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Machine Learning with Python
Machine Learning with Python
📑 Lessons : 10 Lesson
🕒 Duration : 3 days
🎚️ Skill level : Beginner
📑 Language : English
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Our Machine Learning with Python course is crafted to provide a solid foundation in machine learning, diving into both theoretical concepts and hands-on application. This course offers a unique blend of Python programming and machine learning techniques, empowering participants to build practical skills and earn a recognized machine learning certification. With a focus on real-world application, learners will explore Python libraries like Scikit-learn, TensorFlow, and Keras, using these tools to build and optimize models. By the end of this online machine learning certificate course, participants will have the confidence to tackle various machine learning challenges and apply their knowledge in professional environments.
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What you'll learn
✔️ Comprehensive Introduction to Machine Learning: Get an in-depth understanding of supervised and unsupervised learning algorithms, exploring their applications across industries.
✔️ Python Programming for Machine Learning: Hands-on practice with Python libraries such as Scikit-learn for building efficient machine learning models.
✔️ Model Evaluation Techniques: Learn to evaluate models using key performance metrics like accuracy, precision, and recall.
✔️ Data Preprocessing and Feature Engineering: Master data preparation techniques essential for real-world machine learning, including feature scaling and encoding.
✔️ Core Algorithms: Dive into regression, classification, and clustering algorithms and understand their implementation in Python.
✔️ Intro to Deep Learning: Gain foundational knowledge in deep learning, working with TensorFlow or Keras to understand the basics of neural networks.
✔️ Capstone Project: Apply your knowledge to an end-to-end machine learning project, showcasing your skills in data preprocessing, model building, and deployment.
Prerequisites
✔️ Basic knowledge of Python programming.
✔️ Familiarity with fundamental concepts in linear algebra and statistics.
Course Content
Module 1: Introduction to Machine Learning and Python (2 hours)
✔️ What is Machine Learning?
✔️ Key concepts: Supervised, Unsupervised, and Reinforcement Learning
✔️ Overview of Python for Machine Learning (libraries: NumPy, pandas, scikit-learn)
✔️ Hands-on: Setting up the environment and writing first Python ML code
Module 2: Data Preprocessing and Feature Engineering (3 hours)
✔️ Data cleaning: Handling missing values, outliers
✔️ Feature scaling and normalization
✔️ Feature selection and extraction
✔️ Hands-on: Preprocessing real-world datasets (data imputation, normalization, etc.)
Module 3: Supervised Learning: Regression (3 hours)
✔️ Linear and Polynomial Regression
✔️ Model evaluation metrics: MSE, R^2, RMSE
✔️ Overfitting and Regularization: Ridge, Lasso
✔️ Hands-on: Implementing linear and polynomial regression using Python
Module 4: Supervised Learning: Classification (2 hours)
✔️ Logistic Regression and Decision Trees
✔️ Support Vector Machines (SVM)
✔️ Evaluation metrics: Confusion Matrix, Precision, Recall, F1-Score
✔️ Hands-on: Implementing classification algorithms with Python
Module 5: Ensemble Learning Techniques (3 hours)
✔️ Bagging, Boosting, and Random Forests
✔️ Gradient Boosting Machines (GBM), XGBoost, and CatBoost
✔️ Hands-on: Improving model performance using ensemble methods
Module 6: Unsupervised Learning: Clustering (2 hours)
✔️ K-Means Clustering and Hierarchical Clustering
✔️ Dimensionality Reduction: PCA and t-SNE
✔️ Hands-on: Clustering techniques for customer segmentation and anomaly detection
Module 7: Model Evaluation and Tuning (2 hours)
✔️ Cross-validation and K-Fold techniques
✔️ Grid Search and Random Search for hyperparameter tuning
✔️ Hands-on: Optimizing models for better performance
Module 8: Neural Networks and Deep Learning Basics (2 hours)
✔️ Introduction to Neural Networks and Backpropagation
✔️ Implementing Neural Networks using Keras and TensorFlow
✔️ Hands-on: Building a simple neural network
Module 9: Working with Real-World Data (2 hours)
✔️ Handling large datasets and working with APIs
✔️ Hands-on: End-to-end project (data collection, training, and deployment)
Module 10: Capstone Project: End-to-End Machine Learning Solution (3 hours)
✔️ Problem statement formulation
✔️ Data preprocessing, model training, and evaluation
✔️ Hands-on: Building, training, and deploying a machine learning solution
Frequently Asked Questions
📍 Question1: What is the format of this Machine Learning with Python course?
Answer: This is a virtual machine learning course with live sessions conducted online. It provides a comprehensive, hands-on experience, allowing you to practice building machine learning models with Python. The course includes interactive sessions, coding exercises, and project-based learning.
📍 Question2: Will I receive a certification after completing this machine learning course?
Answer: Yes, upon successfully completing the course, participants will receive an online machine learning certificate, which is a valuable credential for professionals looking to demonstrate their skills in machine learning and Python programming.
📍 Question3: Are there any projects included in this machine learning certification course?
Answer: Yes, this machine learning course with projects includes a capstone project where you will apply what you've learned to create an end-to-end machine learning solution, working through data preprocessing, model building, evaluation, and deployment.
📍 Question4: Who is this Python programming machine learning course suitable for?
Answer: This course is ideal for beginners who have a basic understanding of Python and want to gain practical skills in machine learning. It’s also beneficial for professionals aiming to transition into data science or enhance their current skill set with a python certificate course online focused on machine learning.
📍 Question5: What tools will be covered in the course?
Answer: The course covers essential Python libraries like Scikit-learn for machine learning, and introduces deep learning frameworks such as TensorFlow and Keras. These tools are widely used in industry and will prepare you to work on real-world machine learning projects.