Complete Machine Learning & Data Science Bootcamp 2022

Complete Machine Learning & Data Science Bootcamp 2022

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 377 lectures (43h 42m) | 16.3 GB

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies. You will go from zero to mastery!

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.

The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don’t worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:

  • Data Exploration and Visualizations
  • Neural Networks and Deep Learning
  • Model Evaluation and Analysis
  • Python 3
  • Tensorflow 2.0
  • Numpy
  • Scikit-Learn
  • Data Science and Machine Learning Projects and Workflows
  • Data Visualization in Python with MatPlotLib and Seaborn
  • Transfer Learning
  • Image recognition and classification
  • Train/Test and cross validation
  • Supervised Learning: Classification, Regression and Time Series
  • Decision Trees and Random Forests
  • Ensemble Learning
  • Hyperparameter Tuning
  • Using Pandas Data Frames to solve complex tasks
  • Use Pandas to handle CSV Files
  • Deep Learning / Neural Networks with TensorFlow 2.0 and Keras
  • Using Kaggle and entering Machine Learning competitions
  • How to present your findings and impress your boss
  • How to clean and prepare your data for analysis
  • K Nearest Neighbours
  • Support Vector Machines
  • Regression analysis (Linear Regression/Polynomial Regression)
  • How Hadoop, Apache Spark, Kafka, and Apache Flink are used
  • Setting up your environment with Conda, MiniConda, and Jupyter Notebooks
  • Using GPUs with Google Colab

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.

Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don’t really explain things well enough for you to go off on your own and solve real life machine learning problems.

Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.

Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!

What you’ll learn

  • Become a Data Scientist and get hired
  • Master Machine Learning and use it on the job
  • Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
  • Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use
  • Present Data Science projects to management and stakeholders
  • Learn which Machine Learning model to choose for each type of problem
  • Real life case studies and projects to understand how things are done in the real world
  • Learn best practices when it comes to Data Science Workflow
  • Implement Machine Learning algorithms
  • Learn how to program in Python using the latest Python 3
  • How to improve your Machine Learning Models
  • Learn to pre process data, clean data, and analyze large data.
  • Build a portfolio of work to have on your resume
  • Developer Environment setup for Data Science and Machine Learning
  • Supervised and Unsupervised Learning
  • Machine Learning on Time Series data
  • Explore large datasets using data visualization tools like Matplotlib and Seaborn
  • Explore large datasets and wrangle data using Pandas
  • Learn NumPy and how it is used in Machine Learning
  • A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
  • Learn to use the popular library Scikit-learn in your projects
  • Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
  • Learn to perform Classification and Regression modelling
  • Learn how to apply Transfer Learning
Table of Contents

Introduction
1 Course Outline
2 Join Our Online Classroom
3 Exercise Meet The Community
4 Your First Day

Machine Learning 101
5 What Is Machine Learning
6 AI Machine Learning Data Science
7 Exercise Machine Learning Playground
8 How Did We Get Here
9 Exercise YouTube Recommendation Engine
10 Types of Machine Learning
11 Are You Getting It Yet
12 What Is Machine Learning Round 2
13 Section Review
14 Monthly Coding Challenges, Free Resources and Guides

Machine Learning and Data Science Framework
15 Section Overview
16 Introducing Our Framework
17 Step Machine Learning Framework
18 Types of Machine Learning Problems
19 Types of Data
20 Types of Evaluation
21 Features In Data
22 Modelling – Splitting Data
23 Modelling – Picking the Model
24 Modelling – Tuning
25 Modelling – Comparison
26 Overfitting and Underfitting Definitions
27 Experimentation
28 Tools We Will Use
29 Optional Elements of AI

The 2 Paths
30 The 2 Paths
31 Python + Machine Learning Monthly
32 Endorsements On LinkedIN

Data Science Environment Setup
33 Section Overview
34 Introducing Our Tools
35 What is Conda
36 Conda Environments
37 Mac Environment Setup
38 Mac Environment Setup 2
39 Windows Environment Setup
40 Windows Environment Setup 2
41 Linux Environment Setup
42 Sharing your Conda Environment
43 Jupyter Notebook Walkthrough
44 Jupyter Notebook Walkthrough 2
45 Jupyter Notebook Walkthrough 3

