Complete Machine Learning & Data Science Bootcamp 2023 is a projectbased course that focuses on modern Data Scientist skills primarily Data Science and Machine Learning. The course is made up of 2 tracks, one for those who want to learn Python and Machine Learning from scratch, and the other dives into advanced concepts like Neural Networks, Deep Learning, and Transfer Learning.
Students get access to all of the code, workbooks, and templates (Jupyter Notebooks) on Github and they use the latest version of Python, Tensorflow 2.0, and other libraries. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get Complete Machine Learning & Data Science Bootcamp 2023 Course for INR 449.
Who all can opt for this course?
 Anyone who wants to learn Python, Data Science, or Machine Learning but has no prior experience
 You are a programmer who wants to increase your value by expanding your knowledge of Data Science and Machine Learning
 Anyone who wants to learn about these subjects from professionals who haven’t just taught but have worked in the industry
 You’re seeking a single course that will teach you everything you need to know about Machine Learning and Data Science while keeping you updated with the current trends in the field
 Instead of spending hours watching someone code on your screen without actually getting it, you should study the fundamentals and be able to comprehend the subjects
 For your projects, you want to understand how to employ deep learning and neural networks
 By utilizing potent machine learning techniques, you aim to improve the company you work for or run yourself
Course Highlights
Key Highlights  Details 

Registration Link  Apply Now! 
Price  INR 449 ( 
Duration  43 hours 
Rating  4.6/5 
Student Enrollment  80,325 students 
Instructor  Andrei Neagoie https://www.linkedin.com/in/andreineagoie 
Topics Covered 

Course Level  Intermediate 
Total Student Reviews  13,218 
Learning Outcomes
 Learn to be a Data Scientist to land a job
 Learn how to apply Machine Learning at work
 Using the most recent Tensorflow 2.0
 Utilize cuttingedge tools that major tech firms like Google, Apple, Amazon, and Meta utilize
 Present your data science projects to management and stakeholders
 Find out which Machine Learning model is best for a certain problem type
 To understand how things are done in the real world, use case studies and projects from real life
 Learn about data science workflow best practices
 Put machine learning algorithms into practice
 Utilize the most recent Python 3 version to learn Python programming
 Learn ways to make your machinelearning models better
 Learn how to clean, preprocess, and analyze enormous amounts of data
 Create a collection of your work to include on your CV
 Configuration of the data science and machine learning developer environment
 Utilize data visualization tools like Matplotlib and Seaborn to explore big datasets
 Utilize Pandas to explore huge datasets and manage data
 Learn about NumPy and how to implement it in machine learning
 A portfolio of Data Science and Machine Learning projects, complete with source code and notebooks for portfolio
 Use Scikitlearn, a wellliked library, in your projects
 Learn about data engineering and the applications of tools like Kafka, Spark, and Hadoop
 Discover how to put Transfer Learning to use
Course Content
S.No.  Module (Duration)  Topics 

1.  Introduction (15 minutes)  Course Outline 
Join Our Online Classroom!  
Exercise: Meet Your Classmates and Instructor  
Your First Day  
2.  Machine Learning 101 (40 minutes)  What Is Machine Learning? 
AI/Machine Learning/Data Science  
Exercise: Machine Learning Playground  
How Did We Get Here?  
Exercise: YouTube Recommendation Engine  
Types of Machine Learning  
Are You Getting It Yet?  
What Is Machine Learning? Round 2  
Section Review  
Monthly Coding Challenges, Free Resources and Guides  
3.  Machine Learning and Data Science Framework (01 hour 07 minutes)  Section Overview 
Introducing Our Framework  
6 Step Machine Learning Framework  
Types of Machine Learning Problems  
Types of Data  
Types of Evaluation  
Features In Data  
Modelling – Splitting Data  
Modelling – Picking the Model  
Modelling – Tuning  
Modelling – Comparison  
Overfitting and Underfitting Definitions  
Experimentation  
Tools We Will Use  
Optional: Elements of AI  
4.  The 2 Paths (04 minutes)  The 2 Paths 
Python + Machine Learning Monthly  
Endorsements on LinkedIn  
5.  Data Science Environment Setup (01 hour 48 minutes)  Section Overview 
Introducing Our Tools  
What is Conda?  
