ML Description

This Machine Learning equips the participants with the knowledge and hands-on skills required for certification and job competency in machine learning. The demand for machine learning skills is growing quickly. You will become proficient with machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer. By making it possible to quickly, cheaply, and automatically process and analyze huge volumes of complex data, machine learning is critical to countless new and future applications. Machine learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection, and even self-driving cars. Our expert trainer will ensure that your learning goals are achieved in this intensive training session. Our course content includes practice tests and exam prep questions. We also provide access to our online training course for this certification training through e-learning / self-paced training to enable you to start your exam preparation as soon as you confirm your participation in this training course. Our accredited courseware, interactive training provided by an industry expert with hands-on Projects experience, simulated tests, coupled with e-learning access will prepare you to clear the Machine Learning certification exams on your first attempt. Take the first step towards exploring an opportunity to become a Machine Learning certified professional. Prepare for the Machine Learning Certification exam and fast-track your career as a Machine Learning practitioner.

• The emergence of Artificial Intelligence
• Recommender Systems
• Relationship between Artificial Intelligence, Machine Learning, and Data Science
• Definition and Features of Machine Learning
• Machine Learning Approaches
• Machine Learning Techniques
• Applications of Machine Learning

• Data Exploration
• Seaborn
• Data Wrangling
• Missing Values in a Dataset
• Data Manipulation

• Supervised Learning Flow
• Types of Supervised Learning
• Types of Classification Algorithms
• Types of Regression Algorithms
• Accuracy Metrics
• Cost Function
• Evaluating Coefficients
• Challenges in Prediction
• Logistic Regression
• Sigmoid Probability
• Accuracy Matrix

• Regression
• Factor Analysis
• Principal Component Analysis (PCA)
• First Principal Component
• Eigenvalues and PCA
• Demo: Feature Reduction
• Linear Discriminant Analysis
• Maximum Separable Line

• Overview of Classification
• Classification Algorithms
• Decision Tree
• Random Forest Classifier- Bagging and Bootstrapping
• Decision Tree and Random Forest Classifier
• Demo: Horse Survival
• Naive Bayes Classifier
• Steps to Calculate Posterior Probability
• Support Vector Machines
• Linear SVM: Mathematical Representation
• Non-linear SVMs
• The Kernel Trick

• Example and Applications of Unsupervised Learning
• Clustering
• Hierarchical Clustering
• K-means Clustering
• Optimal Number of Clusters

• Time Series Pattern Types
• White Noise
• Stationarity
• Removal of Non-Stationarity
• Time Series Models
• Steps in Time Series Forecasting

• Ensemble Learning Methods
• Working of AdaBoost
• AdaBoost Algorithm and Flowchart
• Gradient Boosting
• XGBoost
• Model Selection
• Common Splitting Strategies

• Purposes and Paradigm of Recommender Systems
• Collaborative Filtering
• Association Rule Mining
• Association Rule Mining: Market Basket Analysis
• Association Rule Generation: Apriori Algorithm