machine learning

Complete Machine Learning & Data Science Bootcamp 2023 is a project-based 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 HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987% off
Duration43 hours
Student Enrollment80,325 students
InstructorAndrei Neagoie
Topics Covered
  • Data Exploration and Visualizations
  • Neural Networks and Deep Learning
  • Model Evaluation and Analysis
Course LevelIntermediate
Total Student Reviews13,218

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 cutting-edge 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 machine-learning 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 Scikit-learn, a well-liked 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
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.Scikit-learn: Creating Machine Learning Models (07 hours 56 minutes)Section Overview
Scikit-learn Introduction
Quick Note: Upcoming Video
Refresher: What Is Machine Learning?
Quick Note: Upcoming Videos
Scikit-learn Cheatsheet
Typical scikit-learn 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 Scikit-learn
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 Scikit-learn 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
Scikit-Learn 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
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 Co-Workers
Weekend Project Principle
Communicating With Outside World
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
Math Functions
Operator Precedence
Exercise: Operator Precedence
Optional: bin() and complex
Expressions vs Statements
Augmented Assignment Operator
String Concatenation
Type Conversion
Escape Sequences
Formatted Strings
String Indexes
Built-In Functions + Methods
Exercise: Type Conversion
Exercise: Password Checker
List Slicing
List Methods
List Methods 2
List Methods 3
Common List Patterns
List Unpacking
Dictionary Keys
Dictionary Methods
Dictionary Methods 2
Tuples 2
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
Exercise: Tricky Counter
While Loops
While Loops 2
break, continue, pass
Our First GUI
Exercise: Find Duplicates
Parameters and Arguments
Default Parameters and Keyword Arguments
Exercise: Tesla
Methods vs Functions
Clean Code
*args and **kwargs
Exercise: Functions
Scope Rules
global Keyword
nonlocal Keyword
Why Do We Need Scope?
Pure Functions
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!


  • 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 comp-sci, wonderfully covers the basics in pandas, matplotlib, numpy, Jupyter notebook.


  • 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 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 top-notch 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 self-taught 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

ParametersComplete Machine Learning & Data Science Bootcamp 2023Tensorflow 2.0: Deep Learning and Artificial IntelligenceTensorFlow Developer Certificate in 2023: Zero to Mastery
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration44 hours22.5 hours63.5 hours
Rating4.6 /54.6 /54.7 /5
Student Enrollments80,31340,05941,054
InstructorsAndrei NeagoieLazy Programmer Inc.Andrei Neagoie
Register HereApply 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 hands-on 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 Human-Centered 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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