Online/ e-LMS
Mentor Based
Beginners
8 Weeks
About
An immersive program on Introduction to Data Science, Artificial Intelligence, and Machine Learning, where participants will delve into the fundamentals and applications of these cutting-edge technologies. Led by industry experts, the program will cover essential topics such as supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, and naive Bayes classifier, along with hands-on implementation using Python. Participants will have the opportunity to engage in practical exercises and a mini-project, gaining valuable insights and skills to leverage data for informed decision-making and predictive analytics. Don’t miss this chance to enhance your expertise in data science and machine learning and stay ahead in today’s data-driven world.
Aim
The aim of this program is to equip participants with the knowledge and skills necessary to effectively apply supervised machine learning techniques using Python. Through hands-on instruction and practical exercises, the program seeks to provide a comprehensive understanding of key concepts, algorithms, and methodologies in supervised learning, empowering participants to develop predictive models, analyze data, and make informed decisions across various domains and applications. By the end of the program, participants will have the proficiency to leverage Python libraries and tools for supervised learning tasks, enabling them to extract valuable insights, drive innovation, and solve real-world problems in diverse fields such as healthcare, finance, marketing, and more.
Program Objectives
- Machine Learning Engineer
- Data Scientist
- Data Analyst
- Business Intelligence Analyst
- Artificial Intelligence Developer
- Data Engineer
- Research Scientist
- Predictive Modeler
- Analytics Consultant
- Machine Learning Researcher
Program Structure
Module 1: Introduction to Supervised Machine Learning
Section 1.1: Understanding Supervised Learning
- Subsection 1.1.1: What is Machine Learning?
- Definition and categories (Supervised, Unsupervised, Reinforcement Learning)
- Subsection 1.1.2: Key Concepts in Supervised Learning
- Training and testing
- Features and labels
Section 1.2: Applications of Supervised Learning
- Subsection 1.2.1: Common Use Cases
- Email spam detection
- Predicting house prices
- Customer churn analysis
- Subsection 1.2.2: Benefits and Limitations
Section 1.3: Setting Up the Python Environment
- Subsection 1.3.1: Installing Python and Jupyter Notebook
- Subsection 1.3.2: Key Libraries for Machine Learning
- NumPy, Pandas, Matplotlib, Scikit-learn
Module 2: Data Preprocessing and Exploration
Section 2.1: Preparing the Dataset
- Subsection 2.1.1: Loading Data with Pandas
- Subsection 2.1.2: Handling Missing Values
- Imputation techniques (mean, median, mode)
Section 2.2: Exploring the Dataset
- Subsection 2.2.1: Descriptive Statistics
- Mean, median, standard deviation
- Subsection 2.2.2: Data Visualization
- Histograms, scatter plots, correlation heatmaps
Section 2.3: Feature Engineering
- Subsection 2.3.1: Encoding Categorical Data
- One-hot encoding, label encoding
- Subsection 2.3.2: Feature Scaling
- Standardization and normalization
Module 3: Building Supervised Machine Learning Models
Section 3.1: Regression Models
- Subsection 3.1.1: Linear Regression
- Concept and mathematical representation
- Training and evaluating a linear regression model using Scikit-learn
- Subsection 3.1.2: Polynomial Regression
- When and why to use polynomial regression
- Implementing polynomial regression in Python
Section 3.2: Classification Models
- Subsection 3.2.1: Logistic Regression
- Concept and sigmoid function
- Building and evaluating a logistic regression model
- Subsection 3.2.2: k-Nearest Neighbors (k-NN)
- How k-NN works (distance metrics, k-value selection)
- Implementing k-NN for classification tasks
Module 4: Advanced Techniques in Supervised Learning
Section 4.1: Decision Trees and Random Forests
- Subsection 4.1.1: Decision Trees
- How decision trees work (splitting criteria, entropy, and Gini index)
- Building and visualizing decision trees in Python
- Subsection 4.1.2: Random Forests
- Ensemble learning and bagging
- Training a random forest model using Scikit-learn
Section 4.2: Model Evaluation
- Subsection 4.2.1: Evaluation Metrics
- Regression: Mean Squared Error (MSE), R-squared
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC
- Subsection 4.2.2: Cross-Validation
- Concept of k-Fold Cross-Validation
- Implementing cross-validation for robust evaluation
Section 4.3: Hyperparameter Tuning
- Subsection 4.3.1: Grid Search
- Subsection 4.3.2: Random Search
Module 5: Deployment and Real-World Applications
Section 5.1: Model Deployment Basics
- Subsection 5.1.1: Saving and Loading Models
- Using
joblibandpickle
- Using
- Subsection 5.1.2: Integrating Models into Applications
- Simple API deployment using Flask
Section 5.2: Real-World Project
- Subsection 5.2.1: Problem Definition
- Example: Predicting customer churn for a telecom company
- Subsection 5.2.2: Model Development
- Data preprocessing, training, evaluation
- Subsection 5.2.3: Deployment and Presentation
- Building a basic user interface to showcase predictions
Capstone Project: End-to-End Implementation
-
- Goal: Apply everything learned in the course to solve a real-world supervised learning problem.
