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Introduction to AI, AI Programming & Machine Learning | AI / ML JumpStart
Description
Introduction to AI, AI Programming & Machine Learning
Welcome to the Introduction to AI, AI Programming and Machine Learning JumpStart course, a comprehensive program designed for a diverse audience including developers eager to transition into data science, analytics managers guiding teams, and business analysts seeking a deeper understanding of data science methodologies.
This course provides an engaging introduction to the dynamic fields of artificial intelligence (AI) and machine learning (ML), ensuring a thorough grounding in Python, which is essential for mastering machine learning techniques.
Over the course of three immersive days, this course offers hands-on experience with a focus on the essential principles of AI and ML. We deconstruct complex mathematical concepts and emphasize understanding the algorithms that power machine learning models.
The curriculum is crafted to facilitate a smooth and deep understanding of various technologies, ideas, and skills vital in the AI and ML domain. Each module is carefully structured to address real-world scenarios, highlight the latest advancements in the field, and present actionable strategies for practical application.
This approach guarantees that participants not only learn the fundamentals but also gain insights into the application of these technologies in real-world situations.
Introduction to AI, AI Programming & Machine Learning Objectives
By the course’s end, you’ll possess practical knowledge of core skills, methods, and algorithms:
- Confidently apply Python for machine learning.
- Grasp machine learning fundamentals from a beginner’s perspective.
- Navigate data science techniques for data-driven decision-making.
- Explore popular machine learning algorithms, understanding their applicability and limitations.
- Apply supervised algorithms for classification and data splitting.
- Master data cleaning and simplification techniques.
- Utilize machine learning packages and tools effectively.
- Dive into neural networks and ensemble methods for complex datasets.
- Engage in practical examples of Data Engineering and Machine Learning
Prerequisites
Before enrolling in this course, ensure you have the following prerequisite skills and knowledge:
- Solid basic Python skills.
- Good foundational mathematics in Linear Algebra and Probability.
- Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su.
Audience
This course is suitable for a wide range of technical learners, including:
- Developers aiming to become data scientists or ML engineers.
- Analytics Managers leading analyst teams.
- Business Analysts who are interested in data science techniques.
- Information Architects looking to master ML algorithms.
- Analytics professionals transitioning to ML and AI roles.
- Graduates pursuing careers in Data Science and ML.
- Experienced professionals incorporating ML into their fields for deeper customer insights.
Introduction to AI, AI Programming & Machine Learning Outline
What is AI and Machine Learning
- Understanding AI and ML
- Differentiating AI and ML
- Machine Learning Examples
Types of Machine Learning
- Supervised, Unsupervised, and Reinforcement Learning
- Labeled and Unlabeled Data
- Regression and Classification
Linear Regression
- Fitting a Line to Data
- Linear Regression Algorithm in Python
- Predicting Housing Prices with Turi Create
- Introduction to Polynomial Regression
Optimizing the Training Process
- Overfitting and Underfitting
- Avoiding Overfitting
- Model Complexity and Regularization
- Selecting the Best Model
The Perceptron Algorithm
- Classification and Sentiment Analysis
- Drawing Separation Lines
- Understanding Perceptrons
- Perceptron Algorithm in Python
Logistic Classifiers
- Hard vs. Soft Assignments
- Sigmoid Function
- Logistic Regression Algorithm
- Logistic Regression in Python
Measuring Classification Models
- Types of Model Errors
- Confusion Matrix
- Metrics: Accuracy, Recall, Precision, F-Score, Sensitivity, Specificity
- ROC Curve
The Naive Bayes Model
- Bayes Theorem
- Dependent and Independent Events
- Prior and Posterior Probabilities
- Naive Bayes Algorithm in Python
Decision Trees
- Introduction to Decision Trees
- Classification and Regression Trees
- Building a Recommendation System
- Using Scikit-Learn for Decision Trees
Neural Networks
- Neural Network Fundamentals
- Architecture: Nodes, Layers, Activation Functions
- Training Neural Networks
- Improving Training Techniques
- Using Neural
$2195.00
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3 Days Course |