Complete 2019 Data Science & Machine Learning Bootcamp

Complete 2019 Data Science & Machine Learning Bootcamp
Complete 2019 Data Science & Machine Learning Bootcamp
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 35.5 Hours | 15.0 GB

Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more!

Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science.

At over 35+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here’s why:

  • The course is a taught by the lead instructor at the App Brewery, London’s leading in-person programming bootcamp.
  • In the course, you’ll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.
  • This course doesn’t cut any corners, there are beautiful animated explanation videos and real-world projects to build.
  • The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.
  • To date, we’ve taught over 200,000 students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.
  • You’ll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.

We’ll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.

The course includes over 35 hours of HD video tutorials and builds your programming knowledge while solving real-world problems.

In the curriculum, we cover a large number of important data science and machine learning topics, such as:

  • Data Cleaning and Pre-Processing
  • Data Exploration and Visualisation
  • Linear Regression
  • Multivariable Regression
  • Optimisation Algorithms and Gradient Descent
  • Naive Bayes Classification
  • Descriptive Statistics and Probability Theory
  • Neural Networks and Deep Learning
  • Model Evaluation and Analysis

Throughout the course, we cover all the tools used by data scientists and machine learning experts, including:

  • Python 3
  • Tensorflow
  • Pandas
  • Numpy
  • Scikit Learn
  • Keras
  • Matplotlib
  • Seaborn
  • SciPy
  • SymPy

By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We’ll be covering all of these Python programming concepts:

  • Data Types and Variables
  • String Manipulation
  • Functions
  • Objects
  • Lists, Tuples and Dictionaries
  • Loops and Iterators
  • Conditionals and Control Flow
  • Generator Functions
  • Context Managers and Name Scoping
  • Error Handling

What you’ll learn

  • You will learn how to program using Python through practical projects
  • Use data science algorithms to analyse data in real life projects such as spam classification and image recognition
  • Build a portfolio of data science projects to apply for jobs in the industry
  • Understand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many more
  • Create your own neural networks and understand how to use them to perform deep learning
  • Understand and apply data visualisation techniques to explore large datasets
Table of Contents

Introduction to the Course
1 What is Machine Learning?
2 What is Data Science?
3 Download the Syllabus
4 Top Tips for Succeeding on this Course
5 Course Resources List

Predict Movie Box Office Revenue with Linear Regression
6 Introduction to Linear Regression & Specifying the Problem
7 Gather & Clean the Data
8 Explore & Visualise the Data with Python
9 The Intuition behind the Linear Regression Model
10 Analyse and Evaluate the Results
11 Download the Complete Notebook Here
12 Join the Student Community

Python Programming for Data Science and Machine Learning
13 Windows Users – Install Anaconda
14 Mac Users – Install Anaconda
15 Does LSD Make You Better at Maths?
16 Download the 12 Rules to Learn to Code
17 [Python] – Variables and Types
18 [Python] – Lists and Arrays
19 [Python & Pandas] – Dataframes and Series
20 [Python] – Module Imports
21 [Python] – Functions – Part 1: Defining and Calling Functions
22 [Python] – Functions – Part 2: Arguments & Parameters
23 [Python] – Functions – Part 3: Results & Return Values
24 [Python] – Objects – Understanding Attributes and Methods
25 How to Make Sense of Python Documentation for Data Visualisation
26 Working with Python Objects to Analyse Data
27 [Python] – Tips, Code Style and Naming Conventions
28 Download the Complete Notebook Here