- Steps:
- Data preprocessing
- Model selection and evaluation
- Hyperparameter tuning
- Deployment of the final model
- Outcome: A portfolio-worthy project demonstrating your expertise in supervised machine learning.
Participant’s Eligibility
- Data scientists, machine learning engineers, analysts, researchers
- Professionals from diverse industries
- Those seeking proficiency in supervised machine learning with Python
- Beginners and those looking to deepen their understanding
- Individuals interested in building predictive models and extracting insights from data
Important Dates
Registration Ends
2023-03-25
Indian Standard Timing 12:00 PM
Program Dates
2023-03-25 to 2023-03-26
Indian Standard Timing IST 1:00 PM ONWARDS
Program Outcomes
- Comprehensive Understanding: Participants will gain a thorough understanding of the principles and applications of data science, artificial intelligence, and machine learning, enabling them to grasp the fundamental concepts underlying predictive analytics and data-driven decision-making.
- Practical Skills Development: Through hands-on exercises and implementation using Python, participants will develop practical skills in applying supervised learning algorithms such as linear regression, logistic regression, decision trees, random forests, and naive Bayes classifier to real-world datasets.
- Ability to Apply ML Techniques: Participants will be equipped with the knowledge and skills necessary to apply machine learning techniques to solve business problems, optimize processes, and extract insights from data across various industries.
- Mini Project Completion: By working on a mini-project, participants will have the opportunity to apply the concepts and techniques learned throughout the program to a real-world scenario, gaining practical experience and demonstrating their proficiency in data science and machine learning.
Mentor Profile
Fee Structure
Fee: INR 21,499 USD 291
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of CurrenciesBatches
Live
Key Takeaways
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Future Career Prospects
- Data Scientist: With proficiency in supervised machine learning techniques using Python, you’ll be well-equipped for roles as a data scientist. You can analyze large datasets, develop predictive models, and extract valuable insights to drive business decisions across various industries.
- Machine Learning Engineer: As a machine learning engineer, you can specialize in designing, implementing, and deploying machine learning systems. You’ll work on building and optimizing predictive models for applications such as recommendation systems, natural language processing, and computer vision.
- AI Researcher: If you’re interested in advancing the field of artificial intelligence, this program can serve as a solid foundation for pursuing a career as an AI researcher. You can explore cutting-edge algorithms and techniques, contribute to research projects, and develop innovative solutions to complex problems.
- Data Analyst: Many organizations rely on data analysts to interpret data, perform statistical analysis, and generate actionable insights. With skills in supervised machine learning using Python, you can excel in roles that involve data analysis and visualization, helping businesses make data-driven decisions.
- Business Intelligence Analyst: Business intelligence analysts leverage data to provide strategic insights and support decision-making processes within organizations. Your expertise in supervised machine learning can enhance your ability to uncover patterns, trends, and opportunities that drive business growth.
Job Opportunities
- Quantitative Analyst: Analyze financial data and develop models for trading strategies and risk management.
- Consultant: Offer expertise in data science and machine learning to help companies develop custom solutions and optimize processes.
- Research Scientist: Conduct research in academia or industry to advance the field of machine learning.
- Software Engineer: Develop software applications with machine learning capabilities for various industries.
- Product Manager: Lead the development of data-driven products and services based on machine learning technologies.
Enter the Hall of Fame!
Take your research to the next level!
Achieve excellence and solidify your reputation among the elite!
Related Courses
Python for Data Science
NanoIndustria: Advanced …
NanoIndustria: Advanced …
AI in Tax Planning and …
Recent Feedbacks In Other Workshops
great knowledge about topic.
