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Master ML and DL Algorithms with Datasets : Industry Use Case using Colab

999.00

Discover the Python-based machine learning and deep learning world with our latest colab E Book  Master Python Algorithms with Datasets!  Join our Python-based machine learning and deep learning journey on Google Colab!

  •  Learn Python Basics, Visualization, Machine Learning, and Neural Networks step-by-step.
  •  No complex coding or theory, just practical hands-on experience!
  • Access datasets, including car_sales.csv, health_data.csv, mtcars, Salary_Data, Diabetes, Iris, and more!
  •  Become a pro in data analysis, regression, classification, clustering, and deep learning with Keras and LSTM.

 

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Discover the Python-based machine learning and deep learning world with our latest colab E Book📚

🐍 Master Python Algorithms with Datasets! 📊📈

🔹 Join our Python-based machine learning and deep learning journey on Google Colab! 🚀

🔹 Learn Python Basics, Visualization, Machine Learning, and Neural Networks step-by-step. 📚

🔹 No complex coding or theory, just practical hands-on experience! 💻

🔹 Access datasets, including car_sales.csv, health_data.csv, mtcars, Salary_Data, Diabetes, Iris, and more! 🗃️

🔹 Become a pro in data analysis, regression, classification, clustering, and deep learning with Keras and LSTM. 🧠

🔹 Perfect for beginners in Data Science and Machine Learning!

Topics

🔹 Chapter 1: Basics of Python

🔹 Chapter 2: Pandas for Visualization

🔹 Chapter 3: Seaborn’s Distribution Plots

🔹 Chapter 4: Seaborn’s Categorical Plots

🔹 Chapter 5: Matrix and Grid Plots with Seaborn

🔹 Chapter 6: Seaborn Regression Plots

🔹 Chapter 7: Data Cleaning using mtcars dataset

🔹 Chapter 8: Feature Engineering

🔹 Chapter 9: Feature Selection

🔹 Chapter 10: Simple Regression Using Salary Data

🔹 Chapter 11: Multiple Regression Using mtcars

🔹 Chapter 12: Linear Regression for Forex Trading Prediction on Yahoo Finance

🔹 Chapter 13: Support Vector Machines (SVM) for classification and regression tasks.

🔹 Chapter 14: Logistic Regression with Diabetes Dataset

🔹 Chapter 15: Machine Learning with all Classification Algorithms using Diabetes Dataset

🔹 Chapter 16: KMeans Classifier using Iris Dataset

🔹 Chapter 17: KMeans Classifier & Regressor with mtcars Dataset

🔹 Chapter 18: Dendrogram and Hierarchical Clustering

🔹 Chapter 19: Principal Component Analysis (PCA) using the mtcars Dataset

🔹 Chapter 20: Deep Learning Keras ANN with Diabetes Dataset

🔹 Chapter 21: RNN LSTM-based Forex Trading Prediction on Yahoo Finance

🔹 Chapter 22: CNN-MNIST Dataset Image Classification

📌 No prior coding or statistics knowledge needed – it’s a practical handbook for beginners in Data Science and Machine Learning. Every step comes with simple Python codes and explanations!

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