Machine Learning Training

Machine Learning Course Online

  • 196 Enrolled
  • 53
Duration : 2 Months
Daily : 2 hours
Overview of Machine Learning Classes

Build a successful career with this machine learning course. This Machine Learning course online will give you the skills you need to become a successful machine learning engineer. Machine learning is a broad field of artificial intelligence that focuses on designing and developing an algorithm that identifies and learns patterns in data provided as input. There are many great jobs in Machine Learning, and most of our students are hired by Fortune 500 companies. This course gives you the confidence to clear Machine Learning certification and opportunities to establish yourself in various job roles such as Machine learning engineer, Machine learning scientist, Machine learning software engineer, and many more critical posts in the organization.

These Machine Learning Classes will help you understand how big data is created, how to extract business-relevant insights, techniques for analyzing structured and unstructured data, the latest machine learning algorithms used to develop advanced predictive models, and how to visualize data. All of this is taught from the perspective of solving complex business problems and ensuring the profitability of organizations. Our students stand out from the crowd through practical, industry-relevant case studies and win outstanding awards from the world's leading companies. By the end of the course, you will build Machine Learning model APIs and optimize the Machine Learning features.

Machine Learning Key Features

  • Core Concepts of Machine Learning
  • Get Machine Learning certification
  • Provide you with top Machine Learning interview questions
  • Guidance in building Machine Learning resume
  • Mock tests, Mock interviews
  • Flexible Timings
  • One on One sessions
Who should learn Machine Learning Tutorial?

This course is beneficial for beginners as well as for experienced individuals. Professionals like data scientists, software developers, data engineers, etc., want to learn or transition into Machine Learning.

