Course description
Machine learning (ML) has become indispensable in various industries and applications, transforming how businesses operate and improving decision-making processes. Here are key reasons why machine learning is essential in the real world:
- Automation and Efficiency
- Predictive Analytics
- Personalization and Customer Insights
- Healthcare and Medical Diagnostics
- Natural Language Processing (NLP)
- Image and Video Analysis
- Financial Services and Fraud Detection
- Machine learning (ML) is a subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models capable of learning from and making predictions or decisions based on data.
- Here’s a comprehensive overview of machine learning, its types, applications, and impact:
- Machine learning continues to revolutionize industries and everyday life, driving innovations in automation, decision-making, and personalized experiences.
- As advancements in algorithms, computing power, and data availability accelerate, the potential for ML to tackle complex problems and create value across diverse domains grows exponentially.
- At Stellaraa, we offer specialized courses to equip individuals with the skills and knowledge needed to thrive in the dynamic field of machine learning.
- Overview of Machine Learning
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Applications of Machine Learning in Various Industries
- Setting Up the Development Environment (Python, Jupyter Notebooks, Anaconda)
- Understanding Data Types and Structures
- Data Cleaning and Handling Missing Values
- Exploratory Data Analysis (EDA) and Visualization
- Feature Engineering and Selection
- Normalization and Standardization of Data
- Regression
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Evaluation Metrics (MSE, RMSE, R² Score)
- Classification
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Decision Trees and Random Forests
- Naive Bayes
- Evaluation Metrics (Accuracy, Precision, Recall, F1 Score, ROC-AUC)
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Evaluation Metrics (Silhouette Score, Inertia)
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Ensemble Learning
- Bagging and Boosting (AdaBoost, Gradient Boosting, XGBoost)
- Stacking and Blending
- Model Selection and Hyperparameter Tuning
- Cross-Validation Techniques
- Grid Search and Random Search
- Bayesian Optimization
- Deep Learning Basics
- Introduction to Neural Networks
- Understanding the Basics of Deep Learning
- Building and Training Neural Networks with TensorFlow and Keras
- Text Processing and Feature Extraction
- Tokenization and Lemmatization
- Bag-of-Words and TF-IDF
- Text Classification
- Sentiment Analysis
- Spam Detection
- Advanced NLP Techniques
- Word Embeddings (Word2Vec, GloVe, FastText)
- Recurrent Neural Networks (RNNs) and LSTMs
- Transformers and BERT
- Model Evaluation and Validation
- Saving and Loading Models
- Introduction to Model Deployment
- Deploying Models using Flask and Docker
- Monitoring and Maintaining Models in Production
- Defining the Project Scope and Objectives
- Data Collection and Preparation
- Model Selection and Development
- Evaluation and Iteration
- Presentation and Reporting
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Keras
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XGBoost
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PyTorch
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Scikit-learn
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TensorFlow
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