AVC Machine Learning Certification - eLearning

450,00 EUR

eLearning

This online course offers an in-depth overview of machine learning topics, including working with real-time data, developing algorithms using supervised and unsupervised learning, regression, classification, and time series modeling. You will also learn how to use Python to draw predictions from data.

Course Timeline

Course Introduction
Lesson 01
- Course Introduction.
Introduction to AI and Machine Learning
Lesson 02
- Learning Objectives
- The 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, and Data Science - Part B
- 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
Data Preprocessing
Lesson 03
- Learning Objective
- Data Exploration: Loading Files
- Demo: Importing and Storing Data
- Practice: Automobile Data Exploration I
- Data Exploration Techniques: Part 1
- Data Exploration Techniques: Part 2
- Seaborn
- Demo: Correlation Analysis
- Practice: Automobile Data Exploration II
- Data Wrangling
- Missing Values in a Dataset
- Outlier Values in a Dataset
- Demo: Outlier and Missing Value Treatment
- Practice: Data Exploration III
- 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
- Lesson-end project: Storing Test Results
Supervised Learning
Lesson 04
- 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 I
- Challenges in Prediction
- Types of Regression Algorithms - Part B
- Demo: Bigmart
- Practice: Boston Homes II
- Logistic Regression - Part A
- Logistic Regression - Part B
- Sigmoid Probability
- Accuracy Matrix
- Demo: Survival of Titanic Passengers
- Practice: Iris Species
- Key Takeaways
- Lesson-end Project: Health Insurance Cost
Feature Engineering
Lesson 05
- 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 the Maximum Separable Line
- Demo: Labeled Feature Reduction
- Practice: LDA Transformation
- Key Takeaways
- Lesson-end Project: Simplifying Cancer Treatment
Supervised Learning: Classification
Lesson 06
- Learning Objectives
- Overview of Classification
- Classification: A Supervised Learning Algorithm
- Use Cases
- 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
- Native 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
- Lesson-end Project: Classify Kinematic Data
Time Series Modeling
Lesson 07
- 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
- Lesson-end Project: Clustering Image Data
Time Series Modeling
Lesson 08
- 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 I
- Practice: Beer Production I
- Time Series Models Part A
- Time Series Models Part B
- Time Series Models Part C
- Steps in Time Series Forecasting
- Demo: Air Passengers II
- Practice: Beer Production II
- Key Takeaways
- Lesson-end Project: IMF Commodity Price Forecast
Ensemble Learning
Lesson 09
- Learning Objectives
- 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
- Lesson-end Project: Tuning Classifier Model with XGBoost
Recommender Systems
Lesson 10
- 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
- Lesson-end Project: Book Rental Recommendation
Text Mining
Lesson 11
- 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
- Practice: 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: Twitter Sentiments
- Practice: Airline Sentiment
- Key Takeaways
- Lesson-end Project: FIFA World Cup
Project 1: Uber Fare Prediction
Project - 01
Design an algorithm that will tell the fare to be charged for a passenger. Uber wants to improve the accuracy of its fare prediction model. Help Uber by choosing the best data and AI technologies for building its next-generation model.
Project 2: Mercedes-Benz Greener Manufacturing
Project - 02
Reduce the time a Mercedes-Benz spends on the test bench. Mercedes-Benz wants to shorten the time models spend on its test bench, thus moving it to the marketing phase sooner. Build and optimize a machine learning algorithm to solve this problem.
Project 3: Amazon.com - Employee Access
Project - 03
Design an algorithm to accurately predict access privileges for Amazon employees. Use the data of Amazon employees and their access permissions to build a model that automatically decides access privileges as employees enter and leave roles within Amazon.
Project 4: Income Quantification
Project - 04
Identify the level of income qualification needed for families in Latin America. The Inter-American Development Bank wants to qualify people for an aid program. Help the bank to build and improve the accuracy of the data set using a random forest classifier.
Exam Format
Exam Information
The exam is done entirely online. You have 3 exam attempts. It is necessary to book the exam attempt more than 48 hours in advance.
- Multiple Choice
- 90 questions per exam
- One mark is awarded for every correct answer
- No negative marks for wrong answers
- 120 minutes duration
- Proctored online exam

Learning Outcomes

At the end of this Machine eLearning Course, you will be able to:

Master the Concepts:

- Supervised and Unsupervised Learning - Recommendation Engines - Time Series Modeling - Statistical and Heuristic Aspects of Machine Learning - Theoretical Concepts and How They Relate to Practical Aspects

Validate Machine Learning Models and Decode Various Accuracy Metrics

Gain Practical Mastery In:

Principles, Algorithms, Applications, Support Vector Machines, Kernel SVM, Naive Bayes, Decision Tree Classifier, Random Forest Classifier, Logistic Regression, K-Means Clustering, Python.

Improve the Final Models using another Set of Optimization Algorithms

- This includes boosting and bagging techniques.

Target Audience

Data Analysts Looking to Upskill

Data Scientists Engaged in Prediction Modeling

Any Professional with Python Knowledge and Interest in Statistics and Math

Business Intelligence Developers

Why it's worth your time!

Professional certifications are important for your personal development and improve the credibility of your expertise in that field. Here are 6 other benefits:

Create a Competitive Advantage

Improve your Knowledge and Skills

Professional Credibility

Career Advancements

Personal Development

Meet Professional or Corporate Requirements

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