Comprehensive Data Science Course for Beginners & Professio

This ad doesn't have any photos.

"GlassKonnect | Top Data Science Training & Assistance | Comprehensive Data Science Course for Beginners & Professionals"

Comprehensive Data Science Course for Beginners & Professionals
Unlock Your Future in Data Science with GlassKonnect — a premier platform offers Top Data Science Training and Assistance for graduates, beginners, and working professionals. Whether you are starting your journey or up skilling for advanced roles, our comprehensive Data Science course is designed to transform you into an industry-ready expert.

Course Description: Data Science

Data Science is an interdisciplinary field that leverages statistical methods, algorithms, and technology to extract insights and knowledge from structured and unstructured data. In today’s data-driven world, the significance of Data Science Course cannot be overstated. It empowers organizations across various industries—such as healthcare, finance, marketing, and technology—to make informed decisions, optimize processes, and innovate solutions.

CURRICULUM
Beginner Python
• Lists
• Tuples
• Dictionraies
• Sets
• IF-else statements
• Loops (for, while)
• Break
• Continue
• Control flow
• Funtcions
• Modules
• Packages
• Exceptional Handling
• Try-except blocks
• oop, class, Object
• Inheritance
• polymorphism
• Encapsulation
• Jupyter Notebooks

Neural Networks
• Artificial Neural Network
• Deep Learning
• Perceptron
• Activation Function
• Weight

• Bias
• Layer (Input, Hidden, Output)
• Feedforward Neural Network
• Backpropagation
• Loss Function
• Optimizer
• Single-Layer Perceptron
• Multi-Layer Perceptron
• Autoencoder
• Sigmoid
• Hyperbolic Tangent (tanh)
• Rectified Linear Unit (ReLU)
• Leaky ReLU
• Softmax
• Gradient Descent
• Stochastic Gradient Descent (SGD)
• Mini-Batch Gradient Descent
• Learning Rate
• Batch Normalization
• Regularization (L1, L2)
• Dropout
• Fine-tuning
• Cross-Entropy Loss
• TensorFlow
• Keras
• Explainable AI (XAI)
• Interpretability in Neural Networks
• Gradient Check
• Vanishing Gradient
• Exploding Gradient
• GPU
• TPU (Tensor Processing Unit)
• Vanishing Gradient Problem
• Exploding Gradient Problem

Introduction to ML & NN

• Supervised Learning
• Unsupervised Learning
• Semi-supervised Learning
• Regression
• Classification
• Ridge and Lasso Regression
• Regularisation
• Performance Metrics
• Confusion Matrix
• F1-score
• Receiver operating characteristic (ROC AUC)
• Accuracy
• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)
• Mean Absolute Percentage Error (MAPE)
• Mean Absolute Error (MAE)
• Gradient Descent
• Stochastic Gradient Descent (SGD)
• Mini-Batch Gradient Descent
• Learning Rate

ML Supervised Algorithms
• Linear Regression
• Polynomial Regression
• Logistic Regression
• Support Vector Machines (SVM)
• Decision Trees
• Bagging/Boosting
• Random Forest
• Gradient Boosting
• XGBoost Naive Bayes

ML Unsupervised Algorithms

• Clustering Analysis
• K-Means Clustering
• Elbow Method
• K-Means++
• Hierarchical Clustering
• Agglomerative clustering
• Silhouette score
• Within-Cluster Sum of Squares (WCSS)
• DBSCAN
• Anomaly Detection
• Outlier Detection
• Isolation Forest
• Gaussian Mixture Model (GMMs)
• Elliptic Envelope
• Local Outlier Factor (LOF)
• One-class-SVM
• Dimensionality Reduction
• Feature Extraction
• Eigenvalues/Eigenvectors
• Eigenvalues decomposition
• Princi