Be A Data Scientist In 2024 – Machine Learning With Python

Free Download Be A Data Scientist In 2024 – Machine Learning With Python
Published 1/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.38 GB | Duration: 10h 7m
Practical Data Science Skills, Python, Real-World Machine Learning, Predictive Modeling, Project-Based Learning

What you’ll learn
Define the roles of Data Scientist
Model and interpret a complete machine learning project on python
Be able to answer most-asked Data Scientist interview questions
Explain the logic and all the fundamentals about Machine Learning algorithms
Requirements
No Machine Learning experience needed
High school level algebra
Very basic understanding about some programming terms (what is a ‘for loop’, what is ‘if conditions’ etc.)
Description
Welcome to "Be a Data Scientist in 2024: Machine Learning with Python", a comprehensive and beginner-friendly course designed to fast-track your journey into the world of data science. This course is not just about learning theories; it’s about experiencing data science as it is in the real world, guided by expertise akin to that of a senior data scientist.Every session in this course is meticulously crafted to reflect the day-to-day challenges and scenarios faced by professionals in the field. You’ll find yourself diving into the core aspects of machine learning, exploring the practical applications of Python in data analysis, and unraveling the mysteries of predictive modeling. Our approach is unique – it combines detailed video tutorials with guided project work, ensuring that every concept you learn is reinforced through practical application.As you progress through the course, you will develop a solid foundation in Python programming, essential for any aspiring data scientist. We delve deep into data manipulation and visualization, teaching you how to turn raw data into insightful, actionable information. The course also covers critical topics such as statistical analysis, machine learning algorithms, and model evaluation, providing you with a well-rounded skill set.What sets this course apart is its emphasis on real-world application. You will engage in hands-on project work that simulates actual data science tasks. This project-based learning approach not only enhances your understanding of the subject matter but also prepares you for the realities of a data science career.By the end of this 10-hour journey, you will have not only learned the fundamentals of data science and machine learning but also gained the confidence to apply these skills in real-world situations. This course is your first step towards becoming a proficient data scientist, equipped with the knowledge and skills that are highly sought after in today’s tech-driven world.Enroll now in "Be a Data Scientist: Machine Learning on Python in 10 Hours" and embark on a learning adventure that will set you on the path to becoming a successful data scientist in 2024 and beyond!
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Course Structure
Section 2: What is Data Science, Machine Learning and Data Science Project Process ?
Lecture 3 Let’s Begin!
Lecture 4 All about Machine Learning.Let’s make first Machine Learning model without code!
Lecture 5 Data Science Project Process
Section 3: Environment Setup
Lecture 6 Anaconda Installation – Windows
Lecture 7 Anaconda Installation – MacOS
Section 4: Toolkit Intro: Statistics and python pandas, numpy, matplotlib and seaborn Recap
Lecture 8 Basic Statistics Intro
Lecture 9 pandas Intro
Lecture 10 numpy Intro
Lecture 11 matplotlib and seaborn Intro
Section 5: Data Preprocessing with Hands-on Python
Lecture 12 First Glance to Our Dataset
Lecture 13 Reading Data into Python
Lecture 14 Detecting Data Leak and Eliminate the Leakage
Lecture 15 Null Handling
Lecture 16 Encoding
Lecture 17 Feature Engineering on Our Geoghraphical Data
Section 6: Machine Learning Classification Algorithms – All the Logic Behind Them
Lecture 18 Logistic Regression Logic
Lecture 19 Logistic Regression Key Takeaways
Lecture 20 kNN Classifier Logic and Key Takeaways
Lecture 21 Decision Tree Classifier Logic
Lecture 22 Logistic Regression, kNN and Decision Tree Algorithms Wrap-up
Lecture 23 There Are Some Inexpensive Lunches in Machine Learning
Lecture 24 Random Forest Classifier Logic – Bagging Algorithm
Lecture 25 LightGBM Logic – Boosting Algorithm
Lecture 26 XGBoost Logic
Section 7: General Modelling Concepts
Lecture 27 Train Test Split and Overfit-Underfit
Lecture 28 More on Overfit-Underfit Concept
Section 8: Classification Model Evaluation Metrics
Lecture 29 Classification Model Evaluation Metrics
Section 9: Logistic Regression Classifier and kNN Classifier – Hands-on in Python
Lecture 30 Data Recap, Separation and Train Test Split
Lecture 31 Outlier Elimination
Lecture 32 Take a Look at the Test Set Considering Outliers
Lecture 33 Feature Scaling
Lecture 34 Update the Train Labels After Outlier Elimination
Lecture 35 Logistic Regression in Python
Lecture 36 kNN Classifier in Python
Section 10: Decision Tree Classifier and Random Forest Classifier – Hands-on in Python
Lecture 37 Decision Tree Classifier in Python
Lecture 38 Random Forest Classifier in Python
Section 11: LightGBM Classifier and XGBoost Classifier – Hands-on in Python
Lecture 39 LightGBM Classifier in Python
Lecture 40 XGBoost Classifier in Python
Section 12: Classification Model Selection, Feature Importance and Final Delivery
Lecture 41 Classification Model Selection
Lecture 42 Feature Importance Concept
Lecture 43 LightGBM Classifier Feature Importance
Lecture 44 LightGBM Classifier Re-train with Top Features
Lecture 45 Final Prediction for Joined Customers
Section 13: Multi-Class Classification – Hands-on in Python
Lecture 46 MultiClass Classification Explanation
Lecture 47 MultiClass Classification in Python
Section 14: Machine Learning Regression Models – Algorithms and Evaluation
Lecture 48 Regression Introduction
Lecture 49 Linear Regression Logic
Lecture 50 kNN, Decision Tree, Random Forest, LGBM and XGBoost Regressors’ Logic
Lecture 51 Regression Model Evaluation Metrics
Section 15: Regression Models in Python – Hands-on Modelling
Lecture 52 Linear Regression in Python
Lecture 53 LightGBM Regressor in Python
Section 16: Unsupervised Learning – Clustering Logic and Python Implementation
Lecture 54 Unsupervised Learning Logic and Use Cases
Lecture 55 K Means Clustering Logic
Lecture 56 Evaluation of Clustering
Lecture 57 Do the Scaling Before KMeans
Lecture 58 KMeans Clustering in Python
Section 17: You Made It !
Lecture 59 Congratz!
People who are curious about Machine Learning,People who have less than 10 hours to learn about Machine Learning

Homepage

https://www.udemy.com/course/be-a-data-scientist-in-2024-machine-learning-with-python/