Master Linear Regression in Python: A Hindi Machine Learning Course

What you will learn:

Real-world problem-solving using Linear Regression
Data analysis using Univariate and Bivariate techniques
Predictive modeling with the simplest Machine Learning algorithm
Converting business problems into Linear Regression problems

Description

Are you ready to unlock the power of predictive modeling? This comprehensive Hindi course on Linear Regression will equip you with the skills to analyze data, build accurate models, and solve real-world business problems using Python.

This isn't just another theoretical course. We'll guide you through every step, from understanding basic statistical concepts to mastering advanced regression techniques. You'll learn how to prepare data, build both simple and multiple linear regression models, interpret results, and assess model accuracy. Our practical approach ensures you gain hands-on experience, building confidence in your ability to apply these skills immediately.

What you'll achieve:

Grasp the core principles of linear regression and its applications.
Master data preprocessing techniques for accurate modeling.
Build and evaluate simple and multiple linear regression models in Python.
Interpret model results with confidence to make data-driven decisions.
Solve real-world business problems using machine learning techniques.

This course includes downloadable practice files, quizzes, assignments, and a verifiable Certificate of Completion. Taught by experienced analytics consultants, Abhishek and Pukhraj, this course blends theoretical knowledge with practical applications, providing you with a solid foundation in machine learning.

Why choose our course? We focus on the entire process, from initial data understanding to result interpretation—something most courses miss. We'll help you identify suitable business problems for this technique and guide you through the entire analytical process.

Enroll now and start your journey to becoming a data-driven decision-maker!

Curriculum

Introduction

This introductory section sets the stage for your machine learning journey. The "Welcome to the course!" lecture introduces the course structure and objectives. The "Course contents" lecture provides a detailed overview of what you'll learn, and "Course Resources" gives you a quick tour of the materials provided.

Setting up Python and Jupyter Notebook

This section equips you with the essential tools for your machine learning projects. You'll learn to set up Python and Jupyter Notebook using Google Colab, master fundamental Python concepts such as arithmetic operators, strings, and indexing, and gain proficiency in using essential libraries such as NumPy, Pandas, and Seaborn for data manipulation and visualization. The included quizzes will reinforce your understanding of these core concepts.

Basics of Statistics

This section covers the fundamental statistical concepts necessary for data analysis and model building. You'll learn about different types of data and statistics, how to represent data graphically, and how to calculate measures of central tendency (mean, median, mode) and dispersion (range, standard deviation). The final quiz assesses your understanding of these core statistical principles.

Introduction to Machine Learning

This section provides a foundational understanding of machine learning. You'll learn the definition of machine learning, key terminology, illustrative examples, and the steps involved in constructing a machine learning model. This module forms the bridge between statistical concepts and practical machine learning applications.

Data Preprocessing

Data preprocessing is crucial for building accurate machine learning models. This section walks you through the process, starting with gathering business knowledge and data exploration (univariate and bivariate analysis). You'll learn to handle outliers, missing values, and seasonality in your data, as well as perform variable transformations and create dummy variables for categorical data. A final quiz helps you check your grasp of these essential data preparation skills.

Linear Regression

This section dives into the core of the course, covering simple and multiple linear regression. You'll learn the underlying theory, how to assess model accuracy (RSE, R-squared, F-statistic), and interpret results, including those involving categorical variables. You'll gain hands-on experience building models in Python using techniques like test-train split and address issues such as bias-variance trade-off and heteroscedasticity. The section concludes with an exploration of alternative regression methods like Ridge and Lasso and a quiz to test your knowledge. A bonus lecture on comprehensive interview questions for your job search will help strengthen your skills and confidence.

Congratulations & About your certificate

This final section celebrates your accomplishment and provides details about your Certificate of Completion. A bonus lecture will be provided for the successful completion of the course.

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