Real World Machine Learning Project In Python From Scratch

Complete Real World Machine Learning Project In Python From Scratch

What you'll learn
Gain insights into the principles and applications of machine learning in real-world scenarios across various domains.
Learn how to choose a machine learning project, define clear goals, and understand the business or problem context.
Dive into feature engineering to enhance model performance by selecting, transforming, and creating relevant features.
Learn how to build a predictive system, integrate your machine learning model, and deploy it for making real-world predictions.

Requirements
Basic understanding of Python programming.
Access to a computer with Python installed.

Description
Course Title: Real World Machine Learning Project in Python From ScratchCourse Description:Welcome to the Real World Machine Learning Project in Python From Scratch course, an immersive experience that takes you through the entire lifecycle of building a practical machine learning project. Whether you're a novice curious about the end-to-end process or an intermediate learner eager to enhance your skills, this course is crafted to guide you through the complexities of real-world machine learning projects using Python.What You Will Learn:Introduction to Real-World Machine Learning:Delve into the principles and applications of machine learning in real-world scenarios, exploring its diverse applications across industries.Selecting a Project and Defining Goals:Learn how to choose a machine learning project, define clear goals, and understand the business or problem context for effective project planning.Data Collection and Exploration:Master techniques for collecting and preparing data, performing exploratory data analysis (EDA) to extract valuable insights essential for project success.Data Preprocessing and Cleaning:Understand the significance of data preprocessing and cleaning, and implement strategies to handle missing values, outliers, and other data anomalies.Feature Engineering:Dive into the world of feature engineering, enhancing model performance by selecting, transforming, and creating relevant features to drive better predictions.Choosing and Implementing Machine Learning Algorithms:Explore a variety of machine learning algorithms, gain the skills to select the most suitable ones for your project, and implement them using Python.Model Training and Evaluation:Grasp the process of training machine learning models, optimize hyperparameters, and evaluate model performance using industry-standard metrics.Hyperparameter Tuning and Model Optimization:Dive deep into hyperparameter tuning techniques and optimization strategies, ensuring your models are fine-tuned for efficiency and accuracy.Building a Predictive System:Learn the steps to build a predictive system, integrating your machine learning model and deploying it for making real-world predictions.Monitoring and Maintaining Models:Understand the importance of monitoring and maintaining machine learning models to ensure ongoing relevance and accuracy in dynamic environments.Ethical Considerations and Best Practices:Engage in meaningful discussions about ethical considerations in machine learning projects and adhere to best practices for responsible development.Why Enroll:Hands-On Project: Engage in a comprehensive hands-on project to reinforce your learning through practical application.Real-World Applications: Acquire skills applicable to real-world scenarios, enhancing your ability to create effective machine learning solutions.Community Support: Join a community of learners, share experiences, and seek assistance from instructors and peers throughout your learning journey.Embark on this practical learning adventure and become proficient in building a Real World Machine Learning Project in Python From Scratch. Enroll now and gain the skills to create impactful machine learning solutions!

Overview
Section 1: MACHINE LEARNING PROJECT ONE

Lecture 1 INTRO TO FACIAL RECOGNITION USING TENSORFLOW PROJECT

Lecture 2 DATASET CREATION FOR FACIAL RECOGNITION

Lecture 3 TRAINING THE DATASET

Lecture 4 VALIDTATE THE TRAINED MODEL

Lecture 5 DOWNLOAD THE MODEL AND ITS FEATURES

Lecture 6 EXTRACT THE MODEL AND ITS LABELS

Lecture 7 INSTALL PACKAGE IN PYCHARM IDE

Lecture 8 EXECUTE THE PROJECT IN PYCHARM IDE

Section 2: INTRODUCTION TO SECOND PROJECT IN MACHINE LEARNING

Lecture 9 INTRODUCTION TO OBJECT RECOGNITION AND ITS FEATURES

Lecture 10 DATASET CREATION FOR PROJECT

Lecture 11 TRAIN THE DATASET

Lecture 12 VALIDATE THE TRAINED MODEL

Lecture 13 DOWNLOAD MODEL

Lecture 14 EXTRACT THE MODEL AND ITS FILES

Lecture 15 PROJECT CODE EXPLANATION

Lecture 16 PROJECT EXECUTE AND OUTPUT

Beginners interested in understanding the end-to-end process of a machine learning project.,Intermediate learners seeking practical experience in building real-world machine learning systems.,Professionals aiming to apply machine learning in their work or projects.

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