Introduction to Optimisation and the Gradient Descent Algorithm
29 What’s Coming Up?
30 How a Machine Learns
31 Introduction to Cost Functions
32 LaTeX Markdown and Generating Data with Numpy
33 Understanding the Power Rule & Creating Charts with Subplots
34 [Python] – Loops and the Gradient Descent Algorithm
35 [Python] – Advanced Functions and the Pitfalls of Optimisation (Part 1)
36 [Python] – Tuples and the Pitfalls of Optimisation (Part 2)
37 Understanding the Learning Rate
38 How to Create 3-Dimensional Charts
39 Understanding Partial Derivatives and How to use SymPy
40 Implementing Batch Gradient Descent with SymPy
41 [Python] – Loops and Performance Considerations
42 Reshaping and Slicing N-Dimensional Arrays
43 Concatenating Numpy Arrays
44 Introduction to the Mean Squared Error (MSE)
45 Transposing and Reshaping Arrays
46 Implementing a MSE Cost Function
47 Understanding Nested Loops and Plotting the MSE Function (Part 1)
48 Plotting the Mean Squared Error (MSE) on a Surface (Part 2)
49 Running Gradient Descent with a MSE Cost Function
50 Visualising the Optimisation on a 3D Surface
51 Download the Complete Notebook Here

Predict House Prices with Multivariable Linear Regression
52 Defining the Problem
53 Gathering the Boston House Price Data
54 Clean and Explore the Data (Part 1): Understand the Nature of the Dataset
55 Clean and Explore the Data (Part 2): Find Missing Values
56 Visualising Data (Part 1): Historams, Distributions & Outliers
57 Visualising Data (Part 2): Seaborn and Probability Density Functions
58 Working with Index Data, Pandas Series, and Dummy Variables
59 Understanding Descriptive Statistics: the Mean vs the Median
60 Introduction to Correlation: Understanding Strength & Direction
61 Calculating Correlations and the Problem posed by Multicollinearity
62 Visualising Correlations with a Heatmap
63 Techniques to Style Scatter Plots
64 A Note for the Next Lesson
65 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques
66 Understanding Multivariable Regression
67 How to Shuffle and Split Training & Testing Data
68 Running a Multivariable Regression
69 How to Calculate the Model Fit with R-Squared
70 Introduction to Model Evaluation
71 Improving the Model by Transforming the Data
72 How to Interpret Coefficients using p-Values and Statistical Significance
73 Understanding VIF & Testing for Multicollinearity
74 Model Simiplication & Baysian Information Criterion
75 How to Analyse and Plot Regression Residuals
76 Residual Analysis (Part 1): Predicted vs Actual Values
77 Residual Analysis (Part 2): Graphing and Comparing Regression Residuals
78 Making Predictions (Part 1): MSE & R-Squared
79 Making Predictions (Part 2): Standard Deviation, RMSE, and Prediction Intervals
80 Build a Valuation Tool (Part 1): Working with Pandas Series & Numpy ndarrays
81 [Python] – Conditional Statements – Build a Valuation Tool (Part 2)
82 Build a Valuation Tool (Part 3): Docstrings & Creating your own Python Module
83 Download the Complete Notebook Here

Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails: Part 1
84 How to Translate a Business Problem into a Machine Learning Problem
85 Gathering Email Data and Working with Archives & Text Editors
86 How to Add the Lesson Resources to the Project
87 The Naive Bayes Algorithm and the Decision Boundary for a Classifier
88 Basic Probability
89 Joint & Conditional Probability
90 Bayes Theorem
91 Reading Files (Part 1): Absolute Paths and Relative Paths
92 Reading Files (Part 2): Stream Objects and Email Structure
93 Extracting the Text in the Email Body
94 [Python] – Generator Functions & the yield Keyword
95 Create a Pandas DataFrame of Email Bodies
96 Cleaning Data (Part 1): Check for Empty Emails & Null Entries
97 Cleaning Data (Part 2): Working with a DataFrame Index
98 Saving a JSON File with Pandas
99 Data Visualisation (Part 1): Pie Charts
100 Data Visualisation (Part 2): Donut Charts
101 Introduction to Natural Language Processing (NLP)
102 Tokenizing, Removing Stop Words and the Python Set Data Structure
103 Word Stemming & Removing Punctuation
104 Removing HTML tags with BeautifulSoup
105 Creating a Function for Text Processing
106 A Note for the Next Lesson
107 Advanced Subsetting on DataFrames: the apply() Function
108 [Python] – Logical Operators to Create Subsets and Indices
109 Word Clouds & How to install Additional Python Packages
110 Creating your First Word Cloud
111 Styling the Word Cloud with a Mask
112 Solving the Hamlet Challenge
113 Styling Word Clouds with Custom Fonts
114 Create the Vocabulary for the Spam Classifier
115 Coding Challenge: Check for Membership in a Collection
116 Coding Challenge: Find the Longest Email
117 Sparse Matrix (Part 1): Split the Training and Testing Data
118 Sparse Matrix (Part 2): Data Munging with Nested Loops
119 Sparse Matrix (Part 3): Using groupby() and Saving .txt Files
120 Coding Challenge Solution: Preparing the Test Data
121 Checkpoint: Understanding the Data
122 Download the Complete Notebook Here

