Data Science and AI
Data Science and Machine Learning Essentials
- 721+ students
- Online Live Classes
Descriptions
The “Data Science and Machine Learning Mastery” course is designed to provide participants with a comprehensive understanding and practical skills in the field of data science and machine learning. This course covers a wide range of topics, from data preprocessing and exploration to advanced machine learning algorithms and model deployment.
Participants will learn how to collect, clean, and analyze data using popular tools and libraries such as Python, pandas, NumPy, and scikit-learn. They will explore different machine learning techniques including supervised learning, unsupervised learning, and deep learning, and understand how to select and evaluate models for various tasks.
Hands-on projects and case studies will be an integral part of this course, allowing participants to apply their knowledge to real-world datasets and solve practical problems. By the end of the course, participants will be equipped to build and deploy machine learning models, extract valuable insights from data, and make data-driven decisions in various domains.
Key Points
- Overview of data science concepts, machine learning algorithms, and their applications.
- Techniques for data cleaning, handling missing values, feature scaling, and exploratory data analysis (EDA).
- Understanding and implementation of regression, classification, and ensemble learning techniques.
- Clustering algorithms, dimensionality reduction techniques, and anomaly detection.
- Methods for evaluating model performance, cross-validation, and optimizing model parameters.
- Introduction to neural networks, deep learning frameworks (e.g., TensorFlow, Keras), and convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
Course Lessons
- Overview of data science, machine learning, and their applications.
- Understanding the data science workflow and machine learning pipeline.
- Introduction to Python programming language and essential libraries like NumPy, pandas, and matplotlib.
- Data acquisition and loading techniques.
- Data cleaning methods for handling missing values, outliers, and inconsistencies.
- Feature engineering and transformation techniques.
- Techniques for visualizing and summarizing data using plots, histograms, and statistical measures.
- Identifying patterns, correlations, and relationships in data.
- Performing data profiling and understanding data distributions.
- Understanding the principles of supervised learning.
- Implementing regression algorithms (linear regression, polynomial regression) for continuous target variables.
- Implementing classification algorithms (logistic regression, decision trees, random forests) for categorical target variables.
- Introduction to unsupervised learning and clustering techniques (k-means clustering, hierarchical clustering).
- Dimensionality reduction techniques (principal component analysis - PCA).
- Anomaly detection methods (Isolation Forest, One-Class SVM).
- Techniques for evaluating model performance (accuracy, precision, recall, F1-score, ROC-AUC).
- Cross-validation methods for assessing model generalization.
- Hyperparameter tuning using grid search and randomized search.
- Basics of neural networks, activation functions, and gradient descent optimization.
- Introduction to deep learning frameworks (TensorFlow, Keras) and building neural network models.
- Introduction to convolutional neural networks (CNNs) for image data and recurrent neural networks (RNNs) for sequential data.
- Techniques for deploying machine learning models in production environments (Flask, Docker).
- Model serialization and deployment on cloud platforms (AWS, Google Cloud).
- Creating APIs for model integration and usage in web applications.
- Hands-on projects and case studies covering real-world datasets and business problems.
- Applying learned techniques to solve problems in domains like healthcare, finance, e-commerce, etc.
- Understanding ethical considerations in data science and machine learning.
- Exploring issues related to bias, fairness, and transparency in machine learning models.
- Strategies for mitigating bias and ensuring responsible AI practices.
- Exploring various career paths in data science and machine learning.
- Interview preparation tips, resume building, and networking strategies.
- Industry trends, best practices, and continuous learning resources.
Instructor
Data Science Expert
This course includes:
- 40 hours on-demand Live Classes
- Full lifetime access
- Access on mobile, Tablet and Laptop
- 100% Paid Internship Assurance
- Certificate of completion
