Machine Learning Certification Course

With the increasing demand for machine learning engineers, it is sought to be one of the top career options for many graduates as well as working professionals. So, take a new turn in your career with this machine learning course at Hatigen. Our expert faculty team will train on machine learning concepts for the entire course duration of 58 hours. Adding to this, you’ll get hands-on experience by working on real-time projects that will enable you to become job-ready.

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Machine Learning Course Overview

Being the subset of artificial intelligence, machine learning is now a highly demanded career option that enables you to become a successful machine learning engineer. Furthermore, in this machine learning course, you’ll get an in-depth knowledge of machine learning concepts such as regression, supervised and unsupervised learning, developing algorithms, classification, modeling, etc.

Machine Learning Online Course – Key Features

  • Trusted content.
  • Re-learn for free anytime in a year.
  • Rigorous assignments and assessments.
  • Learn at your own pace.
  • Mandatory feedback sessions.
  • Mock-interviews.
  • Hands-on real-time experience.
  • Free mentorship.
  • Live chat for instant solutions.
  • Job ready employees post-training.
  • End-to-end training.
  • Download the certificate after the course.
ML Online Course – Skills Covered: Supervised and unsupervised learning. Time series modeling. Linear and logistic regression. Kernel SVM. KMeans clustering. Naive Bayes. Decision tree. Random forest classifiers. Boosting and Bagging techniques. Deep Learning fundamentals.

Machine Learning Course – Benefits

The global market for machine learning is expected to reach $90.1 billion in 2026 with a CAGR – compound annual growth rate for the period of 2021-2026 to be 39.4%.

Designation
Annual Salary
Hiring Companies
Job Wise Benefits
Designation
Machine Learning Engineer

UK
Hiring Companies
Designation
Data Scientist

UK
Hiring Companies

Machine Learning Certification Course – Training Options

Self-Paced Learning

£ 1200

  • 1-year access to the Machine Learning course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Access to the class recordings.
  • Assistance and support.
  • Download certification.
  • Free mentorship.

Online Boot Camp

£ 1000

  • Everything in Self-paced learning +
  • On-spot doubt clarification.
  • Interactive training sessions.
  • Sessions on the capstone project.
  • Live, online classroom training.
  • Mock-interviews.

Corporate Training

Customized to your team’s needs

  • 1-year access to the Machine Learning course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Class recordings.
  • Assistance and support.
  • Certification after the course.

Online Machine Learning Course – Curriculum

Eligibility

Graduates who are aiming to build their careers in Data Science and Machine Learning are eligible for this course. Adding to this, the Machine Learning Online Training Course is best suited for intermediate professionals with job roles such as Business Analysts, Developers, and Analytics Managers who are aiming to work as Machine Learning Engineers.

Pre-requisites

Students who have a good understanding of mathematical concepts and basic statistics at the college level can opt for this Machine Learning Course. Also, basic knowledge of python programming is a plus. Furthermore, before you join the Machine Learning certification course, knowledge in some fundamental subjects such as math refresher, python for data science, and statistics is also beneficial.

