• Clarification of basic concepts (AI, Machine Learning, Data Science, Big Data, etc.)
• Machine Learning Process
• Overview of Machine Learning Tools, Tasks and Technologies
• Machine Learning team structure, clarification of tasks (Machine Learning Engineer, Data Scientist, Data Engineer, MLOps Engineer, etc.)
• Real-world examples for machine learning

Tasks of machine learning

• Business goals, requirements, insights
• Supervised learning
• Classification
• Regression
• Time Series Forecasting
• Model Evaluation and Selection
• Feature Engineering and Selection
• Unsupervised learning
• Clustering
• Dimensionality Reduction
• Association Rule Mining
• Reinforcement Learning

• Data Sources and Types
• Data Cleaning and Preprocessing
• Data Integration and Transformation

Exploratory Data Analysis with Python/Pandas/Matplotlib

• Data Visualization
• Descriptive Statistics
• Data Exploration Techniques

Case study – Programming in Python, Pandas, Matplotlib

• Linear Regression
• Logistic Regression
• Decision Tree
• Ensemble Models (Random Forest, etc.)
• Boosting (AdaBoost, Gradient Boost, etc.)
• KNN
• K-Means
• PCA

Coding machine learning algorithms with Scikit-learn

Case study – Coding machine learning algorithms

Evaluating Machine Learning Algorithms

• Metrics (Accuracy, F scores, ROC/AUC, etc.)

Optimising Machine Learning Algorithms

• Cross validation with scikit-learn
• Optimisation with Optuna

Case study: Evaluation and optimisation with scikit-learn and Optuna

• Neural Networks
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transfer Learning
• Hyper Parameter Tuning
• Coding with Tensorflow/Keras

Case study: Deep learning with Python and Tensorflow

Big Data and Cloud Computing

• Hadoop and Spark
• Cloud Computing Platforms
• Data Streaming and Real-time Analytics

Natural Language Processing (NLP) with Python/NLTK/Spark NLP

• Tasks of NLP
• Text Preprocessing and Tokenization
• Sentiment Analysis
• Text Classification and Clustering
• Language Models and Text Generation

Case study: NLP with Python

Time Series Analysis with Python

• Tasks of Time Series Analysis
• Time Series Decomposition
• Smoothing and Forecasting
• ARIMA and Seasonal Models

Computer Vision with Python/OpenCV/Tensorflow/Keras

• Tasks of Computer Vision
• Object Detection, Image Classification, Object Localization, Instance Segmentation
• R-CNN, Fast R-CNN, Faster R-CNN
• YOLO (You Only Look Once)
• SSD (Single-Shot Detector)

Case study: Computer Vision with Python

• Encoder-Decoder
• GANs (Generative Adversarial Networks)
• LLM (Large Language Models)
• Transformer Models and BERT

Case study: Create simple generative AI

Motivation, DevOps and MLOps principles in Python

• Why MLOps? 
• Examples for MLOps
• DevOps principles, lifecycle
• MLOps principles
• MLOps system design process
• MLOps levels
• MLOps stack, tools
• Tools for DevOps (REST API, Docker, exception handling, logging, documentation, coding style, etc.)

Case study – Creating REST API

ML Development with low code/Deployment

• Tool overview
• Principles of ML Development and deployment
• Advantages of Pycaret
• Advantages of MLFlow

Case study – Pycaret and MLFlow programming

MLOps tools – Monitoring and alerts

• Principles of Monitoring and alerts
• Tools for Monitoring and alerts

Case study – Coding with Streamlit, Evidently AI

MLOps tools – Pipeline Orchestration

• Principles of Pipeline Orchestration
• Tools for Pipeline Orchestration
• Introduction to Airflow

Case study – Coding with Airflow