Train a Naive Bayes Classifier to Create a Spam Filter: Part 2
123 Setting up the Notebook and Understanding Delimiters in a Dataset
124 Create a Full Matrix
125 Count the Tokens to Train the Naive Bayes Model
126 Sum the Tokens across the Spam and Ham Subsets
127 Calculate the Token Probabilities and Save the Trained Model
128 Coding Challenge: Prepare the Test Data
129 Download the Complete Notebook Here

Test and Evaluate a Naive Bayes Classifier: Part 3
130 Set up the Testing Notebook
131 Joint Conditional Probability (Part 1): Dot Product
132 Joint Conditional Probablity (Part 2): Priors
133 Making Predictions: Comparing Joint Probabilities
134 The Accuracy Metric
135 Visualising the Decision Boundary
136 False Positive vs False Negatives
137 The Recall Metric
138 The Precision Metric
139 The F-score or F1 Metric
140 A Naive Bayes Implementation using SciKit Learn
141 Download the Complete Notebook Here

Introduction to Neural Networks and How to Use Pre-Trained Models
142 The Human Brain and the Inspiration for Artificial Neural Networks
143 Layers, Feature Generation and Learning
144 Costs and Disadvantages of Neural Networks
145 Preprocessing Image Data and How RGB Works
146 Importing Keras Models and the Tensorflow Graph
147 Making Predictions using InceptionResNet
148 Coding Challenge Solution: Using other Keras Models
149 Download the Complete Notebook Here

Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow
150 Solving a Business Problem with Image Classification
151 Installing Tensorflow and Keras for Jupyter
152 Gathering the CIFAR 10 Dataset
153 Exploring the CIFAR Data
154 Pre-processing: Scaling Inputs and Creating a Validation Dataset
155 Compiling a Keras Model and Understanding the Cross Entropy Loss Function
156 Interacting with the Operating System and the Python Try-Catch Block
157 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems
158 Use Regularisation to Prevent Overfitting: Early Stopping & Dropout Techniques
159 Use the Model to Make Predictions
160 Model Evaluation and the Confusion Matrix
161 Model Evaluation and the Confusion Matrix
162 Download the Complete Notebook Here

Use Tensorflow to Classify Handwritten Digits
163 What’s coming up?
164 Getting the Data and Loading it into Numpy Arrays
165 Data Exploration and Understanding the Structure of the Input Data
166 Data Preprocessing: One-Hot Encoding and Creating the Validation Dataset
167 What is a Tensor?
168 Creating Tensors and Setting up the Neural Network Architecture
169 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics
170 TensorFlow Sessions and Batching Data
171 Tensorboard Summaries and the Filewriter
172 Understanding the Tensorflow Graph: Nodes and Edges
173 Name Scoping and Image Visualisation in Tensorboard
174 Different Model Architectures: Experimenting with Dropout
175 Prediction and Model Evaluation
176 Download the Complete Notebook Here

Next Steps
177 Where next?
178 What Modules Do You Want to See?
179 Stay in Touch!