  • Free Demo
  • 100% job Assistance
  • Flexible Timing
  • Realtime Project Work
  • Learn From Experts
  • Get Certified
  • Place your career
  • Reasonable fees
  • Access on mobile and Tv
  • High-quality content and Class videos
  • Learning Management System
  • Full lifetime access
Course Curriculum
  • Course Introduction
    • Introduction to machine learning
    • Accessing Practice Lab
  • Introduction to AI and Machine Learning
    • Learning Objectives
    • Emergence of Artificial Intelligence
    • Artificial Intelligence in Practice
    • Sci-Fi Movies with the Concept of AI
    • Recommender Systems
    • Relationship between Machine Learning, Data Science, Artificial Intelligence: Part B
    • Definition and Features of Machine Learning
    • Machine Learning Approaches and Techniques
    • Applications of Machine Learning
  • Data Preprocessing
    • Learning Objectives
    • Data Exploration Loading File
    • Data Exploration Techniques
    • Concept of Seaborn
    • What is Data Wrangling?
    • Missing Values in a Dataset
    • Outlier Values in a Dataset
    • Data Manipulation
    • Workings of Data Object in Python
    • Different Types of Joins
    • Typecasting
    • Storing Test Results
  • Supervised Learning
    • Learning Objectives
    • Supervised Learning
    • Supervised Learning- Real-Life Scenario
    • Understanding the Algorithm
    • Supervised Learning Flow
    • Types of Supervised Learning
    • Types of Classification Algorithms
    • Types of Regression Algorithms
    • Regression Use Case
    • Accuracy Metrics
    • About Cost Function
    • Evaluating Coefficients
    • Challenges in Prediction
    • Logistic Regression
    • Sigmoid Probability
    • Accuracy Matrix
  • Feature Engineering
    • Learning Objectives
    • About Feature Selection
    • Concept of Regression
    • Factor Analysis
    • Factor Analysis Process
    • Principal Component Analysis (PCA)
    • First Principal Component
    • Eigenvalues and PCA
    • Linear Discriminant Analysis
    • Maximum Separable Line
    • Find Maximum Separable Line
    • Simplifying Cancer Treatment
  • Supervised Learning Classification
    • Learning Objectives
    • Overview of Classification
    • Classification: A Supervised Learning Algorithm
    • Use Cases of Classification
    • Classification Algorithms
    • Decision Tree Classifier, Tree Examples, Tree Formation
    • Choosing the Classifier
    • Overfitting of Decision Trees
    • Random Forest Classifier- Bagging and Bootstrapping
    • Decision Tree and Random Forest Classifier
    • Performance Measures: Confusion Matrix
    • Performance Measures: Cost Matrix
    • Naive Bayes Classifier
    • Steps to Calculate Posterior Probability
    • Support Vector Machines : Linear Separability and Classification Margin
    • Linear SVM : Mathematical Representation
    • Non-linear SVMs
    • Understand The Kernel Trick
    • Classify Kinematic Data
  • Unsupervised Learning
    • Learning Objectives
    • Overview
    • Example and Applications of Unsupervised Learning
    • Clustering
    • Hierarchical Clustering
    • Hierarchical Clustering Example
    • K-means Clustering
    • Optimal Number of Clusters
    • Clustering Image Data
  • Time Series Modeling
    • Learning Objectives
    • Overview of Time Series Modeling
    • Time Series Pattern TypesWhite Noise
    • Stationarity
    • Removal of Non-Stationarity
    • Time Series Models
    • Steps in Time Series Forecasting
    • IMF Commodity Price Forecast
  • Ensemble Learning
    • Ensemble Learning
    • Overview
    • Ensemble Learning Methods
    • Working of AdaBoost
    • AdaBoost Algorithm and Flowchart
    • Gradient Boosting
    • XGBoost
    • XGBoost Parameters
    • Model Selection
    • Common Splitting Strategies
    • Tuning Classifier Model with XGBoost
  • Recommender Systems
    • Learning Objectives
    • Introduction to recommender systems
    • Purposes of Recommender Systems
    • Paradigms of Recommender Systems
    • Collaborative Filtering
    • Association Rule Mining
    • Association Rule Mining: Market Basket Analysis
    • Association Rule Generation: Apriori Algorithm
    • Apriori Algorithm Example
    • Apriori Algorithm: Rule Selection
    • Book Rental Recommendation
  • Text Mining
    • Learning Objectives
    • Overview of Text Mining
    • Significance of Text Mining
    • Applications of Text Mining
    • Natural Language ToolKit Library
    • Text Extraction and Preprocessing: Tokenization
    • Text Extraction and Preprocessing: N-grams
    • Text Extraction and Preprocessing: Stop Word Removal
    • Text Extraction and Preprocessing: Stemming
    • Text Extraction and Preprocessing: Lemmatization
    •  Text Extraction and Preprocessing: POS Tagging
    • Text Extraction and Preprocessing: Named Entity Recognition
    • NLP Process Workflow
    • What is Wiki Corpus?
    • Structuring Sentences: Syntax
    • Rendering Syntax Trees
    • Structuring Sentences: Chunking and Chunk Parsing
    • NP and VP Chunk and Parser
    • Structuring Sentences: Chinking
    • Context-Free Grammar (CFG)
Machine Learning Training FAQ's

Machine learning is a data analysis method that automates the creation of analytical models. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Machine learning engineers consider several factors when deciding which programming language is best suited for their project. Some of these languages include Java, Python, C++ and JavaScript.

There are many different positions in machine learning, including data scientist, machine learning engineer, NLP scientist, computer vision engineer, data architect, etc. The machine learning course from Sipexe will give you all the skills you need to get into one of these jobs.

We offer 24/7 support via chat, email and phone. A dedicated team is available to support you through our forums.

Sipexe accredited courses are developed by leading industry experts who know what skills employers value. All topics are covered, and if you have a good grasp of the basics, you can start a great career in this field.

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  • 651 N Broad St, Middletown, DE 19709, United States

  • info@sipexe.com
  • +1-302-208-0020
  • sipexe.com
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