Pandas Data Analysis
46 Section Overview
47 Downloading Workbooks and Assignments
48 Pandas Introduction
49 Series, Data Frames and CSVs
50 Data from URLs
51 Describing Data with Pandas
52 Selecting and Viewing Data with Pandas
53 Selecting and Viewing Data with Pandas Part 2
54 Manipulating Data
55 Manipulating Data 2
56 Manipulating Data 3
57 Assignment Pandas Practice
58 How To Download The Course Assignments

NumPy
59 Section Overview
60 NumPy Introduction
61 Quick Note Correction In Next Video
62 NumPy DataTypes and Attributes
63 Creating NumPy Arrays
64 NumPy Random Seed
65 Viewing Arrays and Matrices
66 Manipulating Arrays
67 Manipulating Arrays 2
68 Standard Deviation and Variance
69 Reshape and Transpose
70 Dot Product vs Element Wise
71 Exercise Nut Butter Store Sales
72 Comparison Operators
73 Sorting Arrays
74 Turn Images Into NumPy Arrays
75 Assignment NumPy Practice
76 Optional Extra NumPy resources

Matplotlib Plotting and Data Visualization
77 Section Overview
78 Matplotlib Introduction
79 Importing And Using Matplotlib
80 Anatomy Of A Matplotlib Figure
81 Scatter Plot And Bar Plot
82 Histograms And Subplots
83 Subplots Option 2
84 Quick Tip Data Visualizations
85 Plotting From Pandas DataFrames
86 Quick Note Regular Expressions
87 Plotting From Pandas DataFrames 2
88 Plotting from Pandas DataFrames 3
89 Plotting from Pandas DataFrames 4
90 Plotting from Pandas DataFrames 5
91 Plotting from Pandas DataFrames 6
92 Plotting from Pandas DataFrames 7
93 Customizing Your Plots
94 Customizing Your Plots 2
95 Saving And Sharing Your Plots
96 Assignment Matplotlib Practice

Scikit-learn Creating Machine Learning Models
97 Section Overview
98 Scikit-learn Introduction
99 Quick Note Upcoming Video
100 Refresher What Is Machine Learning
101 Quick Note Upcoming Videos
102 Scikit-learn Cheatsheet
103 Typical scikit-learn Workflow
104 Optional Debugging Warnings In Jupyter
105 Getting Your Data Ready Splitting Your Data
106 Quick Tip Clean, Transform, Reduce
107 Getting Your Data Ready Convert Data To Numbers
108 Note Update to next video (OneHotEncoder can handle NaN None values)
109 Getting Your Data Ready Handling Missing Values With Pandas
110 Extension Feature Scaling
111 Note Correction in the upcoming video (splitting data)
112 Getting Your Data Ready Handling Missing Values With Scikit-learn
113 NEW Choosing The Right Model For Your Data
114 NEW Choosing The Right Model For Your Data 2 (Regression)
115 Quick Note Decision Trees
116 Quick Tip How ML Algorithms Work
117 Choosing The Right Model For Your Data 3 (Classification)
118 Fitting A Model To The Data
119 Making Predictions With Our Model
120 predict() vs predict proba()
121 NEW Making Predictions With Our Model (Regression)
122 NEW Evaluating A Machine Learning Model (Score) Part 1
123 NEW Evaluating A Machine Learning Model (Score) Part 2
124 Evaluating A Machine Learning Model 2 (Cross Validation)
125 Evaluating A Classification Model 1 (Accuracy)
126 Evaluating A Classification Model 2 (ROC Curve)
127 Evaluating A Classification Model 3 (ROC Curve)
128 Reading Extension ROC Curve + AUC
129 Evaluating A Classification Model 4 (Confusion Matrix)
130 NEW Evaluating A Classification Model 5 (Confusion Matrix)
131 Evaluating A Classification Model 6 (Classification Report)
132 NEW Evaluating A Regression Model 1 (R2 Score)
133 NEW Evaluating A Regression Model 2 (MAE)
134 NEW Evaluating A Regression Model 3 (MSE)
135 Machine Learning Model Evaluation
136 NEW Evaluating A Model With Cross Validation and Scoring Parameter
137 NEW Evaluating A Model With Scikit-learn Functions
138 Improving A Machine Learning Model
139 Tuning Hyperparameters
140 Tuning Hyperparameters 2
141 Tuning Hyperparameters 3
142 Note Metric Comparison Improvement
143 Quick Tip Correlation Analysis
144 Saving And Loading A Model
145 Saving And Loading A Model 2
146 Putting It All Together
147 Putting It All Together 2
148 Scikit-Learn Practice

