Part 43. Hyperparameter Tuning in ML: Bayesian Optimization VS Optuna (Which one is more efficient?)

🎬 Welcome to Machine Learning Simplified with Python! 🚀

Are you ready to step into the world of Artificial Intelligence and Machine Learning using Python? Whether you're a student, researcher, or aspiring data scientist, this channel is your one-stop guide to mastering machine learning from the ground up — no prior experience required!

In this series, you'll learn:

📌 What is Machine Learning?
Explore the core concepts, types of learning (Supervised, Unsupervised, Reinforcement), and real-world applications that power industries today.

🛠️ Set Up Your Environment
We'll guide you through installing Anaconda or Miniconda, working with Jupyter Notebooks, and setting up essential libraries like NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.

🐍 Python Essentials for ML
Master the Python skills you need: data types, loops, functions, and powerful tools for data manipulation and wrangling.

📊 Exploratory Data Analysis (EDA)
Learn how to clean, visualize, and understand your data with histograms, heatmaps, scatter plots, and descriptive stats using Pandas, Matplotlib, and Seaborn.

⚙️ Introduction to Scikit-learn
Get hands-on with the most popular ML library — Scikit-learn. Learn how to split data, define features and targets, and build a full ML pipeline.

🤖 Supervised Learning Models
Dive into powerful algorithms like Logistic Regression, k-Nearest Neighbors, and Decision Trees for classification, and Linear & Polynomial Regression for prediction tasks — along with how to evaluate them using accuracy, F1 score, MAE, RMSE, and more.

🧠 Unsupervised Learning Techniques
Discover hidden patterns in your data with K-Means Clustering, Hierarchical Clustering, and Principal Component Analysis (PCA) for dimensionality reduction.

🧪 Model Evaluation & Tuning
Boost your model’s performance with Cross-Validation, GridSearchCV, and learn how to avoid overfitting and underfitting like a pro.

📁 Mini Projects
Apply what you’ve learned to real-world datasets like Titanic, Iris, and House Prices — covering the full ML workflow from data cleaning to model deployment.

🚀 Advanced Topics & Deployment
Take your skills further with an introduction to Deep Learning using Keras, handle time-series data, and learn to deploy models using Streamlit or Flask.

💡 Bonus Content
• Ready-to-use Jupyter Notebooks
• Quizzes and exercises for hands-on practice
• Google Colab support for easy collaboration

🎓 Learn by doing. Build confidence. Apply your skills.

👉 Subscribe and follow along — because this is more than just a course. It's your gateway to becoming a Machine Learning practitioner with Python. Let's get started!

#machinelearning #python #datascience #mlwithpython #artificialintelligence #deeplearning #scikitlearn #dataanalytics #edatutorial #jupyternotebook #kmeans #LinearRegression #polynomialregression #logisticregression #pandas #numpy #seaborn #matplotlib #pythonforbeginners #MLProjects #streamlit #flaskdeployment #googlecolab #mlcourse #learnmachinelearning
#MAPC #AndrewsCurves #RadViz #2DKernelDensityEstimation #interactive3d #QQPlots, #HeatmapwithClustering, #featureranking #Time-SeriesPlot #CustomCompositeVisualization
#naivebayes #decisiontree #svm #stochasticgradientdescent #sgd
#artificialneuralnetwork #ann #bestmodel #ensemble #kmeansclustering
#MeanShiftClustering #hierarchicalclustering #agglomerative #dbscan #apriori #frequentpattern #eclat #principalcomponentanalysis #pca #svd #featureengineering #featureselection #featureextraction #filtermethods #wrappermethods #embeddedmethods
#hyperparametertuning #gridsearch #randomizedsearch #bayesianoptimization #optuna