Machine Learning Training

Hello! I'm İbrahim ÖZTÜRK.

If you’re looking to step into the world of machine learning, you’re in the right place! I have prepared a comprehensive, understandable, and hands-on machine learning training course just for you. This six-month program, totaling 90 hours, will provide you with a broad range of knowledge from the fundamentals of machine learning to advanced topics.

Training Content:

• Total Course Duration: 96 hours
• Total Training Duration: 6 months
• Participation Options: Online or in-person
• Duration of Each Class: 45 minutes
• Weekly Class Hours: 2 days per week, totaling 4 class hours per week
• Payment Methods: Monthly installments or one-time total payment for package training
• Training Fee: 1121,82 €
• Package Training Full Payment Fee: 1050 €

Training Topics:

Introduction to Machine Learning

• What is Machine Learning?
• Definitions and key concepts
• History and evolution of machine learning
• Types of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
• Machine Learning Process
  • Data collection and preparation
  • Model selection and training
  • Model evaluation and improvement

Python and Data Science Basics

• Python Programming Language
  • Python installation and basic features
  • Using Jupyter Notebook
• Data Science Libraries
  • NumPy: N-dimensional arrays and mathematical operations
  • Pandas: Data structures and data processing
  • Matplotlib and Seaborn: Data visualization
• Data Preprocessing
  • Handling missing data
  • Data normalization and standardization
  • Encoding categorical data

Data Visualization and Exploratory Data Analysis (EDA)

• Data Visualization Techniques
  • Histograms, distribution plots, box plots
  • Line plots and heatmaps
  • Distribution and relationships: scatter plots, pair plots
• Exploratory Data Analysis (EDA)
  • Data summarization and descriptive statistics
  • Identifying patterns and anomalies
  • Feature engineering: Creating new features

Supervised Learning

• Regression Analysis
  • Simple Linear Regression: Model building, accuracy assessment
  • Multiple Linear Regression: Working with multiple variables
  • Improving regression models and accuracy metrics
• Classification
  • Logistic Regression: Binary classification problems
  • Naive Bayes: Statistical classification
  • K-Nearest Neighbors (KNN): Neighbor-based classification
  • Support Vector Machines (SVM): Classification and margin optimization
  • Decision Trees and Random Forests: Tree-based classification and ensemble methods

Unsupervised Learning

• Clustering Algorithms
  • K-means Clustering: Determining the number of clusters and application
  • Hierarchical Clustering: Creating and applying dendrograms
• Dimensionality Reduction
  • PCA (Principal Component Analysis): Reducing data dimensions
  • t-SNE: Visualizing high-dimensional data
• Anomaly Detection
  • Techniques and applications for anomaly detection
  • Isolation forests and Local Outlier Factor (LOF)

Reinforcement Learning and Deep Learning Basics

• Reinforcement Learning
  • Core concepts: Agent, environment, reward
  • Q-Learning and Deep Q-Network (DQN)
  • Model-free and policy learning
• Introduction to Deep Learning
  • Neural networks: Basic building blocks and principles
  • Keras and TensorFlow: Building and training simple neural networks
  • Activation functions: ReLU, Sigmoid, Tanh

Model Evaluation and Improvement

• Model Evaluation
  • Accuracy, Precision, Recall, F1 Score: Performance metrics
  • ROC-AUC Curve: Evaluating classification performance
• Model Improvement
  • Hyperparameter optimization: Grid Search, Random Search
  • Overfitting and Underfitting: Identifying and addressing issues
  • Cross-Validation: Testing model generalizability

Advanced Machine Learning Techniques

• Ensemble Methods
  • Bagging: Model diversity and improvement
  • Boosting: Performance enhancement with XGBoost and LightGBM
• Model Interpretability
  • SHAP (SHapley Additive exPlanations) values for model explainability
  • LIME (Local Interpretable Model-agnostic Explanations) for decision analysis

Real-World Projects and Applications

• Project 1: Data Analysis and Regression Modeling
  • Building and evaluating a regression model using real datasets
• Project 2: Classification Problem Solving
  • Developing and optimizing models for binary or multi-class classification problems
• Project 3: Clustering and Data Visualization
  • Applying clustering algorithms and using data visualization techniques
• Capstone Project: Comprehensive Project
  • Developing an extensive machine learning project using all the knowledge acquired
  • Project presentation and evaluation

Project Presentations and Evaluation

• Project Presentations:
  • Effectively presenting your projects and receiving feedback
• Project Evaluation:
  • Reviewing, assessing, and providing constructive feedback on other participants' projects

Why Should You Choose This Training?

• Comprehensive Training: Gain extensive knowledge from the fundamentals to advanced techniques in machine learning.
• Practical Focus: Acquire hands-on experience with projects designed to solve real-world problems.
• Experienced Instructor: Benefit from my expertise in machine learning and data science for the best learning experience.

Don’t miss this opportunity to build a strong foundation in machine learning and enhance your skills in this field. Our training will equip you with both theoretical knowledge and practical skills to develop solutions for real-world problems.

I look forward to your participation!