Supervised Learning Classification + Regression
149 Milestone Projects

Milestone Project 1 Supervised Learning (Classification)
150 Section Overview
151 Project Overview
152 Project Environment Setup
153 Optional Windows Project Environment Setup
154 Step 1~4 Framework Setup
155 Getting Our Tools Ready
156 Exploring Our Data
157 Finding Patterns
158 Finding Patterns 2
159 Finding Patterns 3
160 Preparing Our Data For Machine Learning
161 Choosing The Right Models
162 Experimenting With Machine Learning Models
163 Tuning Improving Our Model
164 Tuning Hyperparameters
165 Tuning Hyperparameters 2
166 Tuning Hyperparameters 3
167 Quick Note Confusion Matrix Labels
168 Evaluating Our Model
169 Evaluating Our Model 2
170 Evaluating Our Model 3
171 Finding The Most Important Features
172 Reviewing The Project

Milestone Project 2 Supervised Learning (Time Series Data)
173 Section Overview
174 Project Overview
175 Downloading the data for the next two projects
176 Project Environment Setup
177 Step 1~4 Framework Setup
178 Exploring Our Data
179 Exploring Our Data 2
180 Feature Engineering
181 Turning Data Into Numbers
182 Filling Missing Numerical Values
183 Filling Missing Categorical Values
184 Fitting A Machine Learning Model
185 Splitting Data
186 Challenge What’s wrong with splitting data after filling it
187 Custom Evaluation Function
188 Reducing Data
189 RandomizedSearchCV
190 Improving Hyperparameters
191 Preproccessing Our Data
192 Making Predictions
193 Feature Importance

Data Engineering
194 Data Engineering Introduction
195 What Is Data
196 What Is A Data Engineer
197 What Is A Data Engineer 2
198 What Is A Data Engineer 3
199 What Is A Data Engineer 4
200 Types Of Databases
201 Quick Note Upcoming Video
202 Optional OLTP Databases
203 Optional Learn SQL
204 Hadoop, HDFS and MapReduce
205 Apache Spark and Apache Flink
206 Kafka and Stream Processing

Neural Networks Deep Learning, Transfer Learning and TensorFlow 2
207 Section Overview
208 Deep Learning and Unstructured Data
209 Setting Up With Google
210 Setting Up Google Colab
211 Google Colab Workspace
212 Uploading Project Data
213 Setting Up Our Data
214 Setting Up Our Data 2
215 Importing TensorFlow 2
216 Optional TensorFlow 2.0 Default Issue
217 Using A GPU
218 Optional GPU and Google Colab
219 Optional Reloading Colab Notebook
220 Loading Our Data Labels
221 Preparing The Images
222 Turning Data Labels Into Numbers
223 Creating Our Own Validation Set
224 Preprocess Images
225 Preprocess Images 2
226 Turning Data Into Batches
227 Turning Data Into Batches 2
228 Visualizing Our Data
229 Preparing Our Inputs and Outputs
230 Optional How machines learn and what’s going on behind the scenes
231 Building A Deep Learning Model
232 Building A Deep Learning Model 2
233 Building A Deep Learning Model 3
234 Building A Deep Learning Model 4
235 Summarizing Our Model
236 Evaluating Our Model
237 Preventing Overfitting
238 Training Your Deep Neural Network
239 Evaluating Performance With TensorBoard
240 Make And Transform Predictions
241 Transform Predictions To Text
242 Visualizing Model Predictions
243 Visualizing And Evaluate Model Predictions 2
244 Visualizing And Evaluate Model Predictions 3
245 Saving And Loading A Trained Model
246 Training Model On Full Dataset
247 Making Predictions On Test Images
248 Submitting Model to Kaggle
249 Making Predictions On Our Images
250 Finishing Dog Vision Where to next