Course Content

  • 1.01 Course Introduction
  • 1.02 Demo: Jupyter Lab Walk - Through
  • 2.01 Learning Objectives
  • 2.02 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
  • 2.03 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B
  • 2.04 Definition and Features of Machine Learning
  • 2.05 Machine Learning Approaches
  • 2.06 Key Takeaways
  • 3.01 Learning Objectives
  • 3.02 Supervised Learning
  • 3.03 Supervised Learning: Real Life Scenario
  • 3.04 Understanding the Algorithm
  • 3.05 Supervised Learning Flow
  • 3.06 Types of Supervised Learning: Part A
  • 3.07 Types of Supervised Learning: Part B
  • 3.08 Types of Classification Algorithms
  • 3.09 Types of Regression Algorithms: Part A
  • 3.10 Regression Use Case
  • 3.11 Accuracy Metrics
  • 3.12 Cost Function
  • 3.13 Evaluating Coefficients
  • 3.14 Demo: Linear Regression
  • 3.15 Challenges in Prediction
  • 3.16 Types of Regression Algorithms: Part B
  • 3.17 Demo: Bigmart
  • 3.18 Logistic Regression: Part A
  • 3.19 Logistic Regression: Part B
  • 3.20 Sigmoid Probability
  • 3.21 Accuracy Matrix
  • 3.22 Demo: Survival of Titanic Passengers
  • 3.23 Overview of Classification
  • 3.24 Classification: A Supervised Learning Algorithm
  • 3.25 Use Cases
  • 3.26 Classification Algorithms
  • 3.27 Performance Measures: Confusion Matrix
  • 3.28 Performance Measures: Cost Matrix
  • 3.29 Naive Bayes Classifier
  • 3.30 Steps to Calculate Posterior Probability: Part A
  • 3.31 Steps to Calculate Posterior Probability: Part B
  • 3.32 Support Vector Machines: Linear Separability
  • 3.33 Support Vector Machines: Classification Margin
  • 3.34 Linear SVM: Mathematical Representation
  • 3.35 Non linear SVMs
  • 3.36 The Kernel Trick
  • 3.37 Demo: Voice Classification
  • 3.38 Key Takeaways
  • 4.01 Learning Objectives
  • 4.02 Decision Tree: Classifier
  • 4.03 Decision Tree: Examples
  • 4.04 Decision Tree: Formation
  • 4.05 Choosing the Classifier
  • 4.06 Overfitting of Decision Trees
  • 4.07 Random Forest Classifier Bagging and Bootstrapping
  • 4.08 Decision Tree and Random Forest Classifier
  • 4.09 Demo: Horse Survival
  • 4.10 Key Takeaways
  • 5.01 Learning Objectives
  • 5.02 Overview
  • 5.03 Example and Applications of Unsupervised Learning
  • 5.04 Clustering
  • 5.05 Hierarchical Clustering
  • 5.06 Hierarchical Clustering: Example
  • 5.07 Demo: Clustering Animals
  • 5.08 K-means Clustering
  • 5.09 Optimal Number of Clusters
  • 5.10 Demo: Cluster Based Incentivization
  • 5.11 Key Takeaways
  • 6.01 Learning Objectives
  • 6.02 Overview of Time Series Modeling
  • 6.03 Time Series Pattern Types: Part A
  • 6.04 Time Series Pattern Types: Part B
  • 6.05 White Noise
  • 6.06 Stationarity
  • 6.07 Removal of Non Stationarity
  • 6.08 Demo: Air Passengers I
  • 6.09 Time Series Models: Part A
  • 6.10 Time Series Models: Part B
  • 6.11 Time Series Models: Part C
  • 6.12 Steps in Time Series Forecasting
  • 6.13 Demo: Air Passengers II
  • 6.14 Key Takeaways
  • 7.01 Learning Objectives
  • 7.02 Overview
  • 7.03 Ensemble Learning Methods: Part A
  • 7.04 Ensemble Learning Methods: Part B
  • 7.05 Working of AdaBoost
  • 7.06 AdaBoost Algorithm and Flowchart
  • 7.07 Gradient Boosting
  • 7.08 XGBoost
  • 7.09 XGBoost Parameters: Part A
  • 7.10 XGBoost Parameters: Part B
  • 7.11 Demo: Pima Indians Diabetes
  • 7.12 Model Selection
  • 7.13 Common Splitting Strategies
  • 7.14 Demo: Cross Validation
  • 7.15 Key Takeaways
  • 8.01 Learning Objectives
  • 8.02 Introduction
  • 8.03 Purposes of Recommender Systems
  • 8.04 Paradigms of Recommender Systems
  • 8.05 Collaborative Filtering: Part A
  • 8.06 Collaborative Filtering: Part B
  • 8.07 Association Rule: Mining
  • 8.08 Association Rule: Mining Market Basket Analysis
  • 8.09 Association Rule: Generation Apriori Algorithm
  • 8.10 Apriori Algorithm Example: Part A
  • 8.11 Apriori Algorithm Example: Part B
  • 8.12 Apriori Algorithm: Rule Selection
  • 8.13 Demo: User Movie Recommendation Model
  • 8.14 Key Takeaways
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Machine Learning Online Training – FAQs

Renowned as the branch of artificial intelligence (AI) and computer science, Machine learning is the study of computer algorithms and the use of data to imitate the way that humans learn.

Machine learning is used to derive key insights that can help to make business decisions to impact key growth metrics. For these insights, a machine learning engineer trains algorithms with the use of statistical methods to make predictions, or classifications.

The three different types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. However, Hatigen trains you in all three different machine learning types through our expert mentors.

Yes, basic coding knowledge is important before you join a machine learning course online. So, you can have knowledge of either Python, R, or Java. It is necessary for certain machine learning tasks such as statistical analysis.

Yes, Machine Learning is a great career option as many present-day applications are developed by this technology. It is one of the top in-demand career fields which offers amazing salaries and growth of postings.

This Machine Learning Training program enables you to learn machine learning concepts from the scratch. Adding to this, you can get through some articles or tutorials that can allow you to get the grip on machine learning techniques.

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