Instructor Led Training

Machine Learning with Python

Instructor-led training (ILT) is a traditional form of education that involves a skilled instructor leading a classroom or virtual session to deliver training to learners.

Limited seat available, enroll before date August 12, 2024.
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why should buy instructor led course?

Investing in an instructor-led course offers several advantages that can greatly enhance your learning experience. One of the key benefits is the opportunity to receive expert guidance from seasoned professionals who possess extensive knowledge and expertise in the subject matter. These instructors can offer valuable insights, address your queries, and provide guidance tailored to your specific needs. Additionally, instructor-led courses follow a well-structured curriculum, ensuring a comprehensive learning journey that covers all the essential topics. This structured approach enables you to progress in a logical and organized manner, building a strong foundation of knowledge. Moreover, instructor-led courses often provide personalized feedback, allowing you to receive individualized assessments and guidance to improve your understanding and skills.

Professional Certificate.

Obtaining certification of completion is a significant benefit that comes with many instructor-led courses. This certification serves as formal recognition of your successful completion of the course and showcases your commitment to learning and professional development. It can be a valuable addition to your resume or portfolio, highlighting your expertise and dedication in a specific field or skill set. Certification demonstrates to employers, clients, or colleagues that you have acquired the necessary knowledge and skills to perform tasks effectively. It can enhance your credibility and open doors to new career opportunities or advancements. Moreover, certification provides a sense of accomplishment and satisfaction, validating the time and effort you invested in the course. Ultimately, the certification of completion offers tangible evidence of your commitment to continuous learning and professional growth, making it a worthwhile asset in today's competitive job market.

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Estimated time

4 Months

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Enroll by

August 12, 2024

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Skills acquired

No degree or skills required.

How Does It Work?

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Zoom meeting with student twice a week.

As an educator, I have implemented a structured learning approach by conducting Zoom meetings with my students twice a week. This interactive platform has become an invaluable tool for fostering meaningful connections and facilitating engaging discussions in a virtual classroom setting.

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AI Tutor support.

Mentoring support plays a crucial role in guiding individuals towards personal and professional growth. By offering mentorship, I provide a safe and supportive space for individuals to explore their goals, challenges, and aspirations.

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Assignments and Grade.

Assignments and grading are essential components of the educational process, allowing students to demonstrate their understanding of concepts and skills while providing teachers with a means to assess their progress. Assignments are designed to reinforce learning, encourage critical thinking, and promote independent problem-solving.

About This Course

Enroll yourself in the Machine Learning Python course and lab to gain expertise on the processes, patterns, and strategies needed for building effective learning systems. The Machine learning course imparts skills that are required for understanding machine learning algorithms, models, and core machine learning concepts, evaluating classifiers and regressors, connections, extensions, and further directions. The study guide is equipped with learning resources to broaden your toolbox and explore some of the field’s most sophisticated and exciting techniques.

Skills You’ll Get

Get the support you need. Enroll in our Instructor-Led Course.

Lesson Plan

1

Let’s Discuss Learning

  • Welcome
  • Scope, Terminology, Prediction, and Data
  • Putting the Machine in Machine Learning
  • Examples of Learning Systems
  • Evaluating Learning Systems
  • A Process for Building Learning Systems
  • Assumptions and Reality of Learning
  • End-of-Lesson Material
2

Some Technical Background

  • About Our Setup
  • The Need for Mathematical Language
  • Our Software for Tackling Machine Learning
  • Probability
  • Linear Combinations, Weighted Sums, and Dot Products
  • A Geometric View: Points in Space
  • Notation and the Plus-One Trick
  • Getting Groovy, Breaking the Straight-Jacket, and Nonlinearity
  • NumPy versus “All the Maths”
  • Floating-Point Issues
  • EOC
3

Predicting Categories: Getting Started with Classification

  • Classification Tasks
  • A Simple Classification Dataset
  • Training and Testing: Don’t Teach to the Test
  • Evaluation: Grading the Exam
  • Simple Classifier #1: Nearest Neighbors, Long Distance Relationships, and Assumptions
  • Simple Classifier #2: Naive Bayes, Probability, and Broken Promises
  • Simplistic Evaluation of Classifiers
  • EOC
4

