Master Machine Learning & Ai With Python
Published 3/2025
Created by Paul Carlo Tordecilla
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 62 Lectures ( 5h 4m ) | Size: 2.46 GB
Building Intelligent Systems from the Ground Up
What you'll learn
Understand the theory behind machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
Learn data preprocessing, feature engineering, and visualization methods to prepare data for modeling.
Gain hands-on experience building and evaluating models for regression, classification, clustering, and recommendation systems using Python.
Explore deep learning, neural networks, generative models, and advanced topics like meta-learning, federated learning, and graph neural networks through real-wo
Discover how to deploy machine learning models, optimize performance with distributed computing, and integrate AI solutions into applications.
Requirements
Familiarity with Python programming, including data types, control structures, and functions.
A basic understanding of algebra, calculus, and statistics to grasp algorithmic concepts.
Prior exposure to simple ML concepts or courses can be beneficial, though not mandatory for beginners.
Working knowledge of libraries like NumPy and Pandas for data manipulation and analysis.
A proactive attitude toward solving problems, experimenting with code, and building projects.
Description
Embark on a transformative journey into the world of Machine Learning and Artificial Intelligence with our comprehensive online course. Designed for beginners and intermediate learners alike, this course bridges theory and practice, enabling you to master key concepts, techniques, and tools that drive today's intelligent systems. Whether you're aiming to launch a career in data science, build innovative projects, or simply expand your technical prowess, this course provides the robust foundation and hands-on experience you need.What You'll LearnIntroduction to Machine LearningWhat is Machine Learning?Understand the definition, historical evolution, and transformative impact of machine learning in various industries.Types of Machine Learning
ive deep into supervised, unsupervised, and reinforcement learning with real-world applications.Applications & Tools:Explore practical use cases across industries and get acquainted with the Python ecosystem and essential libraries like NumPy, Pandas, and Scikit-Learn.Data PreprocessingUnderstanding Data:Learn to distinguish between structured and unstructured data, and use visualization techniques to explore datasets.Data Cleaning & Feature Engineering:Master techniques for handling missing data, encoding categorical variables, feature scaling, and engineering new features.Data Splitting:Get hands-on experience with training/testing splits and cross-validation to ensure robust model performance.Regression TechniquesStart with Simple Linear Regression and progress to Multiple Linear, Polynomial Regression, and more advanced methods like Support Vector Regression, Decision Tree, and Random Forest Regression.Learn how to tackle issues like multicollinearity, overfitting, and implement these models using Python.Classification TechniquesFoundational Algorithms:Gain insights into Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) for both binary and multiclass problems.Advanced Methods:Understand Naive Bayes, Decision Trees, and ensemble methods such as Random Forests and boosting algorithms like AdaBoost, GBM, and XGBoost.Deep Dive into XGBoost:Learn the introduction to XGBoost and explore its advanced concepts, making it a powerful tool for your classification tasks.Clustering TechniquesExplore unsupervised learning with K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models.Understand how to determine optimal cluster numbers and interpret dendrograms for meaningful insights.Association Rule LearningApriori & Eclat Algorithms:Learn how to mine frequent itemsets and derive association rules to uncover hidden patterns in data.Natural Language Processing (NLP)Text Processing Fundamentalselve into tokenization, stopword removal, stemming, and lemmatization.Vectorization Techniques:Build models using Bag of Words and TF-IDF, and explore sentiment analysis to interpret textual data.Deep LearningNeural Networks & Training:Understand the architecture, training processes (forward and backpropagation), and optimization techniques of neural networks.Specialized Networks:Learn about Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) including LSTM for sequence modeling.Hands-On with Keras & TensorFlow:Build, evaluate, and tune models using industry-standard frameworks.Why Enroll?Comprehensive Curriculum:Our course is meticulously structured to take you from foundational concepts to advanced machine learning techniques, ensuring a holistic understanding of the field.Hands-On Learning:With practical labs and real-world projects, you'll not only learn the theory but also gain the experience needed to implement your ideas effectively.Expert Guidance:Learn from seasoned professionals who bring real industry experience and cutting-edge insights into every lesson.Career Advancement:Gain in-demand skills that are highly valued in tech, finance, healthcare, and beyond, positioning you for success in a rapidly evolving job market.Community & Support:Join a vibrant community of learners and experts, engage in discussions, receive feedback, and collaborate on projects to accelerate your learning journey.Enroll Now!Don't miss this opportunity to transform your career with advanced skills in Machine Learning and AI. Whether you're aspiring to build intelligent systems, analyze complex data, or innovate in your current role, this course is your gateway to success. Secure your spot today and start building the future!Ready to revolutionize your learning journey? Enroll now and become a leader in the era of AI!Who this course is for
Individuals looking to start a career in data science and machine learning with a solid practical foundation.
Developers who want to expand their skill set to include AI and machine learning technologies.
University students or researchers interested in applying ML concepts to academic projects or research problems.
Professionals from various fields seeking to transition into roles that focus on data analytics and machine learning.
Anyone passionate about technology, eager to build real-world AI projects and deepen their understanding of advanced ML techniques.
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