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L'apprentissage automatique avec Python est un domaine passionnant et largement utilisé dans de nombreux secteurs comme la finance, la santé, l'industrie, et bien d'autres. Python, grâce à ses bibliothèques populaires comme scikit-learn, TensorFlow, Keras, PyTorch, et pandas, offre une plateforme robuste et flexible pour développer des modèles d'apprentissage automatique. Voici un aperçu des étapes de base pour commencer l'apprentissage automatique avec Python. Étapes principales d'un projet d'apprentissage automatique Importation des bibliothèques nécessaires D'abord, vous devez importer les bibliothèques de base que vous utiliserez dans votre projet. python Copier Modifier import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, confusion_matrix Chargement des données En général, les données sont stockées sous forme de fichiers CSV, Excel, ou autres formats. Vous pouvez charger ces données à l'aide de pandas. python Copier Modifier data = pd.read_csv('dataset.csv') Exploration des données Une fois que vous avez chargé les données, vous devez explorer et nettoyer les données, par exemple en supprimant les valeurs manquantes, en encodant les variables catégorielles, etc. python Copier Modifier data.info() # Informations générales sur les données data.head() # Afficher les premières lignes du dataset Préparation des données Vous pouvez diviser vos données en variables indépendantes (features) et variables dépendantes (target), puis les diviser en ensembles d'entraînement et de test. python Copier Modifier X = data.drop('target_column', axis=1) # Features y = data['target_column'] # Target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) Standardisation des données La standardisation des données est une étape clé pour de nombreux algorithmes d'apprentissage automatique (comme la régression linéaire, SVM, etc.) afin de mettre toutes les variables sur la même échelle. python Copier Modifier scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) Création et entraînement du modèle Ici, nous utilisons un modèle de régression logistique comme exemple. Cependant, vous pouvez utiliser d'autres modèles comme les arbres de décision, les forêts aléatoires, etc. python Copier Modifier model = LogisticRegression() model.fit(X_train, y_train) Évaluation du modèle Après avoir entraîné votre modèle, vous pouvez évaluer ses performances en utilisant des métriques telles que l'accuracy, la matrice de confusion, etc. python Copier Modifier y_pred = model.predict(X_test) print("Accuracy:", accuracy_score(y_test, y_pred)) print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred)) Exemple complet : Régression logistique pour la classification Voici un exemple complet avec un jeu de données fictif : python Copier Modifier import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, confusion_matrix # Charger les données (ici un jeu de données fictif) data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None) # Nommer les colonnes data.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'] # Diviser les données en features (X) et target (y) X = data.drop('species', axis=1) y = data['species'] # Diviser les données en jeu d'entraînement et test (80% entraînement, 20% test) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Standardisation des données scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Créer un modèle de régression logistique model = LogisticRegression(max_iter=200) # Entraîner le modèle model.fit(X_train, y_train) # Prédictions y_pred = model.predict(X_test) # Évaluer le modèle print("Accuracy:", accuracy_score(y_test, y_pred)) print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred)) Quelques algorithmes d'apprentissage automatique courants : Régression Linéaire Utilisée pour prédire une valeur continue. Exemple : Prédire les prix des maisons. Régression Logistique Utilisée pour les tâches de classification binaire. Exemple : Prédire si un client achètera un produit ou non. Arbres de Décision Utilisés pour la classification ou la régression. Exemple : Prédire la classe d'un client en fonction de ses caractéristiques. Forêts Aléatoires (Random Forest) Un ensemble d'arbres de décision, plus robuste et précis. Exemple : Classification de clients en fonction de divers facteurs. SVM (Support Vector Machines) Utilisé pour la classification, particulièrement dans des cas non linéaires. Exemple : Reconnaissance d'images ou détection de fraude. K-NN (K-Nearest Neighbors) Utilisé pour la classification ou la régression en se basant sur les voisins les plus proches. Exemple : Prédiction du type de maladie d'un patient en fonction des résultats d'autres patients similaires. Clustering (K-Means, DBSCAN) Utilisé pour diviser un ensemble de données en clusters ou groupes similaires. Exemple : Segmentation de clients dans un marché. Ressources pour aller plus loin : Scikit-learn Documentation : https://scikit-learn.org/stable/ TensorFlow Documentation : https://www.tensorflow.org/ Keras Documentation : https://keras.io/ PyTorch Documentation : https://pytorch.org/ Cela te donne un bon point de départ pour travailler sur des projets d'apprentissage automatique avec Python. Si tu as des questions spécifiques ou si tu veux que je t’aide à développer un projet particulier, fais-le moi savoir ! Vous avez dit : Apprentissage automatique avec Python ChatGPT a dit : L'apprentissage automatique (ou machine learning) avec Python est l'un des domaines les plus populaires et largement utilisés dans la science des données. Grâce à des bibliothèques puissantes et bien développées, Python est devenu un choix privilégié pour l'implémentation d'algorithmes d'apprentissage automatique, qu'il s'agisse de classification, régression, clustering ou même d'algorithmes plus avancés comme les réseaux neuronaux. 