Machine Learning – ML
Divine Tech Skills is at the forefront of the technological revolution, offering cutting-edge services in Artificial Intelligence (AI) and Machine Learning (ML). Our expertise in these domains enables us to deliver innovative solutions that drive business transformation, enhance decision-making, and provide a competitive edge for our clients.
Artificial Intelligence involves simulating human intelligence in machines programmed to think, learn, and solve problems autonomously. At Divine Tech Skills, we leverage AI to help businesses:
- Automate Processes: Implement intelligent automation solutions to streamline operations, reduce manual effort, and boost efficiency.
- Enhance Customer Experience: Develop AI-powered customer service applications like chatbots and virtual assistants to provide personalized and timely support.
- Improve Decision Making: Use AI algorithms to analyze vast data sets and generate insights that guide strategic business decisions.
- Innovate Products and Services: Create smart products and services that adapt to user needs and market trends through continuous learning and improvement.
Module 1: Overview of AI and Machine Learning
Lesson 1: Understanding AI
- Overview and History of AI
- Types of AI: Narrow vs General AI
Lesson 2: Introduction to Machine Learning
- Key Concepts and Definitions
- Supervised, Unsupervised, and Reinforcement Learning
Module 2: Python for AI & ML Fundamentals
Lesson 1: Python Basics
- Variables, Data Types, Control Structures
- Functions and Modules
Lesson 2: Data Manipulation with Pandas
- DataFrames, Series, Indexing
- Data Cleaning and Transformation
Lesson 3: Data Visualisation
- Plotting with Matplotlib, Seaborn
- Interactive Visualisations with Plotly
Module 3: ML Statistics & Probability Essentials
Lesson 1: Descriptive Statistics
- Central Tendency & Dispersion
- Data Distributions
Lesson 2: Probability Theory
- Probability Basics
- Probability Distributions
Lesson 3: Inferential Statistics
- Hypothesis Testing
- Confidence Intervals & p-values
Module 4: Learning with Supervision
Lesson 1: Regression Analysis
- Linear Regression
- Multiple Linear Regression
Lesson 2: Classification Algorithms
- Logistic Regression
- Decision Trees & Random Forests
- Support Vector Machines (SVM)
Lesson 3: Model Evaluation
- Metrics: Accuracy, Precision, Recall, F1 Score
- Cross-Validation
Module 5: Self-Learning Models
Lesson 1: Clustering Techniques
- K-means Clustering
- Hierarchical Clustering
Lesson 2: Dimensionality Reduction
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
Lesson 3: Association Rule Learning
- Apriori Algorithm
- Market Basket Analysis
Module 6: Expert Machine Learning Methods
Lesson 1: Ensemble Methods
- Bagging, Boosting, Stacking
- GBM & XGBoost
Lesson 2: Feature Engineering
- Feature Selection & Extraction
- Handling Categorical Data & Missing Values
Lesson 3: Model Optimisation
- Hyperparameter Tuning
- Grid & Random Search
Module 7: Basics of Deep Learning
Lesson 1: Neural Networks
- Introduction to Neural Networks, Activation & Loss Functions
Lesson 2: Deep Learning Frameworks
- TensorFlow, Keras, Building & Training Neural Networks
Lesson 3: Convolutional Neural Networks (CNNs)
- CNN Architecture & Image Classification
Lesson 4: Recurrent Neural Networks (RNNs)
- RNN Architecture & Sequence Modelling
Module 8: Understanding Natural Language Processing (NLP)
Lesson 1: Introduction to NLP
- Text Preprocessing: Tokenization & Normalization
Lesson 2: NLP with Python
- NLTK & SpaCy: Sentiment Analysis & Text Classification
Lesson 3: Advanced NLP Techniques
- Word Embeddings & Word2Vec, Transformers & BERT
Module 9: Reinforcement Learning
Lesson 1: Introduction to Reinforcement Learning
- Key Concepts: Agents, States, Actions, Rewards
- Markov Decision Processes (MDPs)
Lesson 2: RL Algorithms
- Q-Learning & SARSA
- Deep Q-Networks (DQN)
Lesson 3: RL Applications
- Game Playing
- Robotics
Module 10: AI & ML in Manufacturing
Lesson 1: Model Deployment
- Saving & Loading Models
- Deploying with Flask & FastAPI
Lesson 2: Scaling ML Solutions
- Using Cloud Platforms (AWS, Google Cloud, Azure)
- Containerization with Docker
Lesson 3: Monitoring & Maintenance
- Monitoring Performance
- Updating & Maintaining Models
Module 11: Ethical Practices and Responsible AI
Lesson 1: AI Ethics
- AI Bias and Fairness
- Privacy and Security
Lesson 2: Responsible AI Development
- Ethical AI Best Practices
- Responsible AI Tools and Frameworks
Module 12: Final Project
Project Planning & Execution
- Building a Complete AI/ML Solution
- Integrating Machine Learning Models
- Deploying AI/ML Solutions to Production
Module 13: Final Evaluation
Course Feedback, Exam, Certification & Career Support
- Core Concepts: Understand the fundamentals of machine learning, including algorithms, data processing, and model training.
- Hands-on Experience: Practical projects to apply machine learning techniques to real-world problems.
- Advanced Techniques: Learn about supervised and unsupervised learning, neural networks, and deep learning.
- Expert Instructors: Guidance from experienced professionals in the field of machine learning.
- Career Support: Job-ready skills, resume building, and interview preparation.
- Flexible Learning: Online access with flexible scheduling for working professionals.
