Machine Learning Training in Hyderabad
Machine Learning Training in Hyderabad makes you explore different Machine Learning concepts and how it plays an efficient role in transforming the digital world. Machine learning course in Hyderabad offers the knowledge and skills required to become a Machine learning professional and acquire the power of Machine Learning, the administration, and emerging field in the industry. Machine Learning course from Vagdevi Technologies helps you master in-depth Machine learning concepts including time series modeling, classification, regressions, supervised & unsupervised learning, working with real-time data, and more practical and theoretical classes.
Machine Learning Course Overview
Machine Learning Course in Hyderabad
Machine Learning algorithms are an essential part of data science. Machine Learning certification in Hyderabad is crafted based on the International Association of Business Analytics Certification to offer an application and theory of famous machine learning algorithms in deep, supervised, and unsupervised learning. Machine learning training in Hyderabad enables you to learn different algorithms, how to optimize, work, benefits and drawbacks with their practical applications. Once you complete the Machine Learning certification course from Vagdevi Technologies, you receive the globally accredited Machine Learning training course completion certificate.
Machine Learning Training in Ameerpet
Experts develop the Machine Learning course curriculum in Hyderabad to match up the current industry requirements in Machine Learning. The aspirants learn advanced machine learning concepts like naïve Bayes classification, decision tree algorithms, support vector machines, logistic regressions, K-Means clustering, and more. Machine learning certification in Hyderabad helps aspirants gain all the advanced skills in machine learning technology, and it means they can easily get hired on all reputed organization worldwide. Enroll now for the first-class machine learning course certification in Hyderabad program from Vagdevi Technologies and master the hottest job requirements and Machine Learning skills.
Machine Learning Curriculum
- What is Machine Learning?
- Difference between Supervised Learning and Unsupervised Learning?
- Difference between Regression and Classification Models?
- Relationship between variables: Regression (Linear, Multivariate Linear Regression) in prediction
- Hands on Linear and Multiple Regression using a use case
- Understanding the summary output of Linear Regression
- Residual Analysis
- Identifying significant features, feature reduction using AIC, multi-collinearity check, observing influential points, etc.
- Hypothesis testing of Regression Model
- Confidence intervals of Slope
- R-square and goodness of fit
- Influential Observation – Leverage
- Polynomial Regression
- Categorical Variable in Regression
- Logistic Regression Intuition
- Understanding Logit Function
- Hands-on Python Session on Logistic Regression using business case
- Measuring the Evaluation Metrics – Confusion Metrics, Accuracy, Precision, recall and ROC Curve.
- Review probability distributions, Joint and conditional probabilities
- Model Assumptions, Probability estimation
- Required data processing
- Feature Selection
- Classifier
- Introduction to dimensionality reduction and it’s necessity
- Background: Eigen values, Eigen vectors, Orthogonality
- Principal components analysis (PCA)
- Feature Extraction
- Advantage and application of Dimensionality reduction.
- Trend analysis
- Cyclical and Seasonal analysis
- Smoothing; Moving averages; Auto-correlation; ARIMA
- Application of Time Series in financial markets.
- Decision nodes and leaf nodes
- Variable Selection, Parent and child nodes branching
- Stopping Criterion
- Tree pruning and Depth of a tree
- Overfitting
- Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
- Regression using decision tree
- Interpretation of a decision tree using If-else
- Pros and cons of a decision tree
- Accuracy estimation using cross-validation
- What is KNN and why do we use it?
- KNN-algorithm and regression
- Curse of dimensionality and brief introduction to dimension reduction
- KNN-outlier treatment and anomaly detection
- Cross-Validation
- Pros and cons of KNN
- Linear learning machines and Kernel space, making kernels and working in feature space.
- Hands on example of SVM classification and regression problems using a business case in Python.
- Introduction to Ensemble
- Bias and Trade-off
- Bagging & boosting and its impact on bias and variance
- Random forest
- Gradient Boosting
- XGBoost
Online Machine Learning Training
Microsoft, Columbia, Caltech and other major universities and institutions offer introductory courses and tutorials in machine learning and artificial intelligence. Gain a stronger understanding of the major machine learning projects with helpful examples. Learn how to build complex data models, explore data classification and regression, clustering methods, popular machine learning algorithms, sequential models, matrix factorization and explore other key parts of this exciting field. Earning a machine learning certification can help advance your career.
