Complete Machine Learning Data Science Bootcamp 2023

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
Complete Machine Learning Data Science Bootcamp 2023
File Size :
29.37 GB
Total length :
43h 52m



Andrei Neagoie


Last update




Complete Machine Learning Data Science Bootcamp 2023

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 Meta 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

Complete Machine Learning Data Science Bootcamp 2023


No prior experience is needed (not even Math and Statistics). We start from the very basics.
A computer (Linux/Windows/Mac) with internet connection.
Two paths for those that know programming and those that don’t.
All tools used in this course are free for you to use.


This is a top selling Machine Learning and Data Science course just updated this month with the latest trends and skills for 2023! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ 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, Meta, + 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 ColabBy 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!Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!Taught By:Daniel Bourke:A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.My experience in machine learning comes from working at one of Australia’s fastest-growing artificial intelligence agencies, Max Kelsen.I’ve worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia’s leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia’s largest insurance groups.Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question “what should I eat?”.Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views.I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it’s like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.Questions are always welcome.——–Andrei Neagoie:Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc… He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don’t know where to start when learning a complex subject matter, or even worse, most people don’t have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student’s valuable time.   Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. Taking his experience in educational psychology and coding, Andrei’s courses will take you on an understanding of complex subjects that you never thought would be possible.   See you inside the course!


Section 1: Introduction

Lecture 1 Course Outline

Lecture 2 Join Our Online Classroom!

Lecture 3 Exercise: Meet Your Classmates and Instructor

Lecture 4 Your First Day

Section 2: Machine Learning 101

Lecture 5 What Is Machine Learning?

Lecture 6 AI/Machine Learning/Data Science

Lecture 7 ZTM Resources

Lecture 8 Exercise: Machine Learning Playground

Lecture 9 How Did We Get Here?

Lecture 10 Exercise: YouTube Recommendation Engine

Lecture 11 Types of Machine Learning

Lecture 12 Are You Getting It Yet?

Lecture 13 What Is Machine Learning? Round 2

Lecture 14 Section Review

Lecture 15 Monthly Coding Challenges, Free Resources and Guides

Section 3: Machine Learning and Data Science Framework

Lecture 16 Section Overview

Lecture 17 Introducing Our Framework

Lecture 18 6 Step Machine Learning Framework

Lecture 19 Types of Machine Learning Problems

Lecture 20 Types of Data

Lecture 21 Types of Evaluation

Lecture 22 Features In Data

Lecture 23 Modelling – Splitting Data

Lecture 24 Modelling – Picking the Model

Lecture 25 Modelling – Tuning

Lecture 26 Modelling – Comparison

Lecture 27 Overfitting and Underfitting Definitions

Lecture 28 Experimentation

Lecture 29 Tools We Will Use

Lecture 30 Optional: Elements of AI

Section 4: The 2 Paths

Lecture 31 The 2 Paths

Lecture 32 Python + Machine Learning Monthly

Lecture 33 Endorsements On LinkedIN

Section 5: Data Science Environment Setup

Lecture 34 Section Overview

Lecture 35 Introducing Our Tools

Lecture 36 What is Conda?