Conda Environments  
Mac Environment Setup  
Mac Environment Setup 2  
Windows Environment Setup  
Windows Environment Setup 2  
Linux Environment Setup  
Sharing your Conda Environment  
Jupyter Notebook Walkthrough  
Jupyter Notebook Walkthrough 2  
Jupyter Notebook Walkthrough 3  
6.  Pandas: Data Analysis (01 hour 37 minutes)  Section Overview 
Downloading Workbooks and Assignments  
Pandas Introduction  
Series, Data Frames and CSVs  
Data from URLs  
Describing Data with Pandas  
Selecting and Viewing Data with Pandas  
Selecting and Viewing Data with Pandas Part 2  
Manipulating Data  
Manipulating Data 2  
Manipulating Data 3  
Assignment: Pandas Practice  
How To Download The Course Assignments  
7.  NumPy (02 hours 11 minutes)  Section Overview 
NumPy Introduction  
Quick Note: Correction In Next Video  
NumPy DataTypes and Attributes  
Creating NumPy Arrays  
NumPy Random Seed  
Viewing Arrays and Matrices  
Manipulating Arrays  
Manipulating Arrays 2  
Standard Deviation and Variance  
Reshape and Transpose  
Dot Product vs Element Wise  
Exercise: Nut Butter Store Sales  
Comparison Operators  
Sorting Arrays  
Turn Images Into NumPy Arrays  
Exercise: Imposter Syndrome  
Assignment: NumPy Practice  
Optional: Extra NumPy resources  
8.  Matplotlib: Plotting and Data Visualization (02 hours 18 minutes)  Section Overview 
Matplotlib Introduction  
Importing And Using Matplotlib  
Anatomy Of A Matplotlib Figure  
Scatter Plot And Bar Plot  
Histograms And Subplots  
Subplots Option 2  
Quick Tip: Data Visualizations  
Plotting From Pandas DataFrames  
Quick Note: Regular Expressions  
Plotting From Pandas DataFrames 2  
Plotting from Pandas DataFrames 3  
Plotting from Pandas DataFrames 4  
Plotting from Pandas DataFrames 5  
Plotting from Pandas DataFrames 6  
Plotting from Pandas DataFrames 7  
Customizing Your Plots  
Customizing Your Plots 2  
Saving And Sharing Your Plots  
Assignment: Matplotlib Practice  
9.  Scikitlearn: Creating Machine Learning Models (07 hours 56 minutes)  Section Overview 
Scikitlearn Introduction  
Quick Note: Upcoming Video  
Refresher: What Is Machine Learning?  
Quick Note: Upcoming Videos  
Scikitlearn Cheatsheet  
Typical scikitlearn Workflow  
Optional: Debugging Warnings In Jupyter  
Getting Your Data Ready: Splitting Your Data  
Quick Tip: Clean, Transform, Reduce  
Getting Your Data Ready: Convert Data To Numbers  
Note: Update to next video (OneHotEncoder can handle NaN/None values)  
Getting Your Data Ready: Handling Missing Values With Pandas  
Extension: Feature Scaling  
Note: Correction in the upcoming video (splitting data)  
Getting Your Data Ready: Handling Missing Values With Scikitlearn  
NEW: Choosing The Right Model For Your Data  
NEW: Choosing The Right Model For Your Data 2 (Regression)  
Quick Note: Decision Trees  
Quick Tip: How ML Algorithms Work  
Choosing The Right Model For Your Data 3 (Classification)  
Fitting A Model To The Data  
Making Predictions With Our Model  
predict() vs predict_proba()  
NEW: Making Predictions With Our Model (Regression)  
NEW: Evaluating A Machine Learning Model (Score) Part 1  
NEW: Evaluating A Machine Learning Model (Score) Part 2  
Evaluating A Machine Learning Model 2 (Cross Validation)  
Evaluating A Classification Model 1 (Accuracy)  
Evaluating A Classification Model 2 (ROC Curve)  
Evaluating A Classification Model 3 (ROC Curve)  
Reading Extension: ROC Curve + AUC  
Evaluating A Classification Model 4 (Confusion Matrix)  
NEW: Evaluating A Classification Model 5 (Confusion Matrix)  
Evaluating A Classification Model 6 (Classification Report)  
NEW: Evaluating A Regression Model 1 (R2 Score)  
NEW: Evaluating A Regression Model 2 (MAE)  
NEW: Evaluating A Regression Model 3 (MSE)  