1. Environnement et bibliothèques nécessaires Voici les bibliothèques Python les plus populaires utilisées dans le domaine de l'apprentissage automatique : pandas : pour la manipulation de données (lecture, nettoyage, transformation). NumPy : pour les calculs numériques et la gestion des matrices. scikit-learn : pour la mise en œuvre des algorithmes d'apprentissage automatique classiques. matplotlib et seaborn : pour la visualisation des données. TensorFlow et PyTorch : pour l'apprentissage profond (deep learning). Keras : une API de haut niveau pour la création et l'entraînement de réseaux neuronaux. Pour commencer, assure-toi d'installer ces bibliothèques : bash Copier Modifier pip install pandas numpy scikit-learn matplotlib seaborn tensorflow 2. Étapes générales d'un projet d'apprentissage automatique Voici les étapes clés que tu suivras dans un projet typique d'apprentissage automatique avec Python : 1. Chargement et exploration des données En premier lieu, tu dois charger et explorer les données pour comprendre leur structure et détecter d'éventuelles anomalies. python Copier Modifier import pandas as pd # Chargement des données depuis un fichier CSV data = pd.read_csv('dataset.csv') # Afficher les premières lignes print(data.head()) # Informations sur les types de données et les valeurs manquantes print(data.info()) 2. Préparation des données Avant de créer un modèle, il faut préparer les données : nettoyer les valeurs manquantes, convertir les variables catégorielles en numériques, normaliser ou standardiser les données, etc. python Copier Modifier # Remplacer les valeurs manquantes par la moyenne (exemple) data.fillna(data.mean(), inplace=True) # Convertir les variables catégorielles en numériques (si nécessaire) data = pd.get_dummies(data, drop_first=True) # Diviser les données en features (X) et target (y) X = data.drop('target_column', axis=1) y = data['target_column'] 3. Séparation des données en ensembles d'entraînement et de test Pour évaluer la performance du modèle, on divise généralement les données en deux ensembles : un ensemble d'entraînement (pour entraîner le modèle) et un ensemble de test (pour évaluer la performance du modèle sur de nouvelles données). python Copier Modifier from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 4. Sélection du modèle et entraînement Ensuite, on sélectionne un algorithme d'apprentissage automatique. Voici un exemple avec un modèle de régression logistique pour une tâche de classification binaire. python Copier Modifier from sklearn.linear_model import LogisticRegression # Créer le modèle model = LogisticRegression() # Entraîner le modèle model.fit(X_train, y_train) 5. Évaluation du modèle Après avoir entraîné le modèle, il est crucial d'évaluer sa performance. Pour cela, on peut utiliser des métriques comme l'accuracy, la matrice de confusion, ou le score F1. python Copier Modifier from sklearn.metrics import accuracy_score, confusion_matrix # Prédictions y_pred = model.predict(X_test) # Évaluation print("Accuracy:", accuracy_score(y_test, y_pred)) print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred)) 6. Amélioration du modèle Une fois un modèle de base implémenté, il faut souvent l'ajuster en optimisant des hyperparamètres (par exemple, en utilisant la recherche sur grille), en ajoutant des caractéristiques pertinentes ou en utilisant des techniques avancées de validation croisée. 3. Exemple d'un projet complet Imaginons que tu veuilles construire un modèle de régression logistique pour prédire si un client va acheter un produit en fonction de certaines caractéristiques. Voici un exemple complet : python Copier Modifier import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, confusion_matrix from sklearn.preprocessing import StandardScaler # Charger les données data = pd.read_csv('customers.csv') # Exploration rapide des données print(data.head()) # Préparation des données (exemple) X = data.drop('buy_product', axis=1) y = data['buy_product'] # Diviser les données en ensembles d'entraînement et de test X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Standardisation des données scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Créer et entraîner le modèle model = LogisticRegression() model.fit(X_train, y_train) # Prédictions y_pred = model.predict(X_test) # Évaluer le modèle print("Accuracy:", accuracy_score(y_test, y_pred)) print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred)) 4. Autres Algorithmes d'Apprentissage Automatique Voici quelques autres algorithmes courants que tu pourrais utiliser en fonction de la tâche que tu souhaites accomplir : Régression linéaire (pour les tâches de régression) Arbres de décision et forêts aléatoires (pour la classification et la régression) K-NN (K-Nearest Neighbors) (pour la classification ou la régression) SVM (Support Vector Machines) (pour la classification) Réseaux neuronaux (pour l'apprentissage profond) 5. Visualisation des Résultats Utiliser matplotlib ou seaborn pour visualiser les résultats et mieux comprendre la performance du modèle. python Copier Modifier import matplotlib.pyplot as plt import seaborn as sns # Tracer la matrice de confusion sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d', cmap='Blues') plt.xlabel('Prédictions') plt.ylabel('Valeurs réelles') plt.title('Matrice de confusion') plt.show() 6. Aller Plus Loin Validation croisée : Pour éviter le surapprentissage, on peut utiliser la validation croisée, une méthode qui divise les données en plusieurs sous-ensembles pour évaluer la performance du modèle de manière plus robuste. python Copier Modifier from sklearn.model_selection import cross_val_score scores = cross_val_score(model, X, y, cv=5) print("Mean cross-validation score: ", scores.mean()) Optimisation des hyperparamètres : Utiliser GridSearchCV ou RandomizedSearchCV pour rechercher les meilleurs paramètres pour ton modèle. python Copier Modifier from sklearn.model_selection import GridSearchCV param_grid = {'C': [0.1, 1, 10], 'solver': ['liblinear', 'saga']} grid_search = GridSearchCV(LogisticRegression(), param_grid, cv=5) grid_search.fit(X_train, y_train) print("Meilleurs paramètres : ", grid_search.best_params_) Ressources Supplémentaires Cours et tutoriels : Coursera: Machine Learning by Andrew Ng Kaggle: Datasets et notebooks Documentation des bibliothèques : Scikit-learn Documentation TensorFlow Documentation PyTorch Documentation Tu peux aussi explorer des projets plus avancés comme l'apprentissage profond (deep learning) avec TensorFlow ou PyTorch, qui sont particulièrement adaptés pour des tâches complexes telles que la vision par ordinateur, le traitement du langage naturel, et la reconnaissance vocale.

