Fast-Track Machine Learning In Python & Chatgpt

Hands-on Machine Learning Tutorial with Pandas, Numpy, Seaborn, Scikit-learn in Python and ChatGPT: A Complete Work-flow

What you'll learn

Learn to proficiently use Python for various machine learning tasks, including data cleaning, manipulation, preprocessing, and model development.

Gain expertise in building and implementing supervised machine learning models: Regressions, Random Forest, Decision Tree, SVM, XGBoost, and KNN, etc.

Acquire skills in unsupervised machine learning techniques, including KMeans for effective cluster analysis and pattern recognition.

Learn to create a streamlined and efficient workflow for building machine learning models from scratch, incorporating both Python and ChatGPT.

Develop the ability to measure and evaluate the accuracy and performance of machine learning models, enabling decisions on model selection and optimization.

Explore the integration of ChatGPT into the machine learning workflow, leveraging its capabilities for enhanced data analysis, and generating insights.

Understand strategies for selecting the most suitable machine learning model for a given task, considering factors such as accuracy, and scalability.

Apply acquired knowledge to real-world scenarios, solving diverse machine learning challenges and developing solutions.

Requirements

No coding Experience is Needed.

Desktop/Laptop

Description

Unlock the fast track to machine learning mastery with our comprehensive course, "Fast-Track Machine Learning in Python & ChatGPT." Dive deep into hands-on tutorials utilizing essential tools like Pandas, Numpy, Seaborn, Scikit-learn, Python, and the innovative capabilities of ChatGPT. This course is designed to guide you seamlessly through every stage of the machine learning process, ensuring a complete workflow that empowers you to tackle tasks such as data cleaning, manipulation, preprocessing, and the development of powerful supervised and unsupervised machine learning models.In this immersive learning experience, gain proficiency in crafting supervised models, including Linear Regression, Logistic Regression, Random Forests, Decision Trees, SVM, XGBoost, and KNN. Unleash the power of unsupervised models like KMeans and DBSCAN for cluster analysis. The course is strategically structured to enable you to navigate through these complex concepts swiftly, effortlessly, and with precision.Our primary objective is to equip you with the skills to build machine learning models from scratch, leveraging the combined strength of Python and ChatGPT. You will not only learn the theoretical foundations but also engage in practical exercises that solidify your understanding. By the end of the course, you'll have the expertise to measure the accuracy and performance of your machine learning models, enabling you to make informed decisions and select the best models for your specific use case.Whether you are a beginner eager to enter the world of machine learning or an experienced professional looking to enhance your skill set, this course caters to all levels of expertise. Join us on this learning journey, where efficiency meets excellence, and emerge with the confidence to tackle real-world machine learning challenges head-on. Fast-track your way to becoming a proficient machine learning practitioner with our dynamic and comprehensive course.

Overview

Section 1: Setting Up Your Data Analysis Platform

Lecture 1 Install Python and Jupyter Notebook

Lecture 2 Setting Up ChatGPT for Easy Machine Learning

Section 2: What is Machine Learning?

Lecture 3 Machine Learning and Its Characteristics

Lecture 4 Complete Machine Learning Work-flow

Lecture 5 Practice datasets

Lecture 6 Instructions for Quizzes: IMPORTANT

Section 3: Master Data Cleaning for Error-free ML Model

Lecture 7 Load your dataset into Python environment

Lecture 8 Handling missing values with Scikit-learn

Lecture 9 Identify and deal with inconsistent data

Lecture 10 Dealing with miss-identified data types

Lecture 11 Address and remove duplicated data

Lecture 12 Solution 1: Data Cleaning

Section 4: Master Data Manipulation for Strong ML Model

Lecture 13 Sorting and arranging dataset

Lecture 14 Filter data based on conditions

Lecture 15 Merging or adding of supplementary variables

Lecture 16 Concatenating or adding of supplementary data

Lecture 17 Solution 2: Data Manipulation

Section 5: Master Data Preprocessing for Perfect ML Model

Lecture 18 Feature engineering: Generating new data

Lecture 19 Extracting day, months, year from date variable

Lecture 20 Feature encoding: Assigning numeric values

Lecture 21 Creating dummy variables for nominal data

Lecture 22 Data standardizing and normalizing with StandardScaler

Lecture 23 Splitting data into training and testing set

Lecture 24 Solution 3: Data Preprocessing

Section 6: Hands-on Machine Learning Application Part 1: Regression

Lecture 25 **Read It: IMPORTANT**

Lecture 26 Linear regression ML model

Lecture 27 Decision Tree regression ML model

Lecture 28 Random Forest regression ML model

Lecture 29 Support Vector regression ML model

Lecture 30 XGBoost regression ML model

Lecture 31 Solution 4: ML Model Application Part 1

Section 7: Hands-on Machine Learning Application Part 2: Classification

Lecture 32 **Read It: IMPORTANT**

Lecture 33 Logistic Regression ML model

Lecture 34 Decision Tree classification ML model

Lecture 35 Random Forest classification ML model

Lecture 36 K Nearest Neighbours classification ML model

Lecture 37 LightGBM classification ML model

Lecture 38 Solution 5: ML Model Application Part 2

Section 8: Hands-on Machine Learning Application Part 3: Clustering

Lecture 39 KMeans Clustering ML model

Lecture 40 Final Solution: Fast-Track ML in Python & ChatGPT

Section 9: Tips, Tricks and Resources

Lecture 41 ChatGPT: Your best code companion

Lecture 42 Course resources

Python Enthusiasts,Data Science Aspirants,Complete Beginners