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Machine Learning and AI Fundamentals Course – 6 Weeks
USD $39.00 USD $249.00Price range: USD $39.00 through USD $249.00
This 6-week course is designed to provide a strong foundation in Machine Learning and AI. Through hands-on projects and real-world applications, you’ll learn how to use cutting-edge technologies to solve complex problems. Suitable for beginners and professionals looking to upskill, this course will empower you with the knowledge to advance your career in AI.
Aim
This 6-week course is designed to provide participants with a solid foundation in Machine Learning (ML) and Artificial Intelligence (AI). The course covers the fundamental concepts, algorithms, and techniques used in machine learning and AI, including supervised and unsupervised learning, model evaluation, and real-world applications. Participants will gain hands-on experience in applying these techniques using popular libraries like Scikit-learn and TensorFlow.
Program Objectives
- Understand the key concepts and algorithms in Machine Learning and AI.
- Learn how to build and train machine learning models for real-world tasks.
- Gain experience with supervised and unsupervised learning techniques.
- Apply machine learning techniques using Scikit-learn, TensorFlow, and Keras.
- Learn how to evaluate models, tune hyperparameters, and improve model performance.
Program Structure
Week 1: Introduction to Machine Learning and AI
- Overview of Machine Learning and AI: Definitions, types of machine learning (supervised, unsupervised, reinforcement learning).
- Understanding data preprocessing: feature extraction, cleaning, and normalization.
- Introduction to the Machine Learning workflow.
- Hands-on exercise: Basic data manipulation and exploration using Python and Pandas.
Week 2: Supervised Learning - Regression and Classification
- Introduction to supervised learning: Linear regression, logistic regression, and classification.
- Building regression models and predicting continuous outcomes.
- Building classification models and predicting discrete outcomes.
- Hands-on exercise: Implementing a regression model using Scikit-learn and evaluating its performance.
Week 3: Unsupervised Learning - Clustering and Dimensionality Reduction
- Introduction to unsupervised learning: Clustering, K-means, hierarchical clustering.
- Dimensionality reduction techniques: PCA (Principal Component Analysis) and t-SNE.
- Hands-on exercise: Implementing a K-means clustering algorithm and visualizing results.
Week 4: Model Evaluation and Hyperparameter Tuning
- Introduction to model evaluation: Cross-validation, training/testing split, confusion matrix.
- Metrics for classification and regression: accuracy, precision, recall, F1-score, MAE, RMSE.
- Hyperparameter tuning using GridSearchCV and RandomizedSearchCV.
- Hands-on exercise: Evaluating and tuning a classification model.
Week 5: Deep Learning Basics
- Introduction to neural networks and deep learning concepts.
- Understanding activation functions, backpropagation, and loss functions.
- Introduction to TensorFlow and Keras for deep learning models.
- Hands-on exercise: Building a simple neural network using TensorFlow/Keras for classification.
Week 6: Model Deployment and Real-World Applications
- Introduction to model deployment: Exporting models, making predictions, and creating APIs.
- Real-world applications of Machine Learning and AI: Healthcare, finance, e-commerce, and more.
- Final project: Deploying a machine learning model for a real-world problem.
Final Project
- Design and implement a complete machine learning solution to solve a real-world problem (e.g., building a recommendation system, classifying images, predicting house prices, etc.).
- Apply techniques learned throughout the course to complete the final project and deploy the solution.
Participant Eligibility
- Students and professionals in computer science, data science, and engineering fields.
- Anyone interested in learning machine learning techniques and applying them to solve real-world problems.
- Data analysts, data scientists, and machine learning enthusiasts looking to strengthen their foundational skills in AI and machine learning.
Program Outcomes
- Solid understanding of the core concepts and algorithms used in Machine Learning and AI.
- Hands-on experience with real-world machine learning tasks and problem-solving.
- Practical knowledge of model evaluation, hyperparameter tuning, and improving model performance.
- Skills to apply deep learning models using frameworks like TensorFlow and Keras for various tasks.
- Experience in deploying machine learning models to production environments.
Program Deliverables
- Access to e-LMS: Full access to course materials, case studies, and resources.
- Hands-on Projects: Develop machine learning solutions to real-world problems using Python and Scikit-learn.
- Final Project: Build and deploy a complete machine learning solution to a practical problem.
- Certification: Certification awarded after successful completion of the course and final project.
- e-Certification and e-Marksheet: Digital credentials provided upon successful completion.
Future Career Prospects
- Machine Learning Engineer
- Data Scientist
- AI Researcher
- Deep Learning Engineer
- Data Analyst
Job Opportunities
- AI and Data Science Companies: Implementing machine learning models to solve business challenges.
- Tech Firms: Developing AI-powered solutions using machine learning algorithms.
- Startups: Building intelligent systems with machine learning for innovative applications.
- Research Institutions: Conducting research on advanced machine learning models and techniques.
| Variation | E-Lms, Video + E-LMS, Live Lectures + Video + E-Lms |
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