Master Machine Learning & Ai With Python

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.