从Python到预测：逐步构建机器学习模型

这是一门名为《从Python到预测：逐步构建机器学习模型》的入门实践课程，专为觉得机器学习数学理论复杂、术语难懂的初学者设计。课程采用“边做边学”的友好方式，从Python基础、NumPy、Pandas和数据可视化讲起，逐步带学员亲手搭建回归、分类、聚类等核心模型，并深入学习人工神经网络（ANN）、卷积神经网络（CNN）和循环神经网络（RNN）等深度学习模型。通过股票预测、贷款审批、图像识别、情感分析等多个实际项目，学员不仅能用Keras和TensorFlow创建模型并进行评估，还能积累一个可展示的项目作品集，为面试或个人工作打下扎实基础。

MP4 | 视频：h264，1280×720 | 音频：AAC，44.1 KHz，2 Ch
语言：英语 | 时长：5小时47分钟 | 大小：2.03 GB

From Python to Predictions: Build ML Models Step by Step.“Learn to build regression, classification, clustering, ANN, CNN, and RNN models step by step, even as a beginner.” From Python to Prediction: Build Machine Learning Models Step by StepHave you ever wanted to learn Machine Learning but felt overwhelmed by too much math, confusing jargon, or endless theory? You’re not alone — and that’s exactly why I created this course.In From Python to Prediction, we take a practical, hands-on approach to Machine Learning. Instead of drowning in formulas, you’ll actually build models step by step in Python and understand what’s happening as we go. My teaching style is simple: think of this course as learning with a friend, not a professor. Every concept is explained in plain language, every keyword is broken down, and every step is backed up with code you can run.We’ll begin with Python basics for Machine Learning, including variables, functions, NumPy, Pandas, and data visualization. Then we’ll move into building core models like Regression, Classification, and Clustering. Once you’re comfortable, we’ll dive into Deep Learning, where you’ll learn Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) using Keras and TensorFlow.But this isn’t just theory. You’ll apply everything to real-world projects: stock price prediction, loan approval classification, crypto clustering, car purchase predictions, image recognition, and even sentiment analysis on movie reviews.By the end of this course, you’ll not only understand Machine Learning but also have a portfolio of projects to showcase in interviews, internships, or personal work.So if you’re ready to start your journey into Machine Learning — let’s go from Python to Prediction. What you’ll learn Understand Python basics for data science (variables, functions, NumPy, Pandas, visualization). Create Deep Learning models: ANN, CNN, and RNN from scratch with Keras/TensorFlow. Apply ML to real-world datasets (stock prediction, loan approval, crypto clustering, sentiment analysis). Evaluate models using accuracy, confusion matrix, RMSE, and visualize results. Requirements No prior ML experience required. Basic Python knowledge is helpful but not mandatory. Curiosity to learn and explore machine learning.