Machine Learning Training - Best IT and Software Training Institute Delhi | Cooperate Training in Delhi

MACHINE LEARNING COURSE

Machine Learning Training Program

Description

Our Machine Learning Training Program is created by our team of experts to make learning easy and accessible. We aim to simplify complex theories, concepts, and assignments for you. We’ll guide you step-by-step into the world of Machine Learning. Through our training, you will gain new skills and deepen your understanding of this challenging yet rewarding field.

Why to Choose Us:

Pre-Requisite

You need basic programming and scripting skills.
A graduation degree is suggested.
No prior experience is needed for the course.

Curriculum

12 Modules • 48-50 Hours Course Duration

Introduction to Machine Learning

6 lectures

Introduction To Machine Learning

Importance Of Machine Learning

How did Machine Learning work?

Relationship Between Artificial Intelligence, Machine Learning, and Data Science

Different Categories of Machine Learning: Supervised, Unsupervised and Reinforcement

Applications of Machine Learning

Supervised Leering - Linear Regression

8 lectures

Simple Linear Regression

Multiple Linear Regression

Performance Metrics: Accuracy Scores, R-squared, and Adjusted R-squared (used to compare models with different numbers of independent variables)

Methods for Feature Selection: Univariate Selection, Feature Importance, Recursive Feature Elimination (RFE)

Parameter Tuning and Model Evaluation

Data Transformation and Normalization

Ridge and Lasso Regression (L1 and L2 Regularization)

Logistic Regression

4 lectures

Concept of Logistic Regression

Univariate Logistic Regression

Multivariate Logistic Regression: Model Building and Evaluation

Dealing with Categorical Independent Variable – Hot Encoding Vs Dummy Variable

Decision Tree

Concept of Decision Trees

Importance and Usage of Ginny And Entropy

Information Gain

Visualizing Decision Trees Nodes and Splits

Working of The Decision Tree Algorithm

Extending Decision Trees to Regressing Problem

Evaluating Decision Trees Models: Accuracy, Precision, Recall, Confusion Matrix

7 lectures

Classification - k Nearest Neighbor (KNN)

4 lectures

Classification and Regression

Application, Advantages and Disadvantages

Distance Metric – Euclidean, Manhattan, Chebyshev, Minkow

Measuring accuracy using Cross-Validation, Stratified k-fold, Confusion Matrix, Precision, Recall, F1-score

Naive Bayes Classifier

2 lectures

The Bayes Theorem

The Gaussian Naïve’s Bayes Classifier – Assumptions of The Naïve Bayes Classifier, Functioning of The Naïve’s Bayes Algorithm

Classification SVM (Support Vector Machine)

7 Lectures

Classification and Regression

Separating line, Margin and Support Vectors

Linear SVC Classification

Polynomial Kernel – Kernel Trick

Gaussian Radial Basis Function (RBF)

Grid Search to tune hyper-parameters

Support Vector Regression

Unsupervised Learning - Clustering

5 lectures

Introduction to Clustering

K-Means Clustering: Linkage

Use of Elbow Curve & Silhouette Score

Hierarchical Clustering – Agglomerative & Divisive

Distance Matrix, Dendrogram

Density based Clustering

8 lectures

DBSCAN (Density based clustering) Technique

Density based clustering

Eps: Density

Core, Border and Noise points

Density edge and Density connected points

DBSCAN Algorithm

Hyper Parameters: MinPts and EpsA

Advantages and Limitations of DBSCAN

Principle Component Analysis - PCA

8 lectures

Noise in The Data and Dimensionality Reduction

Capturing Variance – The Concept of Principal Components

Assumptions in Using PCA

Eigenvectors and Orthogonality of Principal Components

What Is the Complexity Curve?

Advantages of Using PCA

The Working of The PCA Algorithm

Singular Value Decomposition

Ensemble Modelling

10 lectures

Popular Ensembles

Bagging

Bootstrap

Introduction to Random Forests

Feature Importance in Random Forests

Boosting

How Boosting Algorithm Works?

Adaboost

Gradient Boost

XGBoost

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