How Does Machine Learning Work? ML Model Study - Machine Learning Tutorial in Bangla Using Python -4
Welcome to our comprehensive tutorial, "How Does Machine Learning Work?" This is the fourth installment of our engaging Machine Learning Tutorial series in Bangla, where we'll delve into the fascinating world of ML Model Study.
In this tutorial, we will take a closer look at the functioning of machine learning algorithms. We'll explore how these intelligent algorithms learn from data and make predictions or decisions. Our step-by-step guide will walk you through the core concepts of ML model evaluation, tuning, and fine-tuning, helping you gain a deeper understanding of the model's behavior.
Throughout the tutorial, we'll use Python as our programming language, making it accessible for learners of all levels. Practical examples and hands-on demonstrations will be provided to solidify your knowledge and reinforce the concepts covered.
Whether you're a beginner or an experienced practitioner, this video is designed to enrich your machine learning journey and equip you with valuable insights. Join us in this captivating learning experience and unlock the secrets of machine learning!
Don't miss this opportunity to expand your knowledge and master the art of machine learning. Hit that play button now and let's embark on an exciting ML Model Study together!
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Full Playlist Link: • Machine Learning Tutorial in Bangla Using ...
Chapters and Key Moments:
0:00 - Intro.
0:18 - Basic Discussion About How Does Machine Learning Work?
1:15 - Explain with an Example (Salary Prediction)
2:56 - What is Machine Learning Model.
4:58 - Detailed Analysis of Machine Learning Model. How to Build ML Model?
5:37 - Data Collection to Build Machine Learning Model.
7:18 - Data Pre-Processing, Data Splitting, Choosing Algorithm.
8:25 - ML Model Training, Evaluation, Adjusting, Model Deployment, Continuous Learning.
10:37 - Applying Our ML Model to System.
11:24 - Last Words.
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Topics: Introduction to Machine Learning, Python and Libraries for ML, Data Preprocessing and Exploration, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Neural Networks and Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transfer Learning and Fine-Tuning, Reinforcement Learning, Model Evaluation and Validation, Deployment and Practical Implication, Natural Language Processing (NLP), Time Series Analysis and Forecasting, Recommender Systems, Generative Adversarial Networks, Explainable AI, Big Data and Distributed ML, Advanced Topics in Deep Learning, At Least 5 Projects with Web App and so on...
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