AVC Machine Learning Certification - eLearning

AVC Machine Learning Certification - eLearning

Machine learning enables computers to learn from data and make decisions or predictions without being explicitly programmed to do so. Essentially, machine learning is about creating and implementing algorithms that facilitate these decisions and predictions.

Secure your career with this course in machine learning. Learn this exciting branch of artificial intelligence with a program of applied learning, interactive labs, four hands-on projects, and mentoring. With our machine learning training, you'll master the machine learning concepts required for a certification in machine learning. This online machine learning course will equip you with the skills needed to become a successful machine learning engineer today.

The Machine Learning Certification course is well suited for mid-level participants, including analytics managers, business analysts, information architects, developers who want to become data scientists, and academics who want to pursue a career in data science and machine learning.

This online course provides an in-depth overview of topics related to machine learning, 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 make predictions from data.

Offer: In addition to this practical e-learning course, we offer you free access to our online classroom sessions. You have 90 days to book free online training sessions, which always take place at flexible times. In addition to your e-learning and if you wish, you will have the opportunity to interact with the trainer and other participants. These online classroom sessions are also recorded, so you can save them.

Program Features:

• 58 hours of blended learning
• 15 hours of self-study online 46 hours of instructor-led training
• Four industry-based final project assignments.
• Interactive learning with Jupyter notebooks integrated lab
• Dedicated mentoring session from teachers with industry experts.

How the training is delivered

The program offers a unique combined solution: e-learning and virtual "live" classroom courses. During the lessons, you are in contact with the trainer and other participants. You can ask all your questions. You can attend some sessions and we also record the sessions. You will never miss an opportunity to participate.

Target audience

• Data analysts seeking further training
• Data scientists working on predictive modeling
• All professionals with Python skills and interest in statistics and mathematics Business intelligence developers.

Key learning outcomes:

• Master the concepts of supervised and unsupervised learning, recommendation engine and time series modeling.
• Practical mastery of machine learning principles, algorithms and applications through a hands-on approach that includes working on four major projects from start to finish and 25+ practical exercises.
• In-depth knowledge of statistical and heuristic aspects of machine learning.
• Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classification, random forest classification, logistic regression, K-means clustering and more in Python.
• Validate machine learning models and decode different accuracy measures.
• Improve the final models using a different set of optimization algorithms, including boosting and bagging techniques.
• Understand theoretical concepts and how they relate to practical aspects of machine learning.

Details and criteria for certification:

• Requirements: 85 percent of online self-study or participation in a course in a virtual classroom.
• A score of at least 75 percent in the final course evaluation.
• Successful evaluation of at least one project.

Course program:

Lesson 01 - Course introduction

• Course introduction

Lesson 02 - Introduction to Artificial Intelligence and Machine Learning

Learning objectives

• The emergence of artificial intelligence
• Artificial intelligence in practice
• Sci-Fi movies featuring the concept of AI recommender systems
• The relationship between artificial intelligence, machine learning and data science - Part A The relationship between artificial intelligence, machine learning and data science - Part B Definition and characteristics of machine learning
• Methods of machine learning Techniques of machine learning
• Applications of Machine Learning - Part A Applications of Machine Learning - Part B Main issues.

Lesson 03 - Data preprocessing

Learning objectives

• Exploring data: Loading files Demo: Importing and storing data
• Exercise: Data Exploration I Data Exploration Techniques: Part 1 Data Exploration Techniques: Part 2 Seaborn
• Demo: Correlation Analysis
• Exercise: Data Exploration II Data Wrangling
• Missing values in a data set Speakers in a data set
• Demo: Exercise: Data exploration III
• Manipulating data
• Functions for data objects in Python: Part A Functions for data objects in Python: Part B Different types of connections
• Classification of types
• Demo: Exercise: Comparison of working hours Exercise: Data manipulation
• Main results
• Final project: Saving test results

Lesson 04 - Guidance

Learning outcomes

• Supervised learning
• Supervised learning - a real-life scenario Understanding the algorithm The flow of supervised learning
• Types of supervised learning - Part A Types of supervised learning - Part B
• Types of classification algorithms Types of regression algorithms - Part A Case study on regression use
• Numbers for accuracy Cost function
• Evaluation of Coefficients Demo: Linear Regression Internship: Boston Homes I Challenges associated with predictions
• Types of Regression Algorithms - Part B Demo: Bigmart
• Practice: Boston Homes II Logistic Regression - Part A Logistic Regression - Part B Sigmoid Probability Matrix
• Demo: Survival of Titanic passengers Practical: Iris species
• Main results
• Final project: Health insurance costs

