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Mentor Based

Supervised Machine Learning Using Python

Logistic Regression, Random Forest, Naive Bayes Classifier

Enroll now for early access of e-LMS

MODE
Online/ e-LMS
TYPE
Mentor Based
LEVEL
Beginners
DURATION
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

  1. Machine Learning Engineer
  2. Data Scientist
  3. Data Analyst
  4. Business Intelligence Analyst
  5. Artificial Intelligence Developer
  6. Data Engineer
  7. Research Scientist
  8. Predictive Modeler
  9. Analytics Consultant
  10. 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 joblib and pickle
  • 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

  1. 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.
  2. 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.
  3. 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.
  4. 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

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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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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

  1. Quantitative Analyst: Analyze financial data and develop models for trading strategies and risk management.
  2. Consultant: Offer expertise in data science and machine learning to help companies develop custom solutions and optimize processes.
  3. Research Scientist: Conduct research in academia or industry to advance the field of machine learning.
  4. Software Engineer: Develop software applications with machine learning capabilities for various industries.
  5. 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!

Publication Opportunity
Potentially earn a place in our coveted Hall of Fame.

Centre of Excellence
Join the esteemed Centre of Excellence.

Networking and Learning
Network with industry leaders, access ongoing learning opportunities.

Hall of Fame
Get your groundbreaking work considered for publication in a prestigious Open Access Journal (worth ₹20,000/USD 1,000).

Achieve excellence and solidify your reputation among the elite!


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Recent Feedbacks In Other Workshops

Please prepare better material with both foundamentals on the topics and manifacturing processes. More It was not a good idea to just use existing slides from other presentations put together.
Other sources for informations should also be presented for self tuition

GC Faussone : 2025-01-23 at 10:09 pm

great knowledge about topic.


Mr. Pratik Bhagwan Jagtap : 2025-01-22 at 7:29 pm

In general, it seems to me that the professor knows his subject very well and knows how to explain More it well.
CARLOS OSCAR RODRIGUEZ LEAL : 2025-01-20 at 8:07 am

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