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    • Machine Learning

    Explore the concepts of Machine Learning and understand how it’s transforming the digital world. An exciting branch of Artificial Intelligence, this Machine Learning certification online course will provide the skills you need to become a Machine Learning Engineer and unlock the power of this emerging field.

    Machine Learning Course Overview

    This Machine Learning online course offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling. Learn how to use Python in this Machine Learning certification training to draw predictions from data.

    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 Curriculum

    Elgibility

    The Machine Learning certification online course is well-suited for participants at the intermediate level including, analytics managers, business analysts, information architects, developers looking to become data scientists, and graduates seeking a career in Data Science and Machine Learning.

    • Course Introduction
    • Accessing Practice Lab
    • Learning Objectives
    • Emergence of Artificial Intelligence
    • Artificial Intelligence in Practice
    • Sci-Fi Movies with the Concept of AI
    • Recommender Systems
    • Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A
    • Relationship between Artificial Intelligence, Machine Learning,
    • Definition and Features of Machine Learning
    • Machine Learning Approaches
    • Machine Learning Techniques
    • Applications of Machine Learning: Part A
    • Applications of Machine Learning: Part B
    • Key Takeaways
    • Knowledge Check
    • Learning Objectives
    • Data Exploration Loading Files: Part A
    • Data Exploration Loading Files: Part B
    • Demo: Importing and Storing Data
    • Practice: Automobile Data Exploration - A
    • Data Exploration Techniques: Part A
    • Data Exploration Techniques: Part B
    • Seaborn
    • Demo: Correlation Analysis
    • Practice: Automobile Data Exploration - B
    • Data Wrangling
    • Missing Values in a Dataset
    • Outlier Values in a Dataset
    • Demo: Outlier and Missing Value Treatment
    • Practice: Data Exploration - C
    • Data Manipulation
    • Functionalities of Data Object in Python: Part A
    • Functionalities of Data Object in Python: Part B
    • Different Types of Joins
    • Typecasting
    • Demo: Labor Hours Comparison
    • Practice: Data Manipulation
    • Key Takeaways
    • Knowledge Check
    • Storing Test Results
    • Learning Objectives
    • Supervised Learning
    • Supervised Learning- Real-Life Scenario
    • Understanding the Algorithm
    • Supervised Learning Flow
    • Types of Supervised Learning: Part A
    • Types of Supervised Learning: Part B
    • Types of Classification Algorithms
    • Types of Regression Algorithms: Part A
    • Regression Use Case
    • Accuracy Metrics
    • Cost Function
    • Evaluating Coefficients
    • Demo: Linear Regression
    • Practice: Boston Homes - A
    • Challenges in Prediction
    • Types of Regression Algorithms: Part B
    • Demo: Bigmart
    • Practice: Boston Homes - B
    • Logistic Regression: Part A
    • Logistic Regression: Part B
    • Sigmoid Probability
    • Accuracy Matrix
    • Demo: Survival of Titanic Passengers
    • Practice: Iris Species
    • Key Takeaways
    • Knowledge Check
    • Health Insurance Cost
    • Learning Objectives
    • Feature Selection
    • Regression
    • Factor Analysis
    • Factor Analysis Process
    • Principal Component Analysis (PCA)
    • First Principal Component
    • Eigenvalues and PCA
    • Demo: Feature Reduction
    • Practice: PCA Transformation
    • Linear Discriminant Analysis
    • Maximum Separable Line
    • Find Maximum Separable Line
    • Demo: Labeled Feature Reduction
    • Practice: LDA Transformation
    • Key Takeaways
    • Knowledge Check
    • Simplifying Cancer Treatment
    • Learning Objectives
    • Overview of Classification
    • Classification: A Supervised Learning Algorithm
    • Use Cases of Classification
    • Classification Algorithms
    • Decision Tree Classifier
    • Decision Tree Examples
    • Decision 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
    • Demo: Horse Survival
    • Practice: Loan Risk Analysis
    • Naive Bayes Classifier
    • Steps to Calculate Posterior Probability: Part A
    • Steps to Calculate Posterior Probability: Part B
    • Support Vector Machines : Linear Separability
    • Support Vector Machines : Classification Margin
    • Linear SVM : Mathematical Representation
    • Non-linear SVMs
    • The Kernel Trick
    • Demo: Voice Classification
    • Practice: College Classification
    • Key Takeaways
    • Knowledge Check
    • Classify Kinematic Data
    • Learning Objectives
    • Overview
    • Example and Applications of Unsupervised Learning
    • Clustering
    • Hierarchical Clustering
    • Hierarchical Clustering Example
    • Demo: Clustering Animals
    • Practice: Customer Segmentation
    • K-means Clustering
    • Optimal Number of Clusters
    • Demo: Cluster Based Incentivization
    • Practice: Image Segmentation
    • Key Takeaways
    • Knowledge Check
    • Clustering Image Data
    • Learning Objectives
    • Overview of Time Series Modeling
    • Time Series Pattern Types: Part A
    • Time Series Pattern Types: Part B
    • White Noise
    • Stationarity
    • Removal of Non-Stationarity
    • Demo: Air Passengers - A
    • Practice: Beer Production - A
    • Time Series Models: Part A
    • Time Series Models: Part B
    • Time Series Models: Part C
    • Steps in Time Series Forecasting
    • Demo: Air Passengers - B
    • Practice: Beer Production - B
    • Key Takeaways
    • Knowledge Check
    • IMF Commodity Price Forecast
    • Ensemble Learning
    • Overview
    • Ensemble Learning Methods: Part A
    • Ensemble Learning Methods: Part B
    • Working of AdaBoost
    • AdaBoost Algorithm and Flowchart
    • Gradient Boosting
    • XGBoost
    • XGBoost Parameters: Part A
    • XGBoost Parameters: Part B
    • Demo: Pima Indians Diabetes
    • Practice: Linearly Separable Species
    • Model Selection
    • Common Splitting Strategies
    • Demo: Cross Validation
    • Practice: Model Selection
    • Key Takeaways
    • Knowledge Check
    • Tuning Classifier Model with XGBoost
    • Learning Objectives
    • Introduction
    • Purposes of Recommender Systems
    • Paradigms of Recommender Systems
    • Collaborative Filtering: Part A
    • Collaborative Filtering: Part B
    • Association Rule Mining
    • Association Rule Mining: Market Basket Analysis
    • Association Rule Generation: Apriori Algorithm
    • Apriori Algorithm Example: Part A
    • Apriori Algorithm Example: Part B
    • Apriori Algorithm: Rule Selection
    • Demo: User-Movie Recommendation Model
    • Practice: Movie-Movie recommendation
    • Key Takeaways
    • Knowledge Check
    • Book Rental Recommendation
    • 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
    • Demo: Processing Brown Corpus
    • 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)
    • Demo: Structuring Sentences
    • Practice: Airline Sentiment
    • Key Takeaways
    • Knowledge Check
    • FIFA World Cup
    • Project Highlights
    • Uber Fare Prediction
    • Amazon - Employee Access
    • California Housing Price Prediction
    • Phishing Detector with LR
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