Complete Machine Learning & Data Science Bootcamp 2023

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

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

Requirements

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.

Description

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!

Overview

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 Exercise: Machine Learning Playground

Lecture 8 How Did We Get Here?

Lecture 9 Exercise: YouTube Recommendation Engine

Lecture 10 Types of Machine Learning

Lecture 11 Are You Getting It Yet?

Lecture 12 What Is Machine Learning? Round 2

Lecture 13 Section Review

Lecture 14 Monthly Coding Challenges, Free Resources and Guides

Section 3: Machine Learning and Data Science Framework

Lecture 15 Section Overview

Lecture 16 Introducing Our Framework

Lecture 17 6 Step Machine Learning Framework

Lecture 18 Types of Machine Learning Problems

Lecture 19 Types of Data

Lecture 20 Types of Evaluation

Lecture 21 Features In Data

Lecture 22 Modelling - Splitting Data

Lecture 23 Modelling - Picking the Model

Lecture 24 Modelling - Tuning

Lecture 25 Modelling - Comparison

Lecture 26 Overfitting and Underfitting Definitions

Lecture 27 Experimentation

Lecture 28 Tools We Will Use

Lecture 29 Optional: Elements of AI

Section 4: The 2 Paths

Lecture 30 The 2 Paths

Lecture 31 Python + Machine Learning Monthly

Lecture 32 Endorsements On LinkedIN

Section 5: Data Science Environment Setup

Lecture 33 Section Overview

Lecture 34 Introducing Our Tools

Lecture 35 What is Conda?

Lecture 36 Conda Environments

Lecture 37 Mac Environment Setup

Lecture 38 Mac Environment Setup 2

Lecture 39 Windows Environment Setup

Lecture 40 Windows Environment Setup 2

Lecture 41 Linux Environment Setup

Lecture 42 Sharing your Conda Environment

Lecture 43 Jupyter Notebook Walkthrough

Lecture 44 Jupyter Notebook Walkthrough 2

Lecture 45 Jupyter Notebook Walkthrough 3

Section 6: Pandas: Data Analysis

Lecture 46 Section Overview

Lecture 47 Downloading Workbooks and Assignments

Lecture 48 Pandas Introduction

Lecture 49 Series, Data Frames and CSVs

Lecture 50 Data from URLs

Lecture 51 Describing Data with Pandas

Lecture 52 Selecting and Viewing Data with Pandas

Lecture 53 Selecting and Viewing Data with Pandas Part 2

Lecture 54 Manipulating Data

Lecture 55 Manipulating Data 2

Lecture 56 Manipulating Data 3

Lecture 57 Assignment: Pandas Practice

Lecture 58 How To Download The Course Assignments

Section 7: NumPy

Lecture 59 Section Overview

Lecture 60 NumPy Introduction

Lecture 61 Quick Note: Correction In Next Video

Lecture 62 NumPy DataTypes and Attributes

Lecture 63 Creating NumPy Arrays

Lecture 64 NumPy Random Seed

Lecture 65 Viewing Arrays and Matrices

Lecture 66 Manipulating Arrays

Lecture 67 Manipulating Arrays 2

Lecture 68 Standard Deviation and Variance

Lecture 69 Reshape and Transpose

Lecture 70 Dot Product vs Element Wise

Lecture 71 Exercise: Nut Butter Store Sales

Lecture 72 Comparison Operators

Lecture 73 Sorting Arrays

Lecture 74 Turn Images Into NumPy Arrays

Lecture 75 Exercise: Imposter Syndrome

Lecture 76 Assignment: NumPy Practice

Lecture 77 Optional: Extra NumPy resources

Section 8: Matplotlib: Plotting and Data Visualization

Lecture 78 Section Overview

Lecture 79 Matplotlib Introduction

Lecture 80 Importing And Using Matplotlib

Lecture 81 Anatomy Of A Matplotlib Figure

Lecture 82 Scatter Plot And Bar Plot

Lecture 83 Histograms And Subplots

Lecture 84 Subplots Option 2

Lecture 85 Quick Tip: Data Visualizations

Lecture 86 Plotting From Pandas DataFrames

Lecture 87 Quick Note: Regular Expressions

Lecture 88 Plotting From Pandas DataFrames 2

Lecture 89 Plotting from Pandas DataFrames 3

Lecture 90 Plotting from Pandas DataFrames 4

Lecture 91 Plotting from Pandas DataFrames 5

Lecture 92 Plotting from Pandas DataFrames 6

Lecture 93 Plotting from Pandas DataFrames 7

Lecture 94 Customizing Your Plots

Lecture 95 Customizing Your Plots 2

Lecture 96 Saving And Sharing Your Plots

Lecture 97 Assignment: Matplotlib Practice

Section 9: Scikit-learn: Creating Machine Learning Models

Lecture 98 Section Overview

Lecture 99 Scikit-learn Introduction

Lecture 100 Quick Note: Upcoming Video

Lecture 101 Refresher: What Is Machine Learning?

