Machine Learning Prediction Model From Scratch For Everyone

From Scratch (Data Collection to Apply Algorithms) learn to Develop Naïve Bayes | Random Forest | SVM and Much More

What you'll learn

Machine learning fundamentals: supervised, unsupervised, reinforcement learning, and practical applications.

Master data preprocessing: handle missing data, scale features, and transform datasets for ML.

Explore various ML algorithms, understanding when and how to use them effectively

Evaluate models with metrics like accuracy, precision, recall, and understand the importance of cross-validation.

Develop coding skills from scratch, understanding the inner workings of ML models.

Engage in hands-on projects, building a portfolio demonstrating practical ML expertise.

And Finally publish Research Articles or do project with own understanding

Requirements

No prerequisites required. You will learn everything you need to know.

Description

Are you eager to unlock the powerful world of machine learning and build predictive models from the ground up? Look no further!  "Machine Learning Prediction Model from Scratch" online course is designed to provide you with a comprehensive introduction to the fundamentals of machine learning and guide you through the process of creating prediction models entirely from scratch.In this hands-on course, you will embark on an exciting journey into the heart of machine learning, where you will gain a deep understanding of the underlying concepts, algorithms, and methodologies. Whether you are a beginner with no prior experience or an experienced data enthusiast looking to expand your skills, this course will empower you to construct prediction models with confidence.Key Course Highlights:Fundamentals of Machine Learning: You will start by building a strong foundation in machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning.Data Collection: You will learn how to create forms and circulate them to collect real data. In this project, you learn about data collection hands-on. Data Preprocessing: Learn how to prepare and clean datasets, a crucial step in any machine learning project. You'll explore techniques for handling missing data, feature scaling, and data transformation.Algorithm Selection: Dive into the world of machine learning algorithms and understand their strengths and weaknesses. You'll gain hands-on experience with popular algorithms like linear regression, decision trees, support vector machines, and more.Model Evaluation: Discover how to assess the performance of your prediction models using various evaluation metrics. Real-World Applications: Apply your newly acquired knowledge to real-world problems and datasets, gaining practical experience in building machine learning solutions.Coding from Scratch: Unlike some courses that rely heavily on pre-built libraries, this course encourages you to code your machine learning models from scratch, providing you with a deep understanding of how they work.Hands-On Projects: Work on hands-on projects and assignments that challenge you to build prediction models for various applications, reinforcing your skills and boosting your portfolio.By the end of this course, you will not only be proficient in creating machine learning prediction models from scratch but also have the confidence to tackle complex data-driven challenges. Join us on this exciting journey into the world of machine learning and embark on a path to becoming a proficient machine learning practitioner.Don't miss this opportunity to gain the skills and knowledge you need to excel in the ever-evolving field of machine learning. Enroll today and start building predictive models that make a difference!

Overview

Section 1: Basics Of Machine Learning

Lecture 1 Overview of the course and Resource Materials

Lecture 2 Introduction

Lecture 3 Types of Machine Learning

Section 2: Data Collection and Preprocessing

Lecture 4 Designing Form for Data Collection

Lecture 5 Data Preprocessing

Lecture 6 Data Preprocessing and preparation

Section 3: Prediction Model Using Python Programming Language from Scratch

Lecture 7 Introduction to Google Collab and Python

Lecture 8 Importing Dataset and Preparation at Google Colab

Lecture 9 Data Analysis with Python Libraries

Lecture 10 Prediction Model: Naive Bayes and Random Forest

Lecture 11 Prediction Model: SVM with Confusion Matrix

Lecture 12 Testing Model with Random Data

Section 4: What's NEXT

Lecture 13 Congratulations and Future Direction

This course is suitable for beginners looking to enter the world of machine learning.,Researcher who want to publish research article or project,Students from any background technical or non technical,Anyone Interested in Machine Learning,Developers interested in building predictive models will gain valuable insights.,Data analysts seeking to expand their skill set will find it beneficial.,Students aspiring to learn machine learning from scratch will benefit greatly.