 Bayes’ Theorem
Predictive Models
25 [Activity] Linear Regression
26 [Activity] Polynomial Regression
27 [Activity] Multiple Regression, and Predicting Car Prices
28 Multi-Level Models
Machine Learning with Python
29 Supervised vs. Unsupervised Learning, and TrainTest
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] WINDOWS Installing Graphviz
37 [Activity] MAC Installing Graphviz
38 [Activity] LINUX Installing Graphviz
39 Decision Trees Concepts
40 [Activity] Decision Trees Predicting Hiring Decisions
41 Ensemble Learning
42 [Activity] XGBoost
43 Support Vector Machines (SVM) Overview
44 [Activity] Using SVM to cluster people using scikit-learn
Recommender Systems
45 User-Based Collaborative Filtering
46 Item-Based Collaborative Filtering
47 [Activity] Finding Movie Similarities
48 [Activity] Improving the Results of Movie Similarities
49 [Activity] Making Movie Recommendations to People
50 [Exercise] Improve the recommender’s results
More Data Mining and Machine Learning Techniques
51 K-Nearest-Neighbors Concepts
52 [Activity] Using KNN to predict a rating for a movie
53 Dimensionality Reduction; Principal Component Analysis
54 [Activity] PCA Example with the Iris data set
55 Data Warehousing Overview ETL and ELT
56 Reinforcement Learning
57 [Activity] Reinforcement Learning & Q-Learning with Gym
58 Understanding a Confusion Matrix
59 Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
Dealing with Real-World Data
60 BiasVariance Tradeoff
61 [Activity] K-Fold Cross-Validation to avoid overfitting
62 Data Cleaning and Normalization
63 [Activity] Cleaning web log data
64 Normalizing numerical data
65 [Activity] Detecting outliers
66 Feature Engineering and the Curse of Dimensionality
67 Imputation Techniques for Missing Data
68 Handling Unbalanced Data Oversampling, Undersampling, and SMOTE
69 Binning, Transforming, Encoding, Scaling, and Shuffling
Apache Spark Machine Learning on Big Data
70 Warning about Java 11 and Spark 3!
71 Spark installation notes for MacOS and Linux users
72 [Activity] Installing Spark – Part 1
73 [Activity] Installing Spark – Part 2
74 Spark Introduction
75 Spark and the Resilient Distributed Dataset (RDD)
76 Introducing MLLib
77 Introduction to Decision Trees in Spark
78 [Activity] K-Means Clustering in Spark
79 TF IDF
80 [Activity] Searching Wikipedia with Spark
81 [Activity] Using the Spark 2.0 DataFrame API for MLLib
Experimental Design ML in the Real World
82 Deploying Models to Real-Time Systems
83 AB Testing Concepts
84 T-Tests and P-Values
85 [Activity] Hands-on With T-Tests
86 Determining How Long to Run an Experiment
87 AB Test Gotchas
Deep Learning and Neural Networks
88 Deep Learning Pre-Requisites
89 The History of Artificial Neural Networks
90 [Activity] Deep Learning in the Tensorflow Playground
91 Deep Learning Details
92 Introducing Tensorflow
93 Important note about Tensorflow 2
94 [Activity] Using Tensorflow, Part 1
95 [Activity] Using Tensorflow, Part 2
96 [Activity] Introducing Keras
97 [Activity] Using Keras to Predict Political Affiliations
98 Convolutional Neural Networks (CNN’s)
99 [Activity] Using CNN’s for handwriting recognition
100 Recurrent Neural Networks (RNN’s)
101 [Activity] Using a RNN for sentiment analysis
102 [Activity] Transfer Learning
103 Tuning Neural Networks Learning Rate and Batch Size Hyperparameters
104 Deep Learning Regularization with Dropout and Early Stopping
105 The Ethics of Deep Learning
106 Learning More about Deep Learning
Final Project
107 Your final project assignment
108 Final project review
You made it!
109 More to Explore
110 Don’t Forget to Leave a Rating!
111 Bonus Lecture More courses to explore!
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