Introduction to AI with Python
Discover AI basics and its problem-solving applications. Dive into machine learning with Python, understanding its types and real-world uses. Embrace the AI revolution.
per person
Level
Duration
Training Delivery Format
Face-to-face / Virtual Class
per person
Level
Duration
Training Delivery Format
Face-to-face (F2F) / Virtual Class
Class types
Public Class
Private Class
In-House Training
Bespoke
About this course
Dive into the transformative world of artificial intelligence with this 21-hour course, spanning from foundational concepts to intermediate applications. Begin with a historical backdrop of AI and differentiate between its subsets: machine learning and deep learning.
Progress through fundamental machine learning models and delve into a diverse range of algorithms, from decision trees to support vector machines. Introduce yourself to the nuances of natural language processing and culminate your learning journey with the intricate architectures of neural networks. Guided by expert insights and hands-on coding sessions, you’ll emerge well-equipped to navigate and contribute to the AI revolution.
Who should attend?
- Beginners are eager to enter the realm of artificial intelligence and machine learning.
- Programmers and software developers aiming to diversify their skills into AI and ML.
- Data analysts and data scientists wanting to deepen their understanding of machine learning algorithms.
- Students pursuing computer science or related disciplines keen on staying ahead in AI trends.
- Tech professionals in roles adjacent to AI/ML (like product managers) who wish to gain foundational to intermediate knowledge in the field.
Learning Outcome
By the end of this course, participants will:
- Differentiate between AI, machine learning, and deep learning, understanding their historical and practical contexts.
- Understand and apply fundamental machine learning algorithms, from linear regression to support vector machines.
- Dive deep into neural network architectures, including CNNs and RNNs, and their practical applications.
- Grasp the essentials of data preprocessing, training, testing, and model evaluation.
- Develop hands-on skills by implementing machine learning models using Python.
- Be well-prepared to undertake more advanced AI studies or apply their knowledge in practical settings.
Prerequisites
- Basic understanding of programming concepts.
- Familiarity with Python programming is a plus, but not mandatory.
- A willingness to dive deep into complex algorithms and mathematical concepts.
Course Content
Module 1: Introduction
- Definition and history of artificial intelligence (AI)
- The difference between AI, machine learning (ML), and deep learning
- Importance and applications of ML in today’s world
- Course objectives and overview
Module 2: The Basics of Machine Learning
- Definitions: supervised, unsupervised, and reinforcement learning
- Overview of data preprocessing: cleaning, normalization, and encoding
- Training and test data split
- Evaluation metrics: accuracy, precision, recall, F1-score
Module 3: Your First Models
- Linear regression: concept and application
- Logistic regression: concept and its difference from linear regression
- Implementing the above models using Python
- Evaluating model performance
Module 4: Machine Learning Algorithms
- Decision trees: basics and implementations
- Random forests: ensemble method and importance
- Support vector machines (SVM): basics and kernel trick
- K-means clustering: unsupervised learning and its uses
Module 5: More Machine Learning Algorithms
- Naive Bayes: probabilistic classifiers and their application
- Principal component analysis (PCA): dimensionality reduction
- Gradient boosting machines (GBM): boosting concept and applications
- Introduction to natural language processing (NLP)
Module 6: Neural Networks and Deep Learning
- Introduction to neural networks: perceptrons, layers, activation functions
- Feedforward and backpropagation explained
- Convolutional neural networks (CNN): application in image recognition
- Recurrent neural networks (RNN): application in sequence data
- Transfer learning and pre-trained models
FAQs
Q: Is prior programming experience necessary for this course?
A: While a basic understanding of programming concepts is beneficial, the course is designed to accommodate participants from varying backgrounds. Familiarity with Python can give a head start, but it isn’t mandatory.
Q: Will I get hands-on experience during the course?
Absolutely! The course combines theoretical knowledge with practical coding sessions, allowing participants to implement the algorithms they learn.
Q: How is the course delivered?
A: The course is delivered in a face-to-face classroom or virtual class with expert instructors, interactive discussions, and resource materials for independent study.
Q: Do I need any special software or hardware?
A computer with a stable internet connection is essential. Specific software requirements, like Python and related libraries, will be provided at the start of the course.