Lecture 102 Quick Note: Upcoming Videos

Lecture 103 Scikit-learn Cheatsheet

Lecture 104 Typical scikit-learn Workflow

Lecture 105 Optional: Debugging Warnings In Jupyter

Lecture 106 Getting Your Data Ready: Splitting Your Data

Lecture 107 Quick Tip: Clean, Transform, Reduce

Lecture 108 Getting Your Data Ready: Convert Data To Numbers

Lecture 109 Note: Update to next video (OneHotEncoder can handle NaN/None values)

Lecture 110 Getting Your Data Ready: Handling Missing Values With Pandas

Lecture 111 Extension: Feature Scaling

Lecture 112 Note: Correction in the upcoming video (splitting data)

Lecture 113 Getting Your Data Ready: Handling Missing Values With Scikit-learn

Lecture 114 NEW: Choosing The Right Model For Your Data

Lecture 115 NEW: Choosing The Right Model For Your Data 2 (Regression)

Lecture 116 Quick Note: Decision Trees

Lecture 117 Quick Tip: How ML Algorithms Work

Lecture 118 Choosing The Right Model For Your Data 3 (Classification)

Lecture 119 Fitting A Model To The Data

Lecture 120 Making Predictions With Our Model

Lecture 121 predict() vs predict_proba()

Lecture 122 NEW: Making Predictions With Our Model (Regression)

Lecture 123 NEW: Evaluating A Machine Learning Model (Score) Part 1

Lecture 124 NEW: Evaluating A Machine Learning Model (Score) Part 2

Lecture 125 Evaluating A Machine Learning Model 2 (Cross Validation)

Lecture 126 Evaluating A Classification Model 1 (Accuracy)

Lecture 127 Evaluating A Classification Model 2 (ROC Curve)

Lecture 128 Evaluating A Classification Model 3 (ROC Curve)

Lecture 129 Reading Extension: ROC Curve + AUC

Lecture 130 Evaluating A Classification Model 4 (Confusion Matrix)

Lecture 131 NEW: Evaluating A Classification Model 5 (Confusion Matrix)

Lecture 132 Evaluating A Classification Model 6 (Classification Report)

Lecture 133 NEW: Evaluating A Regression Model 1 (R2 Score)

Lecture 134 NEW: Evaluating A Regression Model 2 (MAE)

Lecture 135 NEW: Evaluating A Regression Model 3 (MSE)

Lecture 136 Machine Learning Model Evaluation

Lecture 137 NEW: Evaluating A Model With Cross Validation and Scoring Parameter

Lecture 138 NEW: Evaluating A Model With Scikit-learn Functions

Lecture 139 Improving A Machine Learning Model

Lecture 140 Tuning Hyperparameters

Lecture 141 Tuning Hyperparameters 2

Lecture 142 Tuning Hyperparameters 3

Lecture 143 Note: Metric Comparison Improvement

Lecture 144 Quick Tip: Correlation Analysis

Lecture 145 Saving And Loading A Model

Lecture 146 Saving And Loading A Model 2

Lecture 147 Putting It All Together

Lecture 148 Putting It All Together 2

Lecture 149 Scikit-Learn Practice

Section 10: Supervised Learning: Classification + Regression

Lecture 150 Milestone Projects!