Lecture 37 Conda Environments

Lecture 38 Mac Environment Setup

Lecture 39 Mac Environment Setup 2

Lecture 40 Windows Environment Setup

Lecture 41 Windows Environment Setup 2

Lecture 42 Linux Environment Setup

Lecture 43 Sharing your Conda Environment

Lecture 44 Jupyter Notebook Walkthrough

Lecture 45 Jupyter Notebook Walkthrough 2

Lecture 46 Jupyter Notebook Walkthrough 3

Section 6: Pandas: Data Analysis

Lecture 47 Section Overview

Lecture 48 Downloading Workbooks and Assignments

Lecture 49 Pandas Introduction

Lecture 50 Series, Data Frames and CSVs

Lecture 51 Data from URLs

Lecture 52 Describing Data with Pandas

Lecture 53 Selecting and Viewing Data with Pandas

Lecture 54 Selecting and Viewing Data with Pandas Part 2

Lecture 55 Manipulating Data

Lecture 56 Manipulating Data 2

Lecture 57 Manipulating Data 3

Lecture 58 Assignment: Pandas Practice

Lecture 59 How To Download The Course Assignments

Section 7: NumPy

Lecture 60 Section Overview

Lecture 61 NumPy Introduction

Lecture 62 Quick Note: Correction In Next Video

Lecture 63 NumPy DataTypes and Attributes

Lecture 64 Creating NumPy Arrays

Lecture 65 NumPy Random Seed

Lecture 66 Viewing Arrays and Matrices

Lecture 67 Manipulating Arrays

Lecture 68 Manipulating Arrays 2

Lecture 69 Standard Deviation and Variance

Lecture 70 Reshape and Transpose

Lecture 71 Dot Product vs Element Wise

Lecture 72 Exercise: Nut Butter Store Sales

Lecture 73 Comparison Operators

Lecture 74 Sorting Arrays

Lecture 75 Turn Images Into NumPy Arrays

Lecture 76 Exercise: Imposter Syndrome

Lecture 77 Assignment: NumPy Practice

Lecture 78 Optional: Extra NumPy resources

Section 8: Matplotlib: Plotting and Data Visualization

Lecture 79 Section Overview

Lecture 80 Matplotlib Introduction

Lecture 81 Importing And Using Matplotlib

Lecture 82 Anatomy Of A Matplotlib Figure

Lecture 83 Scatter Plot And Bar Plot

Lecture 84 Histograms And Subplots

Lecture 85 Subplots Option 2

Lecture 86 Quick Tip: Data Visualizations

Lecture 87 Plotting From Pandas DataFrames

Lecture 88 Quick Note: Regular Expressions

Lecture 89 Plotting From Pandas DataFrames 2

Lecture 90 Plotting from Pandas DataFrames 3

Lecture 91 Plotting from Pandas DataFrames 4

Lecture 92 Plotting from Pandas DataFrames 5

Lecture 93 Plotting from Pandas DataFrames 6

Lecture 94 Plotting from Pandas DataFrames 7

Lecture 95 Customizing Your Plots

Lecture 96 Customizing Your Plots 2

Lecture 97 Saving And Sharing Your Plots

Lecture 98 Assignment: Matplotlib Practice

Section 9: Scikit-learn: Creating Machine Learning Models

Lecture 99 Section Overview

Lecture 100 Scikit-learn Introduction

Lecture 101 Quick Note: Upcoming Video

Lecture 102 Refresher: What Is Machine Learning?

Lecture 103 Quick Note: Upcoming Videos

Lecture 104 Scikit-learn Cheatsheet

Lecture 105 Typical scikit-learn Workflow

Lecture 106 Optional: Debugging Warnings In Jupyter

Lecture 107 Getting Your Data Ready: Splitting Your Data

Lecture 108 Quick Tip: Clean, Transform, Reduce

Lecture 109 Getting Your Data Ready: Convert Data To Numbers

Lecture 110 Note: Update to next video (OneHotEncoder can handle NaN/None values)

Lecture 111 Getting Your Data Ready: Handling Missing Values With Pandas

Lecture 112 Extension: Feature Scaling

Lecture 113 Note: Correction in the upcoming video (splitting data)

Lecture 114 Getting Your Data Ready: Handling Missing Values With Scikit-learn

Lecture 115 NEW: Choosing The Right Model For Your Data

Lecture 116 NEW: Choosing The Right Model For Your Data 2 (Regression)

Lecture 117 Quick Note: Decision Trees

Lecture 118 Quick Tip: How ML Algorithms Work

Lecture 119 Choosing The Right Model For Your Data 3 (Classification)

Lecture 120 Fitting A Model To The Data

Lecture 121 Making Predictions With Our Model

Lecture 122 predict() vs predict_proba()

Lecture 123 NEW: Making Predictions With Our Model (Regression)

Lecture 124 NEW: Evaluating A Machine Learning Model (Score) Part 1

Lecture 125 NEW: Evaluating A Machine Learning Model (Score) Part 2

Lecture 126 Evaluating A Machine Learning Model 2 (Cross Validation)