Machine Learning Model Evaluation  
NEW: Evaluating A Model With Cross Validation and Scoring Parameter  
NEW: Evaluating A Model With Scikitlearn Functions  
Improving A Machine Learning Model  
Tuning Hyperparameters  
Tuning Hyperparameters 2  
Tuning Hyperparameters 3  
Note: Metric Comparison Improvement  
Quick Tip: Correlation Analysis  
Saving And Loading A Model  
Saving And Loading A Model 2  
Putting It All Together  
Putting It All Together 2  
ScikitLearn Practice  
10.  Supervised Learning: Classification + Regression (16 seconds)  Milestone Projects! 
11.  Milestone Project 1: Supervised Learning (Classification) (03 hours 35 minutes)  Section Overview 
Project Overview  
Project Environment Setup  
Optional: Windows Project Environment Setup  
Step 1~4 Framework Setup  
Getting Our Tools Ready  
Exploring Our Data  
Finding Patterns  
Finding Patterns 2  
Finding Patterns 3  
Preparing Our Data For Machine Learning  
Choosing The Right Models  
Experimenting With Machine Learning Models  
Tuning/Improving Our Model  
Tuning Hyperparameters  
Tuning Hyperparameters 2  
Tuning Hyperparameters 3  
Quick Note: Confusion Matrix Labels  
Evaluating Our Model  
Evaluating Our Model 2  
Evaluating Our Model 3  
Finding The Most Important Features  
Reviewing The Project  
12.  Milestone Project 2: Supervised Learning (Time Series Data) (03 hours 12 minutes)  Section Overview 
Project Overview  
Downloading the data for the next two projects  
Project Environment Setup  
Step 1~4 Framework Setup  
Exploring Our Data  
Exploring Our Data 2  
Feature Engineering  
Turning Data Into Numbers  
Filling Missing Numerical Values  
Filling Missing Categorical Values  
Fitting A Machine Learning Model  
Splitting Data  
Challenge: What’s wrong with splitting data after filling it?  
Custom Evaluation Function  
Reducing Data  
RandomizedSearchCV  
Improving Hyperparameters  
Preproccessing Our Data  
Making Predictions  
Feature Importance  
13.  Data Engineering (57 minutes)  Data Engineering Introduction 
What Is Data?  
What Is A Data Engineer?  
What Is A Data Engineer 2?  
What Is A Data Engineer 3?  
What Is A Data Engineer 4?  
Types Of Databases  
Quick Note: Upcoming Video  
Optional: OLTP Databases  
Optional: Learn SQL  
Hadoop, HDFS and MapReduce  
Apache Spark and Apache Flink  
Kafka and Stream Processing  
14.  Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2 (07 hours 07 minutes)  Section Overview 
Deep Learning and Unstructured Data  
Setting Up With Google  
Setting Up Google Colab  
Google Colab Workspace  
Uploading Project Data  
Setting Up Our Data  
Setting Up Our Data 2  
Importing TensorFlow 2  
Optional: TensorFlow 2.0 Default Issue  
Using A GPU  
Optional: GPU and Google Colab  
Optional: Reloading Colab Notebook  
Loading Our Data Labels  
Preparing The Images  
Turning Data Labels Into Numbers  
Creating Our Own Validation Set  
Preprocess Images  
Preprocess Images 2  
Turning Data Into Batches  
Turning Data Into Batches 2  
Visualizing Our Data  
Preparing Our Inputs and Outputs  
Optional: How machines learn and what’s going on behind the scenes?  
