Welcome to the E-Course: machine learning with python

5
★★★★★
1 reviews
₹58.27
Price
Flexible Schedule
Learn at your own pace

Skills you'll gain

**Advanced Python**
**Machine Learning Techniques**
**Data Science Applications**
**Practical Implementation**
**Expert-Level Training**
**AI and Automation**
**Hands-On Projects**

See how employees at top companies are mastering in-demand skills

machine learning with python

Unlock the power of Python in our comprehensive eCourse, "Python-Powered Machine Learning: From Theory to Practice for Experts." Designed for seasoned professionals, this course bridges the gap between theoretical concepts and practical applications. Dive deep into advanced machine learning techniques, explore real-world case studies, and enhance your coding skills with hands-on projects. Whether you're looking to refine your expertise or stay ahead in the rapidly evolving tech landscape, this course equips you with the tools and knowledge to excel. Join us and transform your understanding of machine learning today!

Explore more from Learning English

English for Research
English for Research Paper Writing
University of California, Irvine
English for Journalism
English for Journalism
University of Pennsylvania
Business Communication
Business Communication for Non-Native Speakers
Rice University
English Composition
English Composition I
Duke University

Why people choose Coursera for their career

"I feel excited about my future, and being able to see what's next has really helped me decide what I want to focus my energy on."
J
Jennifer J.
Software Engineer
"The content is very engaging and the instructors are fantastic. I learned so much about career development and professional communication."
M
Michael R.
Marketing Manager
"This course helped me improve my English skills significantly and gave me confidence in job interviews."
S
Sarah L.
Data Analyst
"Excellent course structure and practical exercises. The skills I learned here directly helped me land my current job."
D
David K.
Project Manager
# Table of Contents for "Python-Powered Machine Learning: From Theory to Practice for Experts" +
  • Chapter 1: Table of Contents for "Python-Powered Machine Learning: From Theory to Practice for Experts"
  • Introduction to Machine Learning
  • Python for Machine Learning
  • Data Preprocessing
  • Exploratory Data Analysis (EDA)
  • Model Selection and Evaluation
  • Advanced Machine Learning Techniques
  • Real-World Applications of Machine Learning
  • Conclusion and Next Steps
This chapter lays the groundwork for understanding machine learning, its significance in various domains, and how Python serves as a powerful tool for implementation. It covers the evolution of machine learning and introduces key concepts and terminologies that will be explored throughout the course. +
  • Chapter 2: Understanding Machine Learning
  • The Emergence of Machine Learning
  • Significance of Machine Learning in Various Domains
  • Why Python for Machine Learning?
  • Key Concepts in Machine Learning
  • Conclusion and Next Steps
Data is the backbone of machine learning. This chapter dives into the essential preprocessing techniques necessary for preparing datasets for analysis. It covers handling missing values, normalization, encoding categorical variables, and feature selection, all with practical Python examples using libraries like Pandas and NumPy. +
  • Chapter 3: Data is the Backbone of Machine Learning
  • 1. Understanding Data Preprocessing
  • 2. Handling Missing Values
  • 3. Normalization
  • 4. Encoding Categorical Variables
  • 5. Feature Selection
  • 6. Conclusion
This chapter provides a thorough exploration of supervised learning algorithms, including linear regression, decision trees, and support vector machines. It discusses the theory behind these algorithms, their mathematical foundations, and practical implementations using Python libraries such as scikit-learn. +
  • Chapter 4: Supervised Learning Algorithms
  • 1. Introduction to Supervised Learning
  • 2. Linear Regression
  • 3. Decision Trees
  • 4. Support Vector Machines
  • 5. Conclusion
  • 2.1 Theory Behind Linear Regression
  • 2.2 Practical Implementation
  • 3.1 Theory Behind Decision Trees
  • 3.2 Practical Implementation
  • 4.1 Theory Behind SVM
  • 4.2 Practical Implementation
Focusing on unsupervised learning, this chapter examines key algorithms like K-means clustering, hierarchical clustering, and PCA (Principal Component Analysis). It discusses their applications in real-world scenarios and demonstrates how to implement these techniques using Python. +
  • Chapter 5: Focusing on Unsupervised Learning
  • 1. Understanding Unsupervised Learning
  • 2. K-means Clustering
  • 3. Hierarchical Clustering
  • 4. Principal Component Analysis (PCA)
  • 5. Conclusion
  • 2.1 How K-means Works
  • 2.2 Applications of K-means
  • 2.3 Implementing K-means in Python
  • 3.1 Types of Hierarchical Clustering
  • 3.2 Applications of Hierarchical Clustering
  • 3.3 Implementing Hierarchical Clustering in Python
  • 4.1 How PCA Works
  • 4.2 Applications of PCA
  • 4.3 Implementing PCA in Python
Understanding how to evaluate and optimize machine learning models is crucial. This chapter covers various metrics to assess model performance, such as accuracy, precision, recall, and F1 score. It also delves into cross-validation techniques and hyperparameter tuning using GridSearchCV and RandomizedSearchCV in Python. +
