Data Analysis, Machine Learning, and AI
- Description
- Curriculum
- Reviews
This course explores the fundamentals of data analysis, machine learning, and artificial intelligence (AI). Students will learn to analyze data, build predictive models, and understand the concepts underlying AI technologies.
Course Objectives
Understand data analysis techniques and tools.
Learn machine learning algorithms and their applications.
Gain insights into AI concepts and frameworks.
Develop practical skills through hands-on projects.
-
1
Duration: 3 monthsSchedule: 3 times a week
Month 1: Data Analysis and Exploration
Week 1-2: Introduction to Data Analysis
· Overview of data science and its applications
· Understanding data types and data structures (structured vs unstructured data)
· Introduction to Python for data analysis (NumPy, Pandas)
-
2
Week 3: Data Cleaning and Preprocessing
· Handling missing data
· Data transformation techniques
· Data normalization and scaling
· Introduction to data visualization (Matplotlib, Seaborn)
-
3
Week 4: Exploratory Data Analysis (EDA)
· Statistical measures and data distribution
· Correlation analysis and hypothesis testing
· Visualizing data trends and patterns
-
4
Week 5: Working with Databases
· Introduction to SQL for querying data
· Connecting Python to databases
· Data extraction from external sources (APIs, CSV, JSON, etc.)
-
5
Week 6: Capstone Data Analysis Project
· Analyzing a dataset using the techniques learned
· Presenting insights through visualizations and reports
-
6
Month 2: Machine Learning Fundamentals
Week 7: Introduction to Machine Learning
· Understanding supervised vs unsupervised learning
· Overview of machine learning workflows and tools (Scikit-learn)
· Model evaluation and performance metrics (accuracy, precision, recall, F1-score)
-
7
Week 8-9: Supervised Learning Algorithms
· Linear regression and classification algorithms (logistic regression, decision trees)
· K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)
· Training and testing models, overfitting, and cross-validation
-
8
Week 10: Unsupervised Learning Algorithms
· Clustering algorithms (K-means, hierarchical clustering)
· Dimensionality reduction techniques (PCA, t-SNE)
· Real-world use cases of unsupervised learning
-
9
Week 11: Feature Engineering and Model Optimization
· Feature selection and importance
· Hyperparameter tuning and model optimization
· Introduction to ensemble learning (Random Forest, Gradient Boosting)
-
10
Month 3: Artificial Intelligence and Advanced Machine Learning
Week 12: Introduction to Artificial Intelligence
· Overview of AI and its applications
· Difference between AI, ML, and Deep Learning
· Introduction to natural language processing (NLP)
-
11
Week 13-14: Neural Networks and Deep Learning
Week 13-14: Neural Networks and Deep Learning
· Fundamentals of neural networks
· Introduction to TensorFlow/Keras for building neural networks
· Deep learning architectures: CNNs and RNNs
-
12
Week 15: AI in Action
· AI applications: Image recognition, NLP, and robotics
· Ethical considerations in AI development
· Deploying machine learning models in production
-
13
Week 16: Capstone AI Project
· Developing a machine learning or AI model from scratch
· Presenting model performance and deployment plans