Building A Deep Learning Model  
Building A Deep Learning Model 2  
Building A Deep Learning Model 3  
Building A Deep Learning Model 4  
Summarizing Our Model  
Evaluating Our Model  
Preventing Overfitting  
Training Your Deep Neural Network  
Evaluating Performance With TensorBoard  
Make And Transform Predictions  
Transform Predictions To Text  
Visualizing Model Predictions  
Visualizing And Evaluate Model Predictions 2  
Visualizing And Evaluate Model Predictions 3  
Saving And Loading A Trained Model  
Training Model On Full Dataset  
Making Predictions On Test Images  
Submitting Model to Kaggle  
Making Predictions On Our Images  
Finishing Dog Vision: Where to next?  
15.  Storytelling + Communication: How To Present Your Work (26 minutes)  Section Overview 
Communicating Your Work  
Communicating With Managers  
Communicating With CoWorkers  
Weekend Project Principle  
Communicating With Outside World  
Storytelling  
Communicating and sharing your work: Further reading  
16.  Career Advice + Extra Bits (01 hour 21 minutes)  Endorsements On LinkedIn 
Quick Note: Upcoming Video  
What If I Don’t Have Enough Experience?  
Learning Guideline  
Quick Note: Upcoming Videos  
JTS: Learn to Learn  
JTS: Start With Why  
Quick Note: Upcoming Videos  
CWD: Git + Github  
CWD: Git + Github 2  
Contributing To Open Source  
Contributing To Open Source 2  
Exercise: Contribute To Open Source  
Coding Challenges  
17.  Learn Python (04 hours 12 minutes)  What Is A Programming Language 
Python Interpreter  
How To Run Python Code  
Our First Python Program  
Latest Version Of Python  
Python 2 vs Python 3  
Exercise: How Does Python Work?  
Learning Python  
Python Data Types  
How To Succeed  
Numbers  
Math Functions  
DEVELOPER FUNDAMENTALS: I  
Operator Precedence  
Exercise: Operator Precedence  
Optional: bin() and complex  
Variables  
Expressions vs Statements  
Augmented Assignment Operator  
Strings  
String Concatenation  
Type Conversion  
Escape Sequences  
Formatted Strings  
String Indexes  
Immutability  
BuiltIn Functions + Methods  
Booleans  
Exercise: Type Conversion  
DEVELOPER FUNDAMENTALS: II  
Exercise: Password Checker  
Lists  
List Slicing  
Matrix  
List Methods  
List Methods 2  
List Methods 3  
Common List Patterns  
List Unpacking  
None  
Dictionaries  
DEVELOPER FUNDAMENTALS: III  
Dictionary Keys  
Dictionary Methods  
Dictionary Methods 2  
Tuples  
Tuples 2  
Sets  
Sets 2  
18.  Learn Python Part 2 (04 hours 49 minutes)  Breaking The Flow 
Conditional Logic  
Indentation In Python  
Truthy vs Falsey  
Ternary Operator  
Short Circuiting  
Logical Operators  
Exercise: Logical Operators  
is vs ==  
For Loops  
Iterables  
Exercise: Tricky Counter  
range()  
enumerate()  
While Loops  
While Loops 2  
break, continue, pass  
Our First GUI  
DEVELOPER FUNDAMENTALS: IV  
Exercise: Find Duplicates  
Functions  
Parameters and Arguments  
Default Parameters and Keyword Arguments  
return  
Exercise: Tesla  
Methods vs Functions  
Docstrings  
Clean Code  
*args and **kwargs  
Exercise: Functions  
Scope  
Scope Rules  
global Keyword  
nonlocal Keyword  
Why Do We Need Scope?  
Pure Functions  
map()  
filter()  
zip()  
reduce()  
List Comprehensions  
Set Comprehensions  
Exercise: Comprehensions  
Python Exam: Testing Your Understanding  
Modules in Python  
Quick Note: Upcoming Videos  
Optional: PyCharm  
Packages in Python  
Different Ways To Import  
Next Steps  
Bonus Resource: Python Cheatsheet  
19.  Extra: Learn Advanced Statistics and Mathematics for FREE! (17 seconds)  Statistics and Mathematics 
20.  Where To Go From Here? (03 minutes)  Become An Alumni 
Thank You  
Thank You Part 2  
Course Review  
The Final Challenge  
21.  BONUS SECTION (16 seconds)  Special Bonus Lecture 
Resources Required
 You don’t need any prior knowledge (not even Math and Statistics)
 A Linux, Windows, or Mac computer with an internet connection
Featured Review
Alie Antar (5/5) : Love the structure and flow of this course. The details are incredibly laid out, and content is explained from the ground up!