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

Lecture 151 Section Overview

Lecture 152 Project Overview

Lecture 153 Project Environment Setup

Lecture 154 Optional: Windows Project Environment Setup

Lecture 155 Step 1~4 Framework Setup

Lecture 156 Getting Our Tools Ready

Lecture 157 Exploring Our Data

Lecture 158 Finding Patterns

Lecture 159 Finding Patterns 2

Lecture 160 Finding Patterns 3

Lecture 161 Preparing Our Data For Machine Learning

Lecture 162 Choosing The Right Models

Lecture 163 Experimenting With Machine Learning Models

Lecture 164 Tuning/Improving Our Model

Lecture 165 Tuning Hyperparameters

Lecture 166 Tuning Hyperparameters 2

Lecture 167 Tuning Hyperparameters 3

Lecture 168 Quick Note: Confusion Matrix Labels

Lecture 169 Evaluating Our Model

Lecture 170 Evaluating Our Model 2

Lecture 171 Evaluating Our Model 3

Lecture 172 Finding The Most Important Features

Lecture 173 Reviewing The Project

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

Lecture 174 Section Overview

Lecture 175 Project Overview

Lecture 176 Downloading the data for the next two projects

Lecture 177 Project Environment Setup

Lecture 178 Step 1~4 Framework Setup

Lecture 179 Exploring Our Data

Lecture 180 Exploring Our Data 2

Lecture 181 Feature Engineering

Lecture 182 Turning Data Into Numbers

Lecture 183 Filling Missing Numerical Values

Lecture 184 Filling Missing Categorical Values

Lecture 185 Fitting A Machine Learning Model

Lecture 186 Splitting Data

Lecture 187 Challenge: What's wrong with splitting data after filling it?

Lecture 188 Custom Evaluation Function

Lecture 189 Reducing Data

Lecture 190 RandomizedSearchCV

Lecture 191 Improving Hyperparameters

Lecture 192 Preproccessing Our Data

Lecture 193 Making Predictions

Lecture 194 Feature Importance

Section 13: Data Engineering

Lecture 195 Data Engineering Introduction

Lecture 196 What Is Data?

Lecture 197 What Is A Data Engineer?

Lecture 198 What Is A Data Engineer 2?

Lecture 199 What Is A Data Engineer 3?

Lecture 200 What Is A Data Engineer 4?

Lecture 201 Types Of Databases

Lecture 202 Quick Note: Upcoming Video

Lecture 203 Optional: OLTP Databases

Lecture 204 Optional: Learn SQL

Lecture 205 Hadoop, HDFS and MapReduce

Lecture 206 Apache Spark and Apache Flink

Lecture 207 Kafka and Stream Processing

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

Lecture 208 Section Overview

Lecture 209 Deep Learning and Unstructured Data

Lecture 210 Setting Up With Google

Lecture 211 Setting Up Google Colab

Lecture 212 Google Colab Workspace

Lecture 213 Uploading Project Data

Lecture 214 Setting Up Our Data

Lecture 215 Setting Up Our Data 2

Lecture 216 Importing TensorFlow 2

Lecture 217 Optional: TensorFlow 2.0 Default Issue

Lecture 218 Using A GPU

Lecture 219 Optional: GPU and Google Colab

Lecture 220 Optional: Reloading Colab Notebook

Lecture 221 Loading Our Data Labels

Lecture 222 Preparing The Images

Lecture 223 Turning Data Labels Into Numbers

Lecture 224 Creating Our Own Validation Set

Lecture 225 Preprocess Images

Lecture 226 Preprocess Images 2

Lecture 227 Turning Data Into Batches

Lecture 228 Turning Data Into Batches 2

Lecture 229 Visualizing Our Data

Lecture 230 Preparing Our Inputs and Outputs

Lecture 231 Optional: How machines learn and what's going on behind the scenes?

Lecture 232 Building A Deep Learning Model

Lecture 233 Building A Deep Learning Model 2

Lecture 234 Building A Deep Learning Model 3

Lecture 235 Building A Deep Learning Model 4

Lecture 236 Summarizing Our Model

Lecture 237 Evaluating Our Model

Lecture 238 Preventing Overfitting

Lecture 239 Training Your Deep Neural Network

Lecture 240 Evaluating Performance With TensorBoard

Lecture 241 Make And Transform Predictions

Lecture 242 Transform Predictions To Text

Lecture 243 Visualizing Model Predictions

Lecture 244 Visualizing And Evaluate Model Predictions 2

Lecture 245 Visualizing And Evaluate Model Predictions 3

Lecture 246 Saving And Loading A Trained Model

Lecture 247 Training Model On Full Dataset

Lecture 248 Making Predictions On Test Images

Lecture 249 Submitting Model to Kaggle

Lecture 250 Making Predictions On Our Images

Lecture 251 Finishing Dog Vision: Where to next?

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

Lecture 252 Section Overview

Lecture 253 Communicating Your Work

Lecture 254 Communicating With Managers

Lecture 255 Communicating With Co-Workers

Lecture 256 Weekend Project Principle

Lecture 257 Communicating With Outside World

Lecture 258 Storytelling

Lecture 259 Communicating and sharing your work: Further reading

Section 16: Career Advice + Extra Bits

Lecture 260 Endorsements On LinkedIn

Lecture 261 Quick Note: Upcoming Video

Lecture 262 What If I Don't Have Enough Experience?

