Machine Learning Training

Introduction to Machine Learning

• Understanding the basics of machine learning and its applications in various domains
• Exploring supervised learning, unsupervised learning, and reinforcement learning techniques

Data Preprocessing and Exploratory Data Analysis (EDA)

• Cleaning and preprocessing raw data for machine learning tasks
• Performing exploratory data analysis to gain insights and identify patterns in data

Supervised Learning Algorithms

• Overview of popular supervised learning algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks
• Hands-on experience in implementing and fine-tuning supervised learning models for classification and regression tasks

Unsupervised Learning Algorithms

• Introduction to unsupervised learning techniques, including clustering and dimensionality reduction
• Implementing algorithms like K-means clustering, hierarchical clustering, and principal component analysis (PCA) for data exploration and pattern recognition

Model Evaluation and Validation

• Understanding the principles of model evaluation, cross-validation, and performance metrics
• Evaluating model performance and selecting the best model for deployment using appropriate validation techniques

Feature Engineering and Selection

• Techniques for feature extraction, transformation, and selection to improve model performance
• Handling categorical variables, missing data, and outliers in feature engineering processes

Deep Learning and Neural Networks

• Introduction to deep learning concepts and architectures
• Building and training neural networks using frameworks like TensorFlow and Keras for advanced machine learning tasks