Master Machine Learning Essentials: Learn Key Algorithms & Build Real-World Applications

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Unleash the power of Artificial Intelligence (AI) with this practical, hands-on course. Dive into the core machine learning algorithms driving innovation in every industry. Gain in-demand skills as you learn to design, build, and deploy a real-world application that showcases your mastery.

Limited spots available – don't miss out!

Key Features For This Course:

📈 Confidently understand essential ML algorithms: linear regression, decision trees, neural networks, and more.
🚀 Transform raw data into actionable insights with practical, step-by-step guidance.
🔮 Build a portfolio-ready project that demonstrates your proficiency in real-world ML applications.
🔥 Prepare for a thriving career in data science, machine learning engineering, or AI development.

Ideal For:

💡 Aspiring data scientists and AI professionals
💪 Professionals seeking to enhance their knowledge of AI.

🚨 Launch Your Data Science Career with LunarTech.AI! 🚨

🎁 Free Resources:

🖥️ Resources and Courses to get into Machine Learning

👤 Meet Your Instructor: Vahe Karen Aslanyan

🔔 Connect with Us:

⭐️ Contents ⭐️

⌨️ (00:00:00) - Introduction
⌨️ (00:01:28) - Linear Regression
⌨️ (00:10:50) - Logistic Regression
⌨️ (00:19:17) - Linear Discriminant Analysis
⌨️ (00:27:34) - Logistic Regression vs LDA
⌨️ (00:35:05) - Naive Bayes
⌨️ (00:43:51) - Naive Bayes vs Logistic Regression
⌨️ (00:50:22) - Decision Trees
⌨️ (01:00:44) - Bagging
⌨️ (01:09:46) - Random Forest
⌨️ (01:21:07) - Boosting Or Ensamble Techniques
⌨️ (01:22:59) - AdaBoost
⌨️ (01:31:29) - Gradient Boosting Machines (GBM)
⌨️ (01:39:16) - Extreme Gradient Boosting (XGBoost)
⌨️ (01:44:27) - Adaboost vs GBM vs XGBoost
⌨️ (01:47:03) - Introduction to the course and overview of the movie recommendation system project.
⌨️ (01:53:04) - Detailed look into feature selection, tokenization, and introduction to cosine similarity.
⌨️ (01:59:05) - Discussion on building a web app using Streamlit to showcase the project.
(02:05:06) - Exploration of Python libraries and tools used for data manipulation and analysis.
⌨️ (02:11:07) - In-depth explanation of the movie dataset utilized and features important for the recommendation system.
⌨️ (02:17:08) - Practical demonstration of the recommendation system and explanation of user experience improvements.
⌨️ (02:23:09) - Comparative analysis of content-based versus collaborative filtering.

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