Artificial Intelligence & Machine Learning
Join the bots and learn how to make machines do the work for you. Let’s build a smarter future together.
About this Program
Embark on a thrilling journey into the realms of Artificial Intelligence and Machine Learning! You’ll unravel the mysteries behind cutting-edge technologies. Dive deep into the fascinating world of AI and ML through interactive lessons, hands-on projects, and mind-bending challenges. In this program, we will be learning Python and then studying the basics to advance of machine learning algorithms and statistical methods. Understand how to clean and prepare data for analysis, and use popular libraries like NumPy, Pandas, and Scikit-learn. Then, we will dive deeper into neural networks, deep learning, and natural language processing. Finally, explore advanced topics like computer vision, reinforcement learning, and AI ethics. You will be working on Projects, and discussing with your peers to stay up-to-date on the latest developments!
Modules of Curriculum
- What is Artificial Intelligence?
- What is Machine Learning?
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Introduction to the course curriculum and learning objectives
- Installing Python and Anaconda
- Introduction to Python syntax, data types, and variables
- Basic arithmetic operations in Python
- Conditional statements (if-else)
- Looping statements (for and while loops)
- Nested loops and loop control statements
- Lists: indexing, slicing, and manipulation
- Tuples: creating, accessing, and modifying
- Dictionaries: key-value pairs and operations
- Sets: creating, adding, and removing elements
- NumPy arrays: creation, indexing, and slicing
- Mathematical operations with NumPy arrays
- Array manipulation and reshaping
- Pandas data structures: Series and DataFrame
- Reading data from different sources
- Data indexing, selection, and filtering in Pandas
- Handling missing values
- Removing duplicates
- Dealing with outliers
- Feature scaling and normalization
- What is Machine Learning?
- Steps in the ML process: Data preprocessing, model training, evaluation, and prediction
- Supervised learning, unsupervised learning, and reinforcement learning
- Introduction to Linear Regression
- Simple Linear Regression: concepts, implementation, and evaluation
- Multiple Linear Regression: concepts, implementation, and evaluation
- Introduction to Logistic Regression
- Logistic Regression for binary classification: concepts, implementation, and evaluation
- Multinomial Logistic Regression: concepts, implementation, and evaluation
- Introduction to Decision Trees
- Decision Tree algorithm: concepts, implementation, and evaluation
- Ensemble methods: Introduction to Random Forests
- Introduction to Naive Bayes algorithm
- Types of Naive Bayes classifiers: Gaussian, Multinomial, and Bernoulli
- Implementing Naive Bayes algorithm for classification
What you'll learn
Who Can Enroll
Frequently asked questions
Some prior knowledge of programming concepts, such as data structures, algorithms, and object-oriented programming, is usually required. Some courses may also require a basic understanding of mathematics, such as linear algebra and statistics.
These roles can be found in industries such as technology, healthcare, finance, e-commerce, robotics, automotive, and more:
- AI Engineer/Developer
- Machine Learning Engineer
- Data Scientist
- Research Scientist (AI/ML)
- AI Consultant
- Natural Language Processing (NLP) Engineer
- Computer Vision Engineer
- AI Ethics Specialist
- AI Project Manager
- AI Product Manager
Some tips include practicing regularly, asking questions, collaborating with peers, seeking help from instructors and online resources, and working on real-world projects to build a portfolio.
Once you successfully complete the entire course, you will get a certificate of completion. This certificate will help you will build your credibility as a AI/ML Engineer.
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