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Complete A.I. & Machine Learning, Data Science Bootcamp
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Complete A.I. & Machine Learning, Data Science Bootcamp certification course is designed by Andrei Neagoie - Founder of zerotomastery.io & Senior Software Developer, Daniel Bourke - Machine Learning Engineer, Writer, Videomaker and Instructor, and Zero To Mastery, which is offered by Udemy. Complete Machine Learning & Data Science Bootcamp 2022 online course focuses on offering the most up-to-date learning resources regarding machine learning and data science, so students do not have to go through outdated learning materials.
Complete A.I. & Machine Learning, Data Science Bootcamp online training is a thorough and project-based course that introduces learners to all of the abilities that a data scientist must possess. This course includes more than 40 hours of extensive video lectures that aim to teach the core concepts of data science and machine learning, as well as topics such as data visualization, data analysis, and teaches about various technologies that allow individuals to gain real-world experience with data science projects, such as Python, TensorFlow, neural networks, deep learning, and NumPy.
The highlights
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 43 hours of pre-recorded video content
- 55 articles
- 13 downloadable resources
- 1 coding exercise
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 43 hours of pre-recorded video content
- 55 articles
- 13 downloadable resources
- 1 coding exercise
- Unlimited access
- Accessible on mobile devices
Course and certificate fees
Fees information
₹ 4,099
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
Data science knowledge
Machine learning
Knowledge of data visualization
Knowledge of numpy
The syllabus
Introduction
Machine Learning 101
- 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
Machine Learning and Data Science Framework
- 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
The 2 Paths
- The 2 Paths
- Python + Machine Learning Monthly
- Endorsements On LinkedIN
Data Science Environment Setup
- 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
Pandas: Data Analysis
- 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
NumPy
- 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
- Assignment: NumPy Practice
- Optional: Extra NumPy resources
Matplotlib: Plotting and Data Visualization
- 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
Scikit-learn: Creating Machine Learning Models
- 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
Supervised Learning: Classification + Regression
- Milestone Projects!
Milestone Project 1: Supervised Learning (Classification)
- 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
Milestone Project 2: Supervised Learning (Time Series Data)
- 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
Data Engineering
- 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
Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2
- 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?
Storytelling + Communication: How To Present Your Work
- Section Overview
- Communicating Your Work
- Communicating With Managers
- Communicating With Co-Workers
- Weekend Project Principle
- Communicating With Outside World
- Storytelling
- Communicating and sharing your work: Further reading
Career Advice + Extra Bits
- 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
- Coding Challenges
- Exercise: Contribute To Open Source
Learn Python
- 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
- Built-In 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
Learn Python Part 2
- 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
Extra: Learn Advanced Statistics and Mathematics for Free!
- Statistics and Mathematics
Where To Go From Here?
- Become An Alumni
- Thank You
- Thank You Part 2
- Course Review
- The Final Challenge
Bonus Section
- Bonus Lecture
Instructors
Mr Andrei Neagoie
Senior Software Developer
Freelancer
Mr Daniel Bourke
Machine Learning Engineer
Freelancer
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