PYTHON SKLEARN: KNN, LinearRegression et SUPERVISED LEARNING (20/30)
This French Python tutorial introduces you to SKLEARN, the best package for machine learning with Python. All machine learning models and algorithms have already been implemented with an object-oriented architecture, with each model having its own class. KNN, Linear Regression, Decision Trees, Support Vector Machines, etc. To create a model, we generate an object of the corresponding class. This is called an estimator (in Sklearn). We can also specify the hyperparameters of our model in parentheses. For example, the learning rate of a gradient descent, or the number of trees in a Random Forest. Once we have initialized our model, we can train, evaluate, and use it using three methods found in all Sklearn classes: the Fit, Score, and Predict methods. ► Linear Regression EXAMPLE from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X, y) model.score(X, y) model.predict(X) ► K-Nearest Neighbors EXAMPLE from sklearn.neighbors import KNeighborClassifier model = KNeighborsClassifier() model.fit(X, y) model.score(X, y) model.predict(X) ► VIDEO TIMECODE: 0:00: Intro 01:00: Understanding Machine Learning and Supervised Learning 04:25: SKLEARN, API, and How an Estimator Works 08:27: Regression with SKLEARN (LinearRegression + SVR) 11:47: Classification with SKLEARN (K-Nearest Neighbor) 3:30 PM: Would you have survived the Titanic? + Exercise SKLEARN https://scikit-learn.org/stable/ SKLEARN Algorithm Map https://scikit-learn.org/stable/tutor... ► Support me financially on Tipeee (and get BONUS videos) https://fr.tipeee.com/machine-learnia ► JOIN OUR DISCORD COMMUNITY / discord ► MY WEBSITE: https://www.machinelearnia.com/ ► Get my free book: LEARN MACHINE LEARNING IN ONE WEEK CLICK HERE: https://www.machinelearnia.com/appren... ► Download my codes for free on github: https://github.com/MachineLearnia ► Subscribe: / @machinelearnia ► Who am I? I'm Guillaume Saint-Cirgue, a Senior Data Scientist with over 8 years of experience in the tech, aviation, robotics, energy, and connected factory sectors. In 2019, I founded Machine Learnia to share my knowledge in the field of artificial intelligence. My goal is to explain in detail how machine learning and its algorithms work, while making these concepts accessible to everyone. I firmly believe that skimming over the mathematical aspects of this field is not enough; it's essential to delve deeply into it to stand out. This approach has already convinced more than 150,000 people, and those I train today are among the best in the industry. ► Any questions? Contact me: [email protected]
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