Lecture 127 Evaluating A Classification Model 1 (Accuracy)

Lecture 128 Evaluating A Classification Model 2 (ROC Curve)

Lecture 129 Evaluating A Classification Model 3 (ROC Curve)

Lecture 130 Reading Extension: ROC Curve + AUC

Lecture 131 Evaluating A Classification Model 4 (Confusion Matrix)

Lecture 132 NEW: Evaluating A Classification Model 5 (Confusion Matrix)

Lecture 133 Evaluating A Classification Model 6 (Classification Report)

Lecture 134 NEW: Evaluating A Regression Model 1 (R2 Score)

Lecture 135 NEW: Evaluating A Regression Model 2 (MAE)

Lecture 136 NEW: Evaluating A Regression Model 3 (MSE)

Lecture 137 Machine Learning Model Evaluation

Lecture 138 NEW: Evaluating A Model With Cross Validation and Scoring Parameter

Lecture 139 NEW: Evaluating A Model With Scikit-learn Functions

Lecture 140 Improving A Machine Learning Model

Lecture 141 Tuning Hyperparameters

Lecture 142 Tuning Hyperparameters 2

Lecture 143 Tuning Hyperparameters 3

Lecture 144 Note: Metric Comparison Improvement

Lecture 145 Quick Tip: Correlation Analysis

Lecture 146 Saving And Loading A Model

Lecture 147 Saving And Loading A Model 2

Lecture 148 Putting It All Together

Lecture 149 Putting It All Together 2

Lecture 150 Scikit-Learn Practice

Section 10: Supervised Learning: Classification + Regression

Lecture 151 Milestone Projects!

Section 11: Milestone Project 1: Supervised Learning (Classification)

Lecture 152 Section Overview

Lecture 153 Project Overview

Lecture 154 Project Environment Setup

Lecture 155 Optional: Windows Project Environment Setup

Lecture 156 Step 1~4 Framework Setup

Lecture 157 Getting Our Tools Ready

Lecture 158 Exploring Our Data

Lecture 159 Finding Patterns

Lecture 160 Finding Patterns 2

Lecture 161 Finding Patterns 3

Lecture 162 Preparing Our Data For Machine Learning

Lecture 163 Choosing The Right Models

Lecture 164 Experimenting With Machine Learning Models

Lecture 165 Tuning/Improving Our Model

Lecture 166 Tuning Hyperparameters

Lecture 167 Tuning Hyperparameters 2

Lecture 168 Tuning Hyperparameters 3

Lecture 169 Quick Note: Confusion Matrix Labels

Lecture 170 Evaluating Our Model

Lecture 171 Evaluating Our Model 2

Lecture 172 Evaluating Our Model 3

Lecture 173 Finding The Most Important Features

Lecture 174 Reviewing The Project

Section 12: Milestone Project 2: Supervised Learning (Time Series Data)

Lecture 175 Section Overview

Lecture 176 Project Overview

Lecture 177 Downloading the data for the next two projects

Lecture 178 Project Environment Setup

Lecture 179 Step 1~4 Framework Setup

Lecture 180 Exploring Our Data

Lecture 181 Exploring Our Data 2

Lecture 182 Feature Engineering

Lecture 183 Turning Data Into Numbers

Lecture 184 Filling Missing Numerical Values

Lecture 185 Filling Missing Categorical Values

Lecture 186 Fitting A Machine Learning Model

Lecture 187 Splitting Data

Lecture 188 Challenge: What’s wrong with splitting data after filling it?

Lecture 189 Custom Evaluation Function

Lecture 190 Reducing Data

Lecture 191 RandomizedSearchCV

Lecture 192 Improving Hyperparameters

Lecture 193 Preproccessing Our Data

Lecture 194 Making Predictions

Lecture 195 Feature Importance

Section 13: Data Engineering

Lecture 196 Data Engineering Introduction

Lecture 197 What Is Data?

Lecture 198 What Is A Data Engineer?