The "Machine Learning with Python" course by IBM is a well-structured and beginner-friendly program that introduces core concepts of machine learning in a practical and engaging manner. It covers essential topics like supervised and unsupervised learning, regression, classification, clustering, and neural networks. One of the standout features is its use of real-world datasets and hands-on labs, primarily conducted through Jupyter notebooks, which solidify theoretical concepts with practical implementation. The instructor, Joseph Santarcangelo, explains complex algorithms like KNN, SVM, Decision Trees, and Logistic Regression in a clear and concise manner, making it accessible even to those from non-technical backgrounds. The course also includes visualizations and model evaluation metrics such as accuracy, precision, recall, and F1-score, ensuring that learners understand not just how to build models but also how to assess their performance effectively. Moreover, the course introduces foundational deep learning concepts, offering a smooth transition into more advanced AI topics. It emphasizes Python libraries like Scikit-learn, Pandas, and Matplotlib, which are essential tools in the data science ecosystem. Overall, this course provides an excellent foundation for anyone looking to enter the field of machine learning. Whether you're a student, aspiring data scientist, or software developer, this course helps build the confidence and skills needed to start applying ML in real-life scenarios.

Great course! Get ready to learn, code, debug, sweat, learn some more, fix your code, then finally smile when your ML models work smoothly.