Storytelling + Communication How To Present Your Work
251 Section Overview
252 Communicating Your Work
253 Communicating With Managers
254 Communicating With Co-Workers
255 Weekend Project Principle
256 Communicating With Outside World
257 Storytelling
258 Communicating and sharing your work Further reading

Career Advice + Extra Bits
259 Endorsements On LinkedIn
260 Quick Note Upcoming Video
261 What If I Don’t Have Enough Experience
262 Learning Guideline
263 Quick Note Upcoming Videos
264 JTS Learn to Learn
265 JTS Start With Why
266 Quick Note Upcoming Videos
267 CWD Git + Github
268 CWD Git + Github 2
269 Contributing To Open Source
270 Contributing To Open Source 2
271 Coding Challenges
272 Exercise Contribute To Open Source

Learn Python
273 What Is A Programming Language
274 Python Interpreter
275 How To Run Python Code
276 Our First Python Program
277 Latest Version Of Python
278 Python 2 vs Python 3
279 Exercise How Does Python Work
280 Learning Python
281 Python Data Types
282 How To Succeed
283 Numbers
284 Math Functions
285 DEVELOPER FUNDAMENTALS I
286 Operator Precedence
287 Exercise Operator Precedence
288 Optional bin() and complex
289 Variables
290 Expressions vs Statements
291 Augmented Assignment Operator
292 Strings
293 String Concatenation
294 Type Conversion
295 Escape Sequences
296 Formatted Strings
297 String Indexes
298 Immutability
299 Built-In Functions + Methods
300 Booleans
301 Exercise Type Conversion
302 DEVELOPER FUNDAMENTALS II
303 Exercise Password Checker
304 Lists
305 List Slicing
306 Matrix
307 List Methods
308 List Methods 2
309 List Methods 3
310 Common List Patterns
311 List Unpacking
312 None
313 Dictionaries
314 DEVELOPER FUNDAMENTALS III
315 Dictionary Keys
316 Dictionary Methods
317 Dictionary Methods 2
318 Tuples
319 Tuples 2
320 Sets
321 Sets 2

Learn Python Part 2
322 Breaking The Flow
323 Conditional Logic
324 Indentation In Python
325 Truthy vs Falsey
326 Ternary Operator
327 Short Circuiting
328 Logical Operators
329 Exercise Logical Operators
330 is vs ==
331 For Loops
332 Iterables
333 Exercise Tricky Counter
334 range()
335 enumerate()
336 While Loops
337 While Loops 2
338 break, continue, pass
339 Our First GUI
340 DEVELOPER FUNDAMENTALS IV
341 Exercise Find Duplicates
342 Functions
343 Parameters and Arguments
344 Default Parameters and Keyword Arguments
345 return
346 Exercise Tesla
347 Methods vs Functions
348 Docstrings
349 Clean Code
350 args and kwargs
351 Exercise Functions
352 Scope
353 Scope Rules
354 global Keyword
355 nonlocal Keyword
356 Why Do We Need Scope
357 Pure Functions
358 map()
359 filter()
360 zip()
361 reduce()
362 List Comprehensions
363 Set Comprehensions
364 Exercise Comprehensions
365 Python Exam Testing Your Understanding
366 Modules in Python
367 Quick Note Upcoming Videos
368 Optional PyCharm
369 Packages in Python
370 Different Ways To Import
371 Next Steps
372 Bonus Resource Python Cheatsheet

Extra Learn Advanced Statistics and Mathematics for FREE
373 Statistics and Mathematics

Where To Go From Here
374 Become An Alumni
375 Thank You
376 Thank You Part 2

BONUS SECTION
377 Bonus Lecture

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