Data Science Masterclass: Hands-On Ml & Ai Projects
Data Science Masterclass: Hands-On Ml & Ai Projects
Published 5/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 991.87 MB | Duration: 1h 40m
Solve Real World Business Problems with AI Solutions, Learn Data Science, Data Analysis, Machine Learning (Artificial In
What you'll learn
Build a portfolio of work to have on your resume
Developer Environment setup for Data Science and Machine Learning
Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
Real life case studies and projects to understand how things are done in the real world
Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
Requirements
No prior experience is needed (not even Math and Statistics). We start from the very basics.
Two paths for those that know programming and those that don't.
High School Maths
Basic Python Knowledge
Deep Learning and Machine Learning basics
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 This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore.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!
Overview
Section 1: Python Data Structures: Mastering Lists, Tuples, and Dictionaries
Lecture 1 Mastering Data Structures: From Arrays to Advanced Concepts
Lecture 2 Exploring Tuples: A Powerful Data Structure for Efficient Data Handling
Lecture 3 Data Structure Problem Statement: Tackling Tuple Manipulation for Efficient Data
Section 2: Python Data Structures: Mastering Lists, Tuples, and Dictionaries - 2
Lecture 4 Exploring Data Structure Sets - Unleashing the Power of Collection Uniqueness
Lecture 5 Unleashing the Power of Data Structure Dictionaries: A Comprehensive Guide
Section 3: Python Data Structures: Mastering Lists, Tuples, and Dictionaries - 3
Lecture 6 String Manipulation: Exploring Data Structures and Algorithms for Efficient Text
Lecture 7 Data Structures and Date-Time Handling in Python
Lecture 8 Leveraging Data Structures for Customer Churn Prediction
Section 4: Python - Implementation Of Lambda, Recursion, Functions
Lecture 9 Mastering Lambda Functions in Python: Simplifying Your Code & AI with Examples
Lecture 10 Lambda Expressions: Unleashing the Power of Functional Programming
Section 5: Python - Implementation Of Lambda, Recursion, Functions - 2
Lecture 11 Implementing Functions in Python: From Basics to Advanced
Lecture 12 Implementing Functions in Python: Building the Foundation of Programming
Section 6: Unravelling Recursion: Mastering the Art of Implementing Recursive Functions
Lecture 13 Unravelling Recursion: Mastering the Art of Implementing Recursive Functions
Lecture 14 Mastering Date-Time Feature Engineering: Unlocking Temporal Insights for Machine
Lecture 15 Measuring Model Performance: Key Performance Indicators (KPIs)
Data Scientists who want to apply their knowledge on Real World Case Studies,A computer (Linux/Windows/Mac) with internet connection.,Business Driven people, who are eager to learn how to leverage AI to optimize their Business, maximize profitability and efficiency,Deep Learning practitioners who want to get more Practical Assigmetns
![Data Science Masterclass: Hands-On Ml & Ai Projects]()
What you'll learn
Build a portfolio of work to have on your resume
Developer Environment setup for Data Science and Machine Learning
Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
Real life case studies and projects to understand how things are done in the real world
Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
Requirements
No prior experience is needed (not even Math and Statistics). We start from the very basics.
Two paths for those that know programming and those that don't.
High School Maths
Basic Python Knowledge
Deep Learning and Machine Learning basics
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!
Overview
Section 1: Python Data Structures: Mastering Lists, Tuples, and Dictionaries
Lecture 1 Mastering Data Structures: From Arrays to Advanced Concepts
Lecture 2 Exploring Tuples: A Powerful Data Structure for Efficient Data Handling
Lecture 3 Data Structure Problem Statement: Tackling Tuple Manipulation for Efficient Data
Section 2: Python Data Structures: Mastering Lists, Tuples, and Dictionaries - 2
Lecture 4 Exploring Data Structure Sets - Unleashing the Power of Collection Uniqueness
Lecture 5 Unleashing the Power of Data Structure Dictionaries: A Comprehensive Guide
Section 3: Python Data Structures: Mastering Lists, Tuples, and Dictionaries - 3
Lecture 6 String Manipulation: Exploring Data Structures and Algorithms for Efficient Text
Lecture 7 Data Structures and Date-Time Handling in Python
Lecture 8 Leveraging Data Structures for Customer Churn Prediction
Section 4: Python - Implementation Of Lambda, Recursion, Functions
Lecture 9 Mastering Lambda Functions in Python: Simplifying Your Code & AI with Examples
Lecture 10 Lambda Expressions: Unleashing the Power of Functional Programming
Section 5: Python - Implementation Of Lambda, Recursion, Functions - 2
Lecture 11 Implementing Functions in Python: From Basics to Advanced
Lecture 12 Implementing Functions in Python: Building the Foundation of Programming
Section 6: Unravelling Recursion: Mastering the Art of Implementing Recursive Functions
Lecture 13 Unravelling Recursion: Mastering the Art of Implementing Recursive Functions
Lecture 14 Mastering Date-Time Feature Engineering: Unlocking Temporal Insights for Machine
Lecture 15 Measuring Model Performance: Key Performance Indicators (KPIs)
Data Scientists who want to apply their knowledge on Real World Case Studies,A computer (Linux/Windows/Mac) with internet connection.,Business Driven people, who are eager to learn how to leverage AI to optimize their Business, maximize profitability and efficiency,Deep Learning practitioners who want to get more Practical Assigmetns