That last statement described my workflow during the final assignment/project of this course.

Quite simply, this course was brilliant because not only did it bring everything we've learned so far together but it also built upon the last course and properly introduced us to Machine Learning and its applications. In his videos, Saeed successfully breaks down complex topics into digestible byte-sized content and ensures that you intuitively understand what is going on.

One of the best pieces of advice I have received in regards to my learning and in life in general is to make sure you have a strong grasp of the fundamentals and these become building blocks to much more complex topics. That in a nutshell is what I believe this course has done for me.

To those who are reading this review, trying to decide whether or not to take this course... just do it! What are you waiting for? No seriously? This might be one of the best decisions you make this year.

If you've been racing through the other courses up to this point, I advise you to slow down once you get here and really try to digest what Saeed has taught here.

Watch the videos, pause, take notes, rewind, continue watching, learn, code. Iterate.

Dear Coursera and IBM,

I am writing to express my deep gratitude for the opportunity to learn new skills and knowledge through your world-class platform. As someone from a rural area in Pakistan, access to quality education can be limited, and I feel truly blessed to have been given the chance to learn on such a reputable platform.

The courses and instructors provided by Coursera and IBM Skills Network have been nothing short of exceptional. I am especially grateful for the professionalism and well-formatted courses, which have allowed me to easily navigate and learn at my own pace.

I believe that the skills and knowledge I have gained through Coursera and IBM Skills Network will be invaluable in my personal and professional life, and I am excited to apply them to make a positive impact in my community.

Once again, I cannot express my appreciation enough for the opportunity to learn through your platform. Thank you for everything that you do.

I love every bit of this course. It is very informative and the explanation by the instructor is second to none. He explained most of the concepts especially using real life scenarios like customer segmentation, detection of cancer and many more. Using these real life examples in the explanation made me understand the course very well and also appreciate machine learning. It will be very easy with anyone with mathematical background though people that are not mathematical inclined may have some difficulties understanding some of the concepts. Nevertheless, going through the lab section will make you understand the concepts very well even if you didn't get all the theoretical concepts. The final project was also centered based on what was taught and easy to follow by anyone that paid apt attention to the lectures and followed duly in the lab exercises. Kudos to the instructor.

The course start with introduction to Machine Learning, with various industrial examples and applications along with libraries used for Machine Learning. Understood how supervised machine learning is different from unsupervised machine learning. Then learnt the concept of Linear, Non-linear, Simple and Multiple regression, and their applications, also how to evaluate your regression model, and calculate its accuracy.  

Practiced with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Introduced with main idea behind recommendation engines, then understood two main types of recommendation engines, namely, content-based and collaborative filtering. The course ends with Peer Graded Assignment to apply all the ML modeling learned.

Thanks to IBM and Coursera for this great learning experience.