  • Understanding How to Evaluate and Optimize Machine Learning Models is Crucial
  • 1. Importance of Model Evaluation
  • 2. Key Metrics for Model Evaluation
  • 3. Cross-Validation Techniques
  • 4. Hyperparameter Tuning
  • 5. Conclusion
  • 3.1 K-Fold Cross-Validation
  • 3.2 Stratified K-Fold Cross-Validation
  • 3.3 Leave-One-Out Cross-Validation (LOOCV)
  • 4.1 GridSearchCV
  • 4.2 RandomizedSearchCV
This chapter explores ensemble methods like bagging, boosting, and stacking, which combine multiple models to improve predictive performance. It provides insights into popular algorithms like Random Forests and Gradient Boosting Machines, with practical implementations in Python. +
  • Chapter 7: Ensemble Methods
  • Introduction to Ensemble Methods
  • Why Ensemble Methods?
  • Types of Ensemble Methods
  • Bagging
  • Boosting
  • Stacking
  • Conclusion
  • Random Forests
  • Implementation of Random Forest in Python
  • Gradient Boosting Machines (GBM)
  • Implementation of Gradient Boosting in Python
  • How Stacking Works
  • Implementation of Stacking in Python
Introducing deep learning, this chapter explains the structure and functioning of neural networks. It covers concepts like activation functions, backpropagation, and regularization techniques. Practical examples using TensorFlow and Keras will illustrate how to build and train neural networks. +
  • Chapter 8: Introducing Deep Learning
  • The Structure of Neural Networks
  • Activation Functions
  • Backpropagation
  • Regularization Techniques
  • Building Neural Networks with TensorFlow and Keras
  • Conclusion
  • Setting Up the Environment
  • Creating a Simple Neural Network
Building on the fundamentals, this chapter dives into advanced neural network architectures, including Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequence data. It discusses their applications and implementation in Python for tasks like image classification and natural language processing. +
  • Building on the Fundamentals
  • Advanced Neural Network Architectures
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Conclusion
  • Key Components of CNNs
  • Applications of CNNs
  • Implementing CNNs in Python
  • Key Components of RNNs
  • Applications of RNNs
  • Implementing RNNs in Python
This chapter focuses on the techniques and libraries used for natural language processing (NLP) in Python, including text preprocessing, tokenization, sentiment analysis, and topic modeling. Practical case studies will demonstrate how to apply these techniques to real-world text data. +
  • Chapter 10: Techniques and Libraries Used for Natural Language Processing (NLP) in Python
  • 1. Understanding Natural Language Processing (NLP)
  • 2. Text Preprocessing
  • 3. Tokenization
  • 4. Sentiment Analysis
  • 5. Topic Modeling
  • 6. Practical Case Studies
  • 7. Conclusion
  • 3.1 Using NLTK for Tokenization
  • 3.2 Using spaCy for Tokenization
  • 4.1 Using TextBlob for Sentiment Analysis
  • 4.2 Using VADER for Sentiment Analysis
  • 5.1 Implementing LDA with Gensim
  • 6.1 Case Study 1: Sentiment Analysis of Product Reviews
  • 6.2 Case Study 2: Topic Modeling of News Articles
Transitioning from development to deployment, this chapter covers best practices for deploying machine learning models in production environments. It discusses tools and frameworks such as Flask, Docker, and cloud services, as well as monitoring and maintaining deployed models. +
  • Transitioning from Development to Deployment
  • Understanding the Deployment Pipeline
  • Best Practices for Deploying Machine Learning Models
  • Monitoring and Maintenance of Deployed Models
  • Conclusion
  • 1. Choose the Right Framework
  • 2. Containerization with Docker
  • 3. Use Cloud Services
  • 1. Model Performance Tracking
  • 2. Anomaly Detection
  • 3. Retraining the Model
This chapter addresses the ethical implications of machine learning, including bias in algorithms, data privacy, and the societal impact of AI. It provides guidelines for responsible AI development and highlights the importance of fairness and transparency in machine learning models. +
  • Ethical Implications of Machine Learning
  • 1. Understanding Bias in Algorithms
  • 2. Data Privacy Concerns
  • 3. Societal Impact of AI
  • 4. Guidelines for Responsible AI Development
  • 5. Importance of Fairness and Transparency
  • Conclusion
The final chapter explores the future landscape of machine learning, including emerging trends such as Federated Learning, AutoML, and explainable AI. It discusses how Python continues to evolve as a leading language in this field and the implications for professionals in the industry. +
  • The Final Chapter: Exploring the Future Landscape of Machine Learning
  • Emerging Trends in Machine Learning
  • The Evolution of Python in Machine Learning
  • Implications for Professionals in the Industry
  • Conclusion
  • Federated Learning
  • AutoML
  • Explainable AI (XAI)
  • Rich Ecosystem of Libraries
  • Ease of Learning and Use
  • Integration with Other Technologies
  • Continuous Learning and Skill Development
  • Collaboration Across Disciplines
  • Ethical Considerations and Responsible AI

Student Reviews

0 Reviews
★★★★★
0%
★★★★☆
0%
★★★☆☆
0%
★★☆☆☆
0%
★☆☆☆☆
0%

No reviews yet. Be the first to review this course!