Predicting Numerical Values: Getting Started with Regression

  • A Simple Regression Dataset
  • Nearest-Neighbors Regression and Summary Statistics
  • Linear Regression and Errors
  • Optimization: Picking the Best Answer
  • Simple Evaluation and Comparison of Regressors
  • EOC
5

Evaluating and Comparing Learners

  • Evaluation and Why Less Is More
  • Terminology for Learning Phases
  • Major Tom, There’s Something Wrong: Overfitting and Underfitting
  • From Errors to Costs
  • (Re)Sampling: Making More from Less
  • Break-It-Down: Deconstructing Error into Bias and Variance
  • Graphical Evaluation and Comparison
  • Comparing Learners with Cross-Validation
  • EOC
6

Evaluating Classifiers

  • Baseline Classifiers
  • Beyond Accuracy: Metrics for Classification
  • ROC Curves
  • Another Take on Multiclass: One-versus-One
  • Precision-Recall Curves
  • Cumulative Response and Lift Curves
  • More Sophisticated Evaluation of Classifiers: Take Two
  • EOC
7

Evaluating Regressors

  • Baseline Regressors
  • Additional Measures for Regression
  • Residual Plots
  • A First Look at Standardization
  • Evaluating Regressors in a More Sophisticated Way: Take Two
  • EOC
8

More Classification Methods

  • Revisiting Classification
  • Decision Trees
  • Support Vector Classifiers
  • Logistic Regression
  • Discriminant Analysis
  • Assumptions, Biases, and Classifiers
  • Comparison of Classifiers: Take Three
  • EOC
9

More Regression Methods

  • Linear Regression in the Penalty Box: Regularization
  • Support Vector Regression
  • Piecewise Constant Regression
  • Regression Trees
  • Comparison of Regressors: Take Three
  • EOC
10

Manual Feature Engineering: Manipulating Data for Fun and Profit

  • Feature Engineering Terminology and Motivation
  • Feature Selection and Data Reduction: Taking out the Trash
  • Feature Scaling
  • Discretization
  • Categorical Coding
  • Relationships and Interactions
  • Target Manipulations
  • EOC
11

Tuning Hyperparameters and Pipelines

  • Models, Parameters, Hyperparameters
  • Tuning Hyperparameters
  • Down the Recursive Rabbit Hole: Nested Cross-Validation
  • Pipelines
  • Pipelines and Tuning Together
  • EOC
12

Combining Learners

  • Ensembles
  • Voting Ensembles
  • Bagging and Random Forests
  • Boosting
  • Comparing the Tree-Ensemble Methods
  • EOC
13

Models That Engineer Features for Us

  • Feature Selection
  • Feature Construction with Kernels
  • Principal Components Analysis: An Unsupervised Technique
  • EOC
14

Feature Engineering for Domains: Domain-Specific Learning

  • Working with Text
  • Clustering
  • Working with Images
  • EOC
15

Connections, Extensions, and Further Directions

  • Optimization
  • Linear Regression from Raw Materials
  • Building Logistic Regression from Raw Materials
  • SVM from Raw Materials
  • Neural Networks
  • Probabilistic Graphical Models
  • EOC

Appendix A: mlwpy.py Listing

Frequently asked questions

Instructor Led Training refers to a traditional form of education where a knowledgeable instructor leads a classroom or virtual session to deliver training to learners. It involves direct interaction between the instructor and participants, allowing for real-time feedback and guidance.

ILT offers numerous benefits, including personalized attention, immediate clarification of doubts, interactive discussions, and hands-on learning experiences. It promotes engagement, fosters collaboration among learners, and enables participants to receive expert guidance from the instructor.

Unlike e-learning or self-paced courses, ILT provides a structured and interactive learning environment. It allows participants to engage with the instructor and fellow learners, receive real-time feedback, and benefit from the instructor's expertise. ILT offers the opportunity for immediate clarification and fosters dynamic interactions.

Yes, ILT can be conducted virtually using web conferencing tools or virtual classroom platforms. This allows participants from different locations to join the training session and interact with the instructor and peers through video conferencing, chat features, and shared documents.

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