I recently enrolled in the Machine Learning with Python course as part of the IBM Data Science Professional Certificate on Coursera, and I couldn’t be more pleased with the experience! The course content is well-structured, providing a clear understanding of machine learning fundamentals along with hands-on practice. The instructors explain complex topics in a simplified way, making it accessible for learners at all levels. I especially appreciate the real-world examples and the use of Python libraries like scikit-learn, which solidified my understanding of various algorithms. The course strikes a great balance between theory and application, allowing me to build and evaluate machine learning models confidently. Whether you're new to machine learning or looking to deepen your knowledge, this course is a fantastic choice!

I am thrilled to offer my comprehensive feedback following the successful completion of the esteemed "Machine Learning with Python" course, thoughtfully curated and presented by IBM on the esteemed platform, Coursera. Throughout this journey, I have been immersed in an exceptional learning experience, artfully blending profound theoretical knowledge with invaluable practical exposure through hands-on exercises. The instructor's profound expertise and lucid explanations have been instrumental in elevating my grasp of intricate Machine Learning concepts and their pragmatic implementation using Python. The interactive and immersive course structure, augmented by compelling real-world projects, has served to fortify my skill set, paving the way for tackling future challenges in this dynamic field.

I personally felt, it is one of the best modules offered as part of certification program. Data science has large number of algorithms, so naturally it is difficult to cover most of them and more importantly it is difficult to decide where to start from. Module is well designed, and it has provided basic to intermediate knowledge of most of machine learning algorithms, must to know for beginners. Few minutes introductory video on any given algorithm, followed an hour-long lab practice is really helped to understand algorithm and it’s implementation using python. Provided structured course really helped me to perform machine learning implementation using python. Great content to spent time on!

Machine Learning with Python is a comprehensive guided path into the insights of Machine Learning. Machine Learning, a term of relatively recent appearance, it is actually utilizing mathematical analysis that dates even centuries ago. Linear regression, logistic regression, are based on mathematical theories with a long history. This particular course, introduce the subject in a clear and comprehensive way suited for all audience with little background on this mathematical theorems. And for those who had the knowledge from previous academic courses of practical experience, the course in very helpful as it allows to refresh the concept in a dynamic and easy to follow pace, in my opinion.

The best way to succeed in this course is to when doing the labs, write down with "hand" every line of code on a separate place, though, you will not understand most of it, just keep going. And then type it on Jupyter notebook from "hand written notes". This process might seem hard effort or seems like no learning is there but trust me this process will get you break the thick wall of Machine Learning and python code. The rest will follow. After following the process, I feel very familiar with code, machine learning algorithms and terminologies which I guess is big achievement. I also believe ISLR can help later in understanding these algos and set up more solid foundation.

Certainly a great course, clear voice and visuals in which the concepts have been explained clearly with rich details. I have noticed many are complaining about the math, lab, coding and the conceptual explanations; so here is a reminder than the course strongly suggested a 'background in Python programing language' in the beginning. Additionally, this is an 'intermediate/ advanced' course for engineers and data scientists, so a well-established knowledge in math should've been already acquired by default, even though the math needed here is very basic and can be done automatically. Also, understanding the conceptual part is very important to perform tasks correctly.

Taking the machine learning course was an incredibly inspiring and educational experience for me! The training material was structured and easy to digest, allowing me to deepen my knowledge in this fascinating area. I especially appreciated the hands-on assignments, which helped me put what I learned into practice and confidently tackle real-world machine learning challenges. The course also provided excellent resources for self-study and further development. Through this course, I gained a valuable set of skills and confidence in my machine learning abilities. Many thanks to the course team for their thorough preparation and professionalism!

The Machine Learning with Python course provides a solid foundation in the basics of machine learning, with a focus on practical implementation using Python. It covers key concepts like supervised and unsupervised learning, regression, classification, and clustering, alongside popular libraries such as Scikit-learn, Pandas, and Matplotlib. The course is beginner-friendly, making it accessible for those new to machine learning. However, some may find the theoretical depth limited, so it's best for hands-on learners seeking an applied approach to machine learning rather than a deep dive into the mathematics behind algorithms.

