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Artificial Intelligence with Machine Learning Certification
Course Duration- 180 Hourse | Internship Plus
Course Objectives:
Work on hands-on projects to apply AI and ML skills to industry-relevant challenges.
Master popular ML frameworks and tools like TensorFlow, PyTorch, and Scikit-learn.
Learn to analyze and interpret data to make data-driven decisions.
Understand ethical considerations and best practices in AI and ML applications.
Industry Readiness Certification Programs
Artificial Intelligence with Machine Learning Certification
The Artificial Intelligence with Machine Learning Certification provides a comprehensive understanding of AI principles and ML techniques to build intelligent systems. This program covers foundational concepts, advanced algorithms, and practical applications, including supervised and unsupervised learning, neural networks, natural language processing, and computer vision. Learners gain hands-on experience through real-world projects, preparing them to tackle industry challenges and innovate in the rapidly evolving AI landscape.
This Course is Suitable For
- Beginners: Basic computer literacy and a keen interest in AI are sufficient to start learning artificial intelligence and machine learning concepts.
- Students and Professionals: Ideal for individuals aiming to enhance their skills in data science, AI development, or transition into machine learning careers.
- Career Changers: Perfect for professionals from non-tech backgrounds who want to pivot into the AI and ML domain and gain in-demand skills for the future job market.
Course Syllabus Highlights:
Module 1: Python for Artificial Intelligence
- Introduction to Python: Syntax, data types, and control structures.
- Functions and modules: Creating reusable code
- Data manipulation with Python:
- Working with lists, dictionaries, and sets
- Introduction to libraries: NumPy and Pandas for data handling
- Visualization: Using Matplotlib and Seaborn for data visualization
- Basic file operations: Reading from and writing to files
- Tools Covered
- Python (with Anaconda or standalone)
- Jupyter Notebook for interactive coding and visualization
Module 2: Introduction to Artificial Intelligence:
- Definitions and concepts of AI, ML, and Deep Learning.
- Historical milestones in AI development.
- Applications of AI in various industries (e.g., healthcare, finance, autonomous vehicles)
- Current trends and future directions in AI
- Activities Covered:
- Class discussions on AI applications
- Case studies of successful AI implementations
Module 3: Machine Learning Basics
- Overview of Machine Learning and its importance
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Data preprocessing: Cleaning, normalization, and transformation
- Feature engineering: Selection and extraction
- Tools Covered:
- Python (using libraries like Pandas and NumPy)
- Jupyter Notebook for interactive coding
Module 4:Supervised Learning Algorithms
- Linear Regression: Understanding and implementation
- Logistic Regression: Binary classification and evaluation
- Decision Trees: Splitting criteria, overfitting, and pruning techniques
- Random Forests: Ensemble methods and their advantages
- Support Vector Machines (SVM): Hyperplanes and kernel methods
- Tools Covered:
- Scikitlearn for algorithm implementation
- Matplotlib and Seaborn for data visualization
- Jupyter Notebook for project development
Module 5: Unsupervised Learning Algorithms
- Clustering techniques:
- KMeans: Algorithm and implementation
- Hierarchical Clustering: Dendrograms and applications
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- tDistributed Stochastic Neighbor Embedding (tSNE)
- Tools Covered:
- Scikitlearn for implementing clustering algorithms
- Matplotlib/Seaborn for visualizing clustering results
- Jupyter Notebook for analysis
Module 6: Model Evaluation and Metrics
- Importance of model evaluation.
- Confusion Matrix: True positives, false positives, true negatives, false negatives
- Evaluation Metrics
- Accuracy
- Precision
- Recall
- F1 Score
- ROC Curve and Area Under the Curve (AUC)
- Tools Covered:
- Scikitlearn for model evaluation function
- Jupyter Notebook for reporting results
Module 7: Neural Networks and Deep Learning
- Basics of Neural Networks: Neurons, layers, and architecture
- Activation Functions: ReLU, Sigmoid, Softmax
- Loss Functions: Crossentropy and Mean Squared Error
- Optimization Techniques: Gradient Descent and its variants
- Introduction to Convolutional Neural Networks (CNNs) and their applications in image recognition
- Tools Covered:
- TensorFlow or PyTorch for building neural networks
- Keras (if using TensorFlow) for simplified neural network construction
- Jupyter Notebook for development and experimentation
Module 8: Advanced Machine Learning Techniques
- Ensemble Learning:
- Bagging: Random Forests
- Boosting: AdaBoost, Gradient Boosting, and XGBoost
- Introduction to Natural Language Processing (NLP):
- Text preprocessing: Tokenization, stemming, and lemmatization
- Basic NLP techniques: Sentiment analysis and text classification
- Tools Covered:
- Scikitlearn for ensemble methods
- NLTK or SpaCy for NLP tasks
- Jupyter Notebook for project development
Module 9: Model Deployment and Best Practices
- Strategies for model selection and hyperparameter tuning
- Best practices in machine learning: Cross validation and regularization techniques
- Model deployment methods: Creating APIs with Flask and containerization with Docker
- Tools Covered:
- Flask for web application development
- Docker for containerization
Module 10:Ethical Considerations in AI
- Understanding bias and fairness in AI systems
- Privacy concerns and ethical considerations in data collection and usage
- Overview of AI regulations and ethical frameworks
Module 11:Capstone Project and Case Studies
- Review of successful AI and ML applications in various domains
- Key challenges in AI implementation and future trends
Project Work:
- Group project: Develop an AI solution to a real-world problem, incorporating techniques learned throughout the course
- Tools Covered:
- Combination of Tools used in previous modules (Python, Scikitlearn, TensorFlow, etc.)
- Jupyter Notebook for project development
- Presentations:
- Final presentations showcasing project methodologies, results, and ethical considerations
What You'll Learn?
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Foundations of AI and ML
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Supervised and Unsupervised Learning
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Neural Networks and Deep Learning
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Natural Language Processing (NLP)
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Computer Vision
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Reinforcement Learning
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Model Evaluation and Optimization
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Hands-On Projects
-
Deployment of ML Models
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Ethical AI Practices
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Career-Ready Skills
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