Lesson 05 - Functional engineering

Learning objectives

• Selection of characteristics Regression factor analysis
• The process of factor analysis
• 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
• Main results
• Final project: Simplification of cancer treatment

Lesson 06 - Learning from supervision: Classification

Learning objectives

• Overview of classification
• Classification: An Algorithm for Supervised Learning Areas of use
• Classification Algorithms Decision Trees Classifiers Decision Trees: Examples Decision Tree Formation Classifier Selection Decision Tree Overfitting
• Random Forest Classifier - Bagging and Bootstrapping Decision Tree and Random Forest Classifier Performance Measure: Cost Matrix
• Demo: Horse Survival Practical: Analyzing Loan Risks Naive Bayes Classifier
• Steps to calculate the posterior probability: Part A: Steps to calculate the posterior probability: Steps to calculate the posterior probability: Part B Support vector machines: linear separability Support vector machines: classification margin Linear SVM: mathematical representation
• Non-linear SVMs The Core Trick
• Demo: Voice Classification Exercise: Main Points: Peer Classification
• Final project: Classification of kinematic data

Lesson 07 - Unsupervised learning

Overview of the learning objectives

• Examples and applications of unsupervised learning Clustering
• Hierarchical clustering Hierarchical clustering: Examples Demo: Clustering Animals Practice: Customer segmentation K-means Clustering
• Optimal number of clusters
• Demo: Cluster-based stimulation Practical: Image segmentation
• Main results
• Final project: Clustering of image data

Lesson 08 - Time series modeling

Learning objectives

• Overview of time series modeling Patterns of time series Part A Patterns of time series Part B White noise
• Stationarity
• Removal of non-stationarity Demo: Airline Passenger I Exercise: Time series models Part A Time series models Part B Time series models Part C
• Steps in time series forecasting Demo: Airline passenger II Exercise: Oil production II
• Main results
• Final project: Forecasting IMF commodity prices

Lesson 09 - Learning in ensembles

Learning objectives

• Ensemble learning methods Part A Ensemble learning methods Part B How AdaBoost works
• AdaBoost algorithm and flowchart Gradient Boosting
• XGBoost
• XGBoost Parameters Part A XGBoost Parameters Part B Demo: Pima Indian Diabetes
• Exercise: Linear separable types of model selection
• Common splitting strategies Demo: Cross validation Exercise: Model selection Important notes
• Final project: Adjusting the classification model with XGBoost

Lesson 10 - Recommendation system

Learning objectives

• Introduction
• Objectives 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
• Example of Apriori algorithm: Part B Apriori algorithm: Rule selection
• Demo: Recommendation model for user movies Internship: Movie-movie recommendation Main points
• Final project: Book rental recommendation

Lesson 11 - Word processing

Learning objectives

• Overview of word processing The importance of word processing Using word processing
• The Natural Language Toolkit library
• Text Extraction and Preprocessing: Tokenization Text Extraction and Preprocessing: N-grams
• Text extraction and preprocessing: stopword removal Text extraction and preprocessing: voting
• Text extraction and preprocessing: Lemmatization Text extraction and preprocessing: POS tagging
• Text Extraction and Preprocessing: Named Entity Recognition Workflow for NLP Processes
• Demo: Brown Corpus Processing Practical: Wiki Corpus Structuring Sentences: Syntax Rendering Syntax Tree
• Structuring Sentences: Chunking and Chunk Parsing NP and VP Chunk and Parser
• Structuring Sentences: Chinking Context Free Grammar (CFG) Demo: Twitter Sentiments Internship: Sentiment airline
• Main results
• Final project: Football World Cup

Projects covered:

Project 1: Uber Price Prediction

Design an algorithm that predicts a passenger's price.

Uber wants to improve the accuracy of its fare prediction model. Help Uber by choosing the best data and AI technologies to build the next generation model.

Project 2: Mercedes-Benz greener manufacturing

Reducing the time a Mercedes-Benz spends on the test bench.

Mercedes-Benz wants to shorten the time models spend on the test bench to get them to the marketing phase faster. Build and optimize a machine learning algorithm to solve this problem.

Project 3: Amazon.com - Employee Access

Design an algorithm to accurately predict access rights for Amazon employees. Use data about Amazon employees and their access rights to build a model that automatically determines access rights when employees enter and leave a role within Amazon.

Project 4: Income Qualification

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 build and improve the accuracy of the dataset using a random forest classifier.