I am thoroughly enjoying the course. The codes written are the shortest possible codes but the narrations are just fabulous to comprehend and remember. I need more practice to write the codes correctly by my own but my fundas are all cleared and I know exactly why am I doing the next step. Having worked my way through the IBM Data Science courses, this one was the "pay off" - it was so cool to finally apply more sophisticated techniques to real world data sets. The labs were fantastic. Highly recommend this course to anyone interested in learning about the most popular machine learning algorithms.

This is a Perfect course, except for the name of the course. It is one of the perfect courses for those who wanted to become familiar with different machine learning algorithms (different classification algorithms, as well as different clustering algorithms). In fact, it is the course I definitely recommend for those who want to start machine learning. By the way, I did not understand why the author used this title for this awesome course, given that he is not used Python programming. The best title might be this one, I guess:

"Different machine learning problems, and algorithms "

It's very basic but essential to understand more complex topics regarding machine learning. The Course is well-structured with good presentations. The only issue that I've encountered is with the IBM Watson Studio. For some reason, it just refuses to accept my credit card (which I'm currently using to pay for my other stuff) and boots me off the website every time. I create a GitHub repository for the final project and, fortunately, people have no problem reviewing my notebook on GitHub which allowed me to get around with the issue with IBM Watson Studio.

This course is perfect for beginners. It gives a basic idea about clustering, regression, decision tree, recommender system, classification algorithms along with Labs. You should know a little bit about Python programming and few libraries like NumPy, pandas, sciPy, and sci-kit learn. The Labs are great because you will be using the concepts learnt in the video lectures on the sample datasets and when you see the results, it will motivate you to go for some hands-on projects from Coursera Rhyme Project Network and it will be beneficial for you.

I recently completed the "Machine Learning with Python" online course and I highly recommend it. The course provides a comprehensive introduction to machine learning using Python. The content is well-structured, the explanations are clear, and the practical exercises enhance understanding. The instructor is knowledgeable and engaging. The emphasis on practical implementation and model evaluation is a major plus. The course is user-friendly and allows for flexible learning. Overall, a fantastic course for anyone interested in machine learning!

This course is an excellent platform to understand the basics of Machine Learning with python. The lab tools pioneer a way to understand the code and implement it. The videos are crisp and clearly mention the scope of the course which creates a curiosity to know more. However, the peer graded assignment is not an efficient way as 'sample notebook" paves the way to plagiarism. The peer grading also restricts the user creativity to write a simpler code as it may not be understood by other peers. Overall I am very happy with the course

I absolutely loved the "Machine Learning with Python" course by IBM! The practical labs and concise, explanatory videos were a real highlight, covering a range of supervised and unsupervised learning algorithms, including regression, classification, and clustering. Learning metrics such as accuracy, precision, recall, F1 score, MSE, RMSE, MAE, and log loss, as well as gradient descent and cost function explanations, was particularly helpful. I also appreciate the quizzes, which helped me to check my knowledge.

Highly recommended!

Excellent content and relatable use cases. As a beginner in data science with no formal programming, the information is presented in a way to help you understand the fundamentals and then apply them using the pre-built python packages that are widely available. I started with data science 3 years ago and it was very difficult to get started without any programming or statistics background. This course does a tremendous job of making it accessible, understandable and quite frankly a lot fun in the process.

I wanted to thank you both for the wonderful Machine Learning with Python course you have created. I found the course very informative and thought-provoking. I learned a great deal about applying machine learning techniques to data science projects. I really appreciate the way you presented the course and the real-life examples.

Thank you for giving me the opportunity to expand my data science skills. It was a pleasure taking your course and I look forward to learning more from you both in the future.

This course is fantastic, It has adequate amount of theory supplemented by labs. I also like the Watson Studio, and the fact that you actually learn to use some industry level tools in this course really takes the icing on top. The staff is supportive and wonderful, the community and cohorts are great. Overall I would happily recommend anyone who has absolutely no knowledge about Data Science to start right here with this course. Really enjoyed and thank you IBM for you digital badge. :-)