Machine Learning Certification Course
Learner rating  9 

 Course platform: Simplilearn
 Level: Advanced
 Full lifetime access
 Paid course
 Class length: Approx. 58hrs.
Machine Learning Certification Course
What is Machine Learning?
In simple words, Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in an autonomous fashion, by feeding them data and information in the form of observations and realworld interactions.
Machine learning is the process of teaching a computer system on how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a selfdriving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video.
“Machine learning will increase productivity throughout the supply chain.”
–Dave Waters
About this Course
The machine learning certification course offers an indepth overview of Machine Learning topics including working with realtime 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.
Moreover, at the end of this course, you will be subjected to a project where you can apply your knowledge thought in the class to solve realworld problems.
Course Curriculum:
Lesson 01 – Course Introduction
Lesson 02 – Introduction to AI and Machine Learning
 The emergence of Artificial Intelligence and SciFi Movies with the concept of AI
 Recommender Systems, Relationship Between Artificial Intelligence, Machine Learning, and Data Science – Part A & Part B
 Definition and Features of Machine Learning and Machine Learning Approaches with Machine Learning Techniques
 Applications of Machine Learning – Part A and Part B.
Lesson 03 – Data Preprocessing
 Learning Objectives and Data Exploration: Loading Files & Techniques Part 1 and Part 2
 Seaborn & Demo of Correlation Analysis
 Data Wrangling, Missing Values in a Dataset Outlier, Values in a Dataset
 Functionalities of Data Object in Python: Part A and Part B and Different Types of Joins Typecasting
Lesson 04 – Supervised Learning
 Supervised Learning and RealLife Scenario and Understanding the Algorithms and Supervised Learning Flow
 Types of Supervised Learning – Part A Part B
 Types of Classification Algorithms and Types of Regression Algorithms – Part A and Part B
 Regression Use Case, Accuracy Metrics, Cost Function, and Challenges in Prediction
 Logistic Regression – Part A and Part B and Sigmoid Probability and Accuracy Matrix
Lesson 05 – Feature Engineering
 Feature Selection and Regression
 Factor Analysis and Factor Analysis Process, Principal Component Analysis (PCA), First Principal Component and Eigenvalues PCA
 Linear Discriminant Analysis and Maximum Separable Line
 Maximum Separable Line and Demo: Labeled Feature Reduction
 Practice: LDA Transformation
Lesson 06 – Supervised Learning: Classification
 Overview and Classification: A Supervised Learning Algorithm
 Classification of Algorithms and Decision Tree Classifier, Examples and Decision Tree Formation
 Choosing the Classifier and Overfitting of Decision Trees and Random Forest Classifier Bagging and Bootstrapping
 Performance Measures: Confusion Matrix and Cost Matrix
 Naive Bayes Classifier and Steps to Calculate Posterior Probability: Part A and Part B
 Support Vector Machines: Linear Separability and Classification Margin and Linear SVM: Mathematical Representation and Nonlinear SVMs
 The Kernel Trick Demo: Voice Classification and Practice: College Classification
Lesson 07 – Unsupervised Learning
 Learning Objectives and Example and Applications of Unsupervised Learning
 Clustering Hierarchical Clustering with Example and Demo: Clustering Animals
 Practice of Customer Segmentation and Kmeans Clustering
 Optimal Number of Clusters and Demo: ClusterBased Incentivization and Practice: Image Segmentation
Lesson 08 – Time Series Modeling
 Time Series Pattern Types Part A and Part B
 White Noise and Stationarity and Removal of NonStationarity and Demo of Air Passengers
 Practice of Beer Production and Time Series Models Part A, Part B, and Part C
 Steps in Time Series Forecasting
Lesson 09 – Ensemble Learning
 Ensemble Learning Methods Part A and Part B
 Working of AdaBoost, AdaBoost Algorithm, Flowchart Gradient Boosting, XGBoost and XGBoost Parameters Part and Part B
 Demo of Pima Indians Diabetes and Practice: Linearly Separable Species
 Model Selection Common Splitting Strategies and Demo: CrossValidation and Practice: Model Selection
Lesson 10 – Recommender Systems
 Purposes and Paradigms of Recommender Systems
 Collaborative Filtering Part A and Part B
 Association Rule Mining: Market Basket Analysis, Association Rule Generation: Apriori Algorithm and Example: Apriori Algorithm Part A and Part B
Lesson 11 – Text Mining
 Significance and Applications of Text Mining and Natural Language Toolkit Library
 Text Extraction and Preprocessing: Tokenization, Ngrams, Stop Word Removal Text, Stemming, Lemmatization, TPOS Tagging and Named Entity Recognition
 NLP Process Workflow
 Practice: Wiki Corpus and Structuring Sentences: Syntax
 Rendering Syntax Trees and Structuring Sentences: Chunking and Chunk Parsing with NP and VP Chunk and Parser
 Structuring Sentences: Chinking ContextFree Grammar (CFG) and Demo: Twitter Sentiments Practice: Airline Sentiment
What you will learn from this course
When you complete this Machine Learning Certification course, you will be able to accomplish the following:
 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
Prerequisites
The machine learning certification course requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial. You should understand these fundamental courses including Python for Data Science, Math Refresher, and Statistics Essential for Data Science, before getting into the Machine Learning online course.
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