Lecture 199 What Is A Data Engineer 2?

Lecture 200 What Is A Data Engineer 3?

Lecture 201 What Is A Data Engineer 4?

Lecture 202 Types Of Databases

Lecture 203 Quick Note: Upcoming Video

Lecture 204 Optional: OLTP Databases

Lecture 205 Optional: Learn SQL

Lecture 206 Hadoop, HDFS and MapReduce

Lecture 207 Apache Spark and Apache Flink

Lecture 208 Kafka and Stream Processing

Section 14: Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2

Lecture 209 Section Overview

Lecture 210 Deep Learning and Unstructured Data

Lecture 211 Setting Up With Google

Lecture 212 Setting Up Google Colab

Lecture 213 Google Colab Workspace

Lecture 214 Uploading Project Data

Lecture 215 Setting Up Our Data

Lecture 216 Setting Up Our Data 2

Lecture 217 Importing TensorFlow 2

Lecture 218 Optional: TensorFlow 2.0 Default Issue

Lecture 219 Using A GPU

Lecture 220 Optional: GPU and Google Colab

Lecture 221 Optional: Reloading Colab Notebook

Lecture 222 Loading Our Data Labels

Lecture 223 Preparing The Images

Lecture 224 Turning Data Labels Into Numbers

Lecture 225 Creating Our Own Validation Set

Lecture 226 Preprocess Images

Lecture 227 Preprocess Images 2

Lecture 228 Turning Data Into Batches

Lecture 229 Turning Data Into Batches 2

Lecture 230 Visualizing Our Data

Lecture 231 Preparing Our Inputs and Outputs

Lecture 232 Optional: How machines learn and what’s going on behind the scenes?

Lecture 233 Building A Deep Learning Model

Lecture 234 Building A Deep Learning Model 2

Lecture 235 Building A Deep Learning Model 3

Lecture 236 Building A Deep Learning Model 4

Lecture 237 Summarizing Our Model

Lecture 238 Evaluating Our Model

Lecture 239 Preventing Overfitting

Lecture 240 Training Your Deep Neural Network

Lecture 241 Evaluating Performance With TensorBoard

Lecture 242 Make And Transform Predictions

Lecture 243 Transform Predictions To Text

Lecture 244 Visualizing Model Predictions

Lecture 245 Visualizing And Evaluate Model Predictions 2

Lecture 246 Visualizing And Evaluate Model Predictions 3

Lecture 247 Saving And Loading A Trained Model

Lecture 248 Training Model On Full Dataset

Lecture 249 Making Predictions On Test Images

Lecture 250 Submitting Model to Kaggle

Lecture 251 Making Predictions On Our Images

Lecture 252 Finishing Dog Vision: Where to next?

Section 15: Storytelling + Communication: How To Present Your Work

Lecture 253 Section Overview

Lecture 254 Communicating Your Work

Lecture 255 Communicating With Managers

Lecture 256 Communicating With Co-Workers

Lecture 257 Weekend Project Principle

Lecture 258 Communicating With Outside World

Lecture 259 Storytelling

Lecture 260 Communicating and sharing your work: Further reading

Section 16: Career Advice + Extra Bits

Lecture 261 Endorsements On LinkedIn

Lecture 262 Quick Note: Upcoming Video

Lecture 263 What If I Don’t Have Enough Experience?

Lecture 264 Learning Guideline

Lecture 265 Quick Note: Upcoming Videos

Lecture 266 JTS: Learn to Learn

Lecture 267 JTS: Start With Why

Lecture 268 Quick Note: Upcoming Videos

Lecture 269 CWD: Git + Github

Lecture 270 CWD: Git + Github 2

Lecture 271 Contributing To Open Source

Lecture 272 Contributing To Open Source 2

Lecture 273 Exercise: Contribute To Open Source

Lecture 274 Coding Challenges

Section 17: Learn Python

Lecture 275 What Is A Programming Language

Lecture 276 Python Interpreter

Lecture 277 How To Run Python Code

Lecture 278 Our First Python Program

Lecture 279 Latest Version Of Python

Lecture 280 Python 2 vs Python 3

Lecture 281 Exercise: How Does Python Work?

