Machine Learning Certification Course

Simplilearn

Category: Data Science Tags: Advanced, Online Course

About the Machine Learning Certification Course

Machine learning is the process of teaching a computer system how to make accurate predictions when fed data. The machine learning certification 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.

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 real-world problems.

What you will learn from the Machine Learning Certification 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

Syllabus

Lesson 01 – Introduction to the Machine Learning Certification

Lesson 02 – AI and Machine Learning

• The emergence of Artificial Intelligence and Sci-Fi 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 Real-Life 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 Non-linear 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 K-means Clustering
• Optimal Number of Clusters and Demo: Cluster-Based 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 Non-Stationarity 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: Cross-Validation 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, N-grams, 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 Context-Free Grammar (CFG) and Demo: Twitter Sentiments Practice: Airline Sentiment

Pre-requisites for the Machine Learning Certification

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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