Pros
 Nelson G. Gonzalez (5/5): I didn’t expect this course would be so practical and so hands on, I come from taking another bootcamp with a lot of theory (and practice as well, excellent by the way).
 Cherukuri Thanuja (5/5): For Beginners, this is the best Data Science course on Udemy.
 SURAJ TADE (5/5): The way it is being taught and all the learning part, it’s all wonderful.
 Jonathan (5/5): Excellent intro to compsci, wonderfully covers the basics in pandas, matplotlib, numpy, Jupyter notebook.
Cons
 David Boland (1/5): Just one algorithm is presented – the random forest and no explanation as to what this algorithm is for is given.
 Dominik Gschrei (2/5): The runtime is artificially inflated to a ridiculous degree, seemingly to get to the advertisable 30+hrs of runtime.
 Rami Aldoush (1/5): Seriously, this Course is not good Machine Learning section is very poor my advise find another course
 Simon Crayond’bois (1/5): Just started the course, it is EXTREMELY slow (and we all know they do this to artificially increase the global duration).
About the Author
The instructor of this course is Andrei Neagoie who is the founder of zerotomastery.io. With a 4.6 instructor rating and 2,35,184 reviews on Udemy, he offers 26 courses and has taught 9,20,475 students so far.
 Some of the most popular programming and technical courses online are taught by Andrei.
 He is now the creator of ZTM Academy, one of the education platforms with the quickest rate of growth worldwide.
 ZTM Academy is renowned for its topnotch instructors and high student success rates.
 Some of the greatest tech businesses in the world, including Apple, Google, Tesla, Amazon, JP Morgan, IBM, UNIQLO, etc. have hired alumni from his program.
 For many years, he worked as a senior software developer in Silicon Valley and Toronto.
 Now, he is using what he has learned to teach programming skills and to guide you in discovering the incredible job prospects that being a developer in life enables.
 As a selftaught programmer, he is aware that there are a bewildering array of online tutorials, books, and courses that are unnecessarily verbose and ineffective in imparting the necessary skills.
Comparison Table
Parameters  Complete Machine Learning & Data Science Bootcamp 2023  Tensorflow 2.0: Deep Learning and Artificial Intelligence  TensorFlow Developer Certificate in 2023: Zero to Mastery 

Offers  INR 455 (  INR 455 (  INR 455 ( 
Duration  44 hours  22.5 hours  63.5 hours 
Rating  4.6 /5  4.6 /5  4.7 /5 
Student Enrollments  80,313  40,059  41,054 
Instructors  Andrei Neagoie  Lazy Programmer Inc.  Andrei Neagoie 
Register Here  Apply Now!  Apply Now!  Apply Now! 
Complete Machine Learning & Data Science Bootcamp 2023: FAQs
Ques. Is a Bootcamp worth it for Data Science?
Ans. Yes, Data Science Bootcamps generally cost less and take less time to complete than a traditional Data Science degree program. Bootcamps are intensive in nature and they offer more practical and handson training.
Ques. Can you get a job after a Data Science Bootcamp?
Ans. Data Science Bootcamp offers the opportunity to connect with recruiters and Data Scientists. Besides helping students develop portfolios, they teach them all the skills and tools that can help them land jobs after graduation.
Ques. Is it worth learning Machine Learning in 2023?
Ans. There are many career options available to Machine Learning experts. They can become Machine Learning Engineers, Data Scientists, NLP Scientists, Business Intelligence Developers, or HumanCentered Machine Learning Designers with a background in Machine Learning.
Ques. Is Python required for Complete Machine Learning & Data Science Bootcamp?
Ans. Python is required for Complete Machine Learning & Data Science Bootcamp, but the course includes a track for those who want to learn Python and Machine Learning before getting started.
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