Lecture 282 Learning Python

Lecture 283 Python Data Types

Lecture 284 How To Succeed

Lecture 285 Numbers

Lecture 286 Math Functions


Lecture 288 Operator Precedence

Lecture 289 Exercise: Operator Precedence

Lecture 290 Optional: bin() and complex

Lecture 291 Variables

Lecture 292 Expressions vs Statements

Lecture 293 Augmented Assignment Operator

Lecture 294 Strings

Lecture 295 String Concatenation

Lecture 296 Type Conversion

Lecture 297 Escape Sequences

Lecture 298 Formatted Strings

Lecture 299 String Indexes

Lecture 300 Immutability

Lecture 301 Built-In Functions + Methods

Lecture 302 Booleans

Lecture 303 Exercise: Type Conversion


Lecture 305 Exercise: Password Checker

Lecture 306 Lists

Lecture 307 List Slicing

Lecture 308 Matrix

Lecture 309 List Methods

Lecture 310 List Methods 2

Lecture 311 List Methods 3

Lecture 312 Common List Patterns

Lecture 313 List Unpacking

Lecture 314 None

Lecture 315 Dictionaries


Lecture 317 Dictionary Keys

Lecture 318 Dictionary Methods

Lecture 319 Dictionary Methods 2

Lecture 320 Tuples

Lecture 321 Tuples 2

Lecture 322 Sets

Lecture 323 Sets 2

Section 18: Learn Python Part 2

Lecture 324 Breaking The Flow

Lecture 325 Conditional Logic

Lecture 326 Indentation In Python

Lecture 327 Truthy vs Falsey

Lecture 328 Ternary Operator

Lecture 329 Short Circuiting

Lecture 330 Logical Operators

Lecture 331 Exercise: Logical Operators

Lecture 332 is vs ==

Lecture 333 For Loops

Lecture 334 Iterables

Lecture 335 Exercise: Tricky Counter

Lecture 336 range()

Lecture 337 enumerate()

Lecture 338 While Loops

Lecture 339 While Loops 2

Lecture 340 break, continue, pass

Lecture 341 Our First GUI


Lecture 343 Exercise: Find Duplicates

Lecture 344 Functions

Lecture 345 Parameters and Arguments

Lecture 346 Default Parameters and Keyword Arguments

Lecture 347 return

Lecture 348 Exercise: Tesla

Lecture 349 Methods vs Functions

Lecture 350 Docstrings

Lecture 351 Clean Code

Lecture 352 *args and **kwargs

Lecture 353 Exercise: Functions

Lecture 354 Scope

Lecture 355 Scope Rules

Lecture 356 global Keyword

Lecture 357 nonlocal Keyword

Lecture 358 Why Do We Need Scope?

Lecture 359 Pure Functions

Lecture 360 map()

Lecture 361 filter()

Lecture 362 zip()

Lecture 363 reduce()

Lecture 364 List Comprehensions

Lecture 365 Set Comprehensions

Lecture 366 Exercise: Comprehensions

Lecture 367 Python Exam: Testing Your Understanding

Lecture 368 Modules in Python

Lecture 369 Quick Note: Upcoming Videos

Lecture 370 Optional: PyCharm

Lecture 371 Packages in Python

Lecture 372 Different Ways To Import

Lecture 373 Next Steps

Lecture 374 Bonus Resource: Python Cheatsheet

Section 19: Extra: Learn Advanced Statistics and Mathematics for FREE!

Lecture 375 Statistics and Mathematics

Section 20: Where To Go From Here?

Lecture 376 Become An Alumni

Lecture 377 Thank You

Lecture 378 Thank You Part 2


Lecture 379 Special Bonus Lecture

Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python,You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable,Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field,You’re looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry,You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”,You want to learn to use Deep learning and Neural Networks with your projects,You want to add value to your own business or company you work for, by using powerful Machine Learning tools.

Course Information:

Udemy | English | 43h 52m | 29.37 GB
Created by: Andrei Neagoie

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