From Python to Predictions: Build ML Models Step by Step

从Python到预测：零基础构建ML模型

本课程从零开始，手把手教你用Python构建回归、分类、聚类及深度学习模型（ANN/CNN/RNN），涵盖股票预测、贷款审批等实际案例，无需ML经验。

Published 10/2025
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 47m | Size: 2.03 GB

“Learn to build regression, classification, clustering, ANN, CNN, and RNN models step by step, even as a beginner.”

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.

Description
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.