Overview of Machine Learning & AI
Machine Learning (ML) and Artificial Intelligence (AI), including OpenAI, represent transformative fields at the intersection of computer science and data analysis. ML involves the development of algorithms and models that enable computer systems to learn and make predictions or decisions based on data patterns without explicit programming.
On the other hand, Artificial Intelligence encompasses broader concepts where machines mimic human cognitive functions, including learning, problem-solving, and decision-making. ML and AI technologies like ChatGPT 4 and MidJourney AI are rapidly advancing and finding applications in various industries, ranging from healthcare and finance to manufacturing and marketing.
The power of ML lies in its ability to uncover insights from vast datasets, while AI extends this capability to intelligent decision-making and problem-solving. Enroll in our Machine Learning and AI course in Tiruvannamalai at Ariyath Academy to kickstart your career in this dynamic field.
Syllabus
- • Overview of ML and AI concepts
- • Historical context and evolution
- • Applications in various industries
- • Basic statistical concepts
- • Probability theory and distributions
- • Introduction to Python
- • Key libraries for ML and AI (NumPy, Pandas, Scikit-learn)
- • Linear regression
- • Logistic regression
- • Decision trees and random forests
- • Support Vector Machines (SVM)
- • Neural networks and deep learning
- • Clustering algorithms (K-means, hierarchical clustering)
- • Dimensionality reduction (Principal Component Analysis - PCA)
- • Association rule learning
- • Cross-validation
- • Model performance metrics
- • Tuning model parameters for better performance
- • Basics of NLP
- • Text preprocessing
- • Sentiment analysis and text classification
- • Image processing and feature extraction
- • Object detection and recognition
- • Convolutional Neural Networks (CNN)
- • Basics of reinforcement learning
- • Markov Decision Processes (MDP)
- • Q-learning and policy gradients
- • Model deployment strategies
- • Integration with web applications
- • Model monitoring and maintenance
- • Considerations for ethical AI development
- • Bias and fairness in ML models
- • Privacy and security concerns
- • Applying ML and AI concepts to real-world scenarios
- • Solving business problems through practical projects
- • Hands-on experience with industry-relevant datasets
- • Strategic integration of ML and AI in organizations
- • Business use cases and ROI analysis
- • Future trends and developments in the field
- • Culminating project applying acquired skills
- • Demonstrating end-to-end ML or AI solution development
- • Presentation and documentation of the project
What is Artificial Intelligence?
Artificial Intelligence replicates human intelligence in machines programmed to replicate human thought and behaviour. The term applies to any computer demonstrating human-like characteristics such as learning and problem-solving.
AI is founded on the premise that human intelligence may be characterised so that a machine can imitate it and carry out tasks ranging from the simplest to the most difficult. It aims to simulate human cognitive processes and let the system do human-like work.
Prerequisites for Artificial Intelligence
To qualify for a job in Artificial Intelligence, a graduate degree, preferably in computer science, is beneficial. However, possessing specific skills is equally important. Here are the prerequisites for entering the field of AI:
1. Programming Knowledge:
• Mastering programming languages like R, Python, Java, and C++ is crucial for an AI job. Understanding concepts such as data sets and classes is essential regardless of the chosen programming language.
2. Calculus, Linear Algebra, and Statistics:
• A strong grasp of statistics is necessary to comprehend how programs and Machine Learning function. Understanding basic statistical concepts like Gaussian distributions, median, variance, and being able to forecast reports using probabilities and Naive Bayes models is vital. Additionally, a solid foundation in calculus, integrals, and derivatives is required.
3. Natural Language Processing (NLP):
• It is fundamental for AI professionals to have a good understanding of NLP libraries like NLTK and Gensim. They should also be familiar with techniques such as sentimental analysis, summaries, and word2vec.
4. Neural Network Structures:
• Neural networks, composed of artificial neurons akin to those in our bodies, are effective for addressing complex problems that are difficult to code manually. Understanding neural network basics is essential for tasks like speech recognition, image processing, and translation.
What is Machine Learning?
Machine Learning (ML) is a subset of artificial intelligence that enables systems to learn from data and replicate intelligent human behavior. Its primary objective is to enhance application accuracy in predicting outcomes without the need for explicit coding.
Prerequisites for Machine Learning:
While a master's degree can showcase your abilities, practical knowledge and skills are crucial for building projects or pursuing careers in Machine Learning. Here are some key prerequisites:
Mathematical Skills:
Understanding mathematics is fundamental for Machine Learning. Key areas include:.
• Statistics: ML concepts often originate from statistics or are closely linked to it. Strong statistical knowledge is essential for solving modern problems, including concepts like logistic regression, distributions, and standard deviation.
• Linear Algebra: This field deals with vectors, matrices, and linear transformations, crucial for ML algorithms to operate on multidimensional datasets.
• Probability: Probability assessment helps in predicting events accurately despite limited information. It is used for hypothesis testing, understanding distributions like the Gaussian distribution, and probability density functions in ML.
Calculus: While not needing an in-depth understanding, basic knowledge of calculus is necessary. Concepts like differentiation and gradient descent are vital for ML algorithms.
Having a solid foundation in these mathematical areas is vital for anyone venturing into Machine Learning..
Upcoming Batches
| November 8 | SAT & SUN weekend batch |
Timings 9:00AM to 11:00AM |
| November 22 | SAT & SUN Online batch |
Timings 9:00AM to 11:00AM |
| November 13 | MON & THU Offline batch |
Timings 9:00AM to 11:00AM |
| November 27 | MON & THU Blended batch |
Timings 9:00AM to 11:00AM |