Be A Data Scientist In 2024: Machine Learning With Python

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