Master Machine Learning & Ai With Python
Published 3/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.97 GB | Duration: 5h 4m
Published 3/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.97 GB | Duration: 5h 4m
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:Dive 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 Fundamentals:Delve 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!
Overview
Section 1: Introduction to Machine Learning
Lecture 1 What is Machine Learning?
Lecture 2 Types of Machine Learning
Lecture 3 Applications of Machine Learning
Lecture 4 Tools and Libraries for Machine Learning
Section 2: Data Preprocessing
Lecture 5 Data Preprocessing in Machine Learning
Lecture 6 Working with Structured Data
Lecture 7 Data Exploration
Lecture 8 Data Visualization without Libraries
Section 3: Date Preprocessing: Handling Missing Data
Lecture 9 Handling Missing Data
Section 4: Encoding Categorical Data: Data Preprocessing
Lecture 10 Introduction
Lecture 11 Label Encoding
Lecture 12 One-Hot Encoding
Lecture 13 Encoding Techniques for High Cardinality
Lecture 14 Target Encoding
Lecture 15 Frequency Encoding
Lecture 16 Hash Encoding
Lecture 17 Key Insight
Section 5: Feature Scaling: Data Preprocessing
Lecture 18 Feature Scaling
Section 6: Feature Engineering: Data Preprocessing
Lecture 19 introduction Feature Engineering
Lecture 20 Filter Methods
Lecture 21 Wrapper Methods
Lecture 22 Embedded Methods
Section 7: Splitting Data: Data Preprocessing
Lecture 23 Data Splitting Techniques
Lecture 24 Training and Testing sets
Lecture 25 Cross-validation techniques
Section 8: Regression Techniques
Lecture 26 Introduction of Regression
Lecture 27 Simple Linear Regression
Lecture 28 Multiple Linear Regression
Lecture 29 Polynomial Regression
Lecture 30 Support Vector Regression (SVR)
Lecture 31 Decision Tree Regression
Lecture 32 Random Forest Regression
Section 9: Classification Techniques
Lecture 33 Introduction of Classification Techniques
Lecture 34 Binary Logistic Regression
Lecture 35 Multiclass Logistic Regression
Lecture 36 K-Nearest Neighbors
Lecture 37 Support Vector Machines
Lecture 38 Naive Bayes
Lecture 39 Decision Trees For Classification
Lecture 40 Random Forest
Lecture 41 Boosting Algorithms
Section 10: Clustering Techniques
Lecture 42 K-means Clustering
Lecture 43 Hierarchical Clustering
Lecture 44 Density-based Spatial Clustering
Lecture 45 Gaussian Mixture Models
Section 11: Association Rule Learning
Lecture 46 Introduction of Association Rule Learning
Lecture 47 Apriori Algorithm
Lecture 48 Eclat Algorithm
Section 12: Natural Language Processing (NLP)
Lecture 49 Introduction of Natural Laungage Processing (NLP)
Lecture 50 Text Preprocessing Techniques
Lecture 51 Tokenization - Text Preprocessing
Lecture 52 Stopword Removal - Text Preprocessing
Lecture 53 Stemming and Lemmazation - Text Preprocessing
Lecture 54 Bag of Words Model
Lecture 55 Understanding TF-IDF
Lecture 56 Sentiment Analysis
Section 13: Deep Learning
Lecture 57 Introduction of Deep Learning
Lecture 58 Building Neural Networks
Lecture 59 Training Neural Networks
Lecture 60 Convolutional Neural Networks(CNNs)
Lecture 61 Recurrent Neural Networks(RNNs)
Lecture 62 Keras and Tensorflow
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.