4.62
Course Overview
The 55375AC: Fundamentals of Machine Learning course offered by CounselTrain is designed to provide a comprehensive introduction to the core concepts and techniques of machine learning. This course covers essential topics such as supervised and unsupervised learning, model evaluation, and feature selection. Participants will explore various machine learning algorithms, including linear regression, decision trees, and clustering methods, while gaining practical experience through hands-on labs and real-world case studies. The course is ideal for beginners who are new to machine learning, as well as professionals looking to strengthen their foundational understanding of the field. By the end of the course, learners will be equipped with the skills to implement basic machine learning models and understand their applications in various industries.
Learning Options
Schedule Dates
55375AC: Fundamentals of Machine Learning
55375AC: Fundamentals of Machine Learning
55375AC: Fundamentals of Machine Learning
55375AC: Fundamentals of Machine Learning
Course Content
- Understanding Machine Learning
- Understanding Machine Learning Models
- Understanding the Process for Creating a Machine Learning Model
- Reviewing Essential Math Concepts
- Using Common Python Libraries and Packages for Machine Learning
- Understanding Decision Trees
- Understanding Random Forests
- Understanding Gradient Boosted Trees
- Understanding XGBoost
- Understanding Logistic Regression
- Understanding the K-Nearest Neighbors Algorithm
- What are Other Common Algorithms?
- Preparing the Data
- Building and Fitting a Model
- Testing and Validating a Classification Model
- Understanding Multi-class Classification
- Understanding the One versus Rest and One versus One Algorithms
- Understanding Multi-label Classification
- Understanding Statistical Sampling
- Understanding Measures of Central Tendency
- Calculating Measures of Dispersion
- Evaluating the Sampling Strategy
- Estimating Confidence Intervals and Sampling Error
- Quantifying the Differences between Data Distributions
- Graphing Data to Examine Relationships and Identify Skew
- What is Correlation and Casuality?
- Selecting Model Features
- Extracting and Scaling Features
- Creating a preprocessing pipeline
- Understanding Performance Measures for a Classification Model
- Understanding Regularization to Reduce Overfitting
- Evaluating a Model
- Understanding Imbalanced Classification
- Calibrating a Model
- Using Data Sampling to Balance a Dataset
- Understanding Evaulation Metrics for an Imbalanced Dataset
FAQs
Participants should have basic knowledge of programming and statistics. Familiarity with Python or another programming language commonly used in data science is helpful but not required.
By completing this course, you will gain an understanding of fundamental machine learning concepts, the ability to implement basic machine learning models, and knowledge of various algorithms and techniques used in the field. You will also learn how to evaluate and optimize models for better performance.
Yes, the course includes hands-on labs and practical exercises designed to help participants apply the concepts learned and gain practical experience with machine learning tools and techniques.
CounselTrain provides instructor support, access to course materials, and additional resources such as forums or community groups to assist participants throughout the course.
Duration
Language
Batch Options
Upcoming Batch
Soft copy of Courseware
Certificate of Course Complition
Experienced and Professional Trainer
Lab and Exercises During Training 
Sample Questions for Exam Prepration
Mock Test on the Last Day of Training 
Exam Registration Guidance 
5-Star Training Environmment 