Lecture 263 Learning Guideline

Lecture 264 Quick Note: Upcoming Videos

Lecture 265 JTS: Learn to Learn

Lecture 266 JTS: Start With Why

Lecture 267 Quick Note: Upcoming Videos

Lecture 268 CWD: Git + Github

Lecture 269 CWD: Git + Github 2

Lecture 270 Contributing To Open Source

Lecture 271 Contributing To Open Source 2

Lecture 272 Exercise: Contribute To Open Source

Lecture 273 Coding Challenges

Section 17: Learn Python

Lecture 274 What Is A Programming Language

Lecture 275 Python Interpreter

Lecture 276 How To Run Python Code

Lecture 277 Our First Python Program

Lecture 278 Latest Version Of Python

Lecture 279 Python 2 vs Python 3

Lecture 280 Exercise: How Does Python Work?

Lecture 281 Learning Python

Lecture 282 Python Data Types

Lecture 283 How To Succeed

Lecture 284 Numbers

Lecture 285 Math Functions

Lecture 286 DEVELOPER FUNDAMENTALS: I

Lecture 287 Operator Precedence

Lecture 288 Exercise: Operator Precedence

Lecture 289 Optional: bin() and complex

Lecture 290 Variables

Lecture 291 Expressions vs Statements

Lecture 292 Augmented Assignment Operator

Lecture 293 Strings

Lecture 294 String Concatenation

Lecture 295 Type Conversion

Lecture 296 Escape Sequences

Lecture 297 Formatted Strings

Lecture 298 String Indexes

Lecture 299 Immutability

Lecture 300 Built-In Functions + Methods

Lecture 301 Booleans

Lecture 302 Exercise: Type Conversion

Lecture 303 DEVELOPER FUNDAMENTALS: II

Lecture 304 Exercise: Password Checker

Lecture 305 Lists

Lecture 306 List Slicing

Lecture 307 Matrix

Lecture 308 List Methods

Lecture 309 List Methods 2

Lecture 310 List Methods 3

Lecture 311 Common List Patterns

Lecture 312 List Unpacking

Lecture 313 None

Lecture 314 Dictionaries

Lecture 315 DEVELOPER FUNDAMENTALS: III

Lecture 316 Dictionary Keys

Lecture 317 Dictionary Methods

Lecture 318 Dictionary Methods 2

Lecture 319 Tuples

Lecture 320 Tuples 2

Lecture 321 Sets

Lecture 322 Sets 2

Section 18: Learn Python Part 2

Lecture 323 Breaking The Flow

Lecture 324 Conditional Logic

Lecture 325 Indentation In Python

Lecture 326 Truthy vs Falsey

Lecture 327 Ternary Operator

Lecture 328 Short Circuiting

Lecture 329 Logical Operators

Lecture 330 Exercise: Logical Operators

Lecture 331 is vs ==

Lecture 332 For Loops

Lecture 333 Iterables

Lecture 334 Exercise: Tricky Counter

Lecture 335 range()

Lecture 336 enumerate()

Lecture 337 While Loops

Lecture 338 While Loops 2

Lecture 339 break, continue, pass

Lecture 340 Our First GUI

Lecture 341 DEVELOPER FUNDAMENTALS: IV

Lecture 342 Exercise: Find Duplicates

Lecture 343 Functions

Lecture 344 Parameters and Arguments

Lecture 345 Default Parameters and Keyword Arguments

Lecture 346 return

Lecture 347 Exercise: Tesla

Lecture 348 Methods vs Functions

Lecture 349 Docstrings

Lecture 350 Clean Code

Lecture 351 *args and **kwargs

Lecture 352 Exercise: Functions

Lecture 353 Scope

Lecture 354 Scope Rules

Lecture 355 global Keyword

Lecture 356 nonlocal Keyword

Lecture 357 Why Do We Need Scope?

Lecture 358 Pure Functions

Lecture 359 map()

Lecture 360 filter()

Lecture 361 zip()

Lecture 362 reduce()

Lecture 363 List Comprehensions

Lecture 364 Set Comprehensions

Lecture 365 Exercise: Comprehensions

Lecture 366 Python Exam: Testing Your Understanding

Lecture 367 Modules in Python

Lecture 368 Quick Note: Upcoming Videos

Lecture 369 Optional: PyCharm

Lecture 370 Packages in Python

Lecture 371 Different Ways To Import

Lecture 372 Next Steps

Lecture 373 Bonus Resource: Python Cheatsheet

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

Lecture 374 Statistics and Mathematics

Section 20: Where To Go From Here?

Lecture 375 Become An Alumni

Lecture 376 Thank You

Lecture 377 Thank You Part 2

Section 21: BONUS SECTION

Lecture 378 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.