Practical Machine Learning using Python
Why take this course?
🚀 Course Title: Practical Machine Learning using Python
🎓 Course Headline: Concepts and Projects-Based Learning for Aspiring Machine Learning Professionals
Dive into the World of Machine Learning with Python! 🐍
Are you aspiring to become a Machine Learning Engineer or Data Scientist? If your answer is a resounding yes, then this course is exactly what you need to kickstart your journey! 🚀
In this comprehensive course, you'll delve into the core concepts of Machine Learning, exploring use cases, the crucial role of data, and the challenges of Bias, Variance, and Overfitting. You'll become proficient in selecting the right Performance Metrics, understanding Model Evaluation Techniques, and optimizing your models using Hyperparameter Tuning and Grid Search Cross Validation.
📚 What You'll Learn:
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Core Machine Learning Concepts: Gain a solid understanding of what Machine Learning entails, the types of algorithms used, and their practical applications in real-world scenarios.
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Data Exploration & Analysis: Master the use of Numpy and Pandas libraries for Exploratory Data Analysis (EDA) to uncover insights from your datasets.
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Visualization Techniques: Learn to visualize your data using Marplotlib and Seaborn libraries, turning complex datasets into clear and insightful graphics.
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Python for Machine Learning & Data Science: If you're a beginner in Python, this course will get you up to speed with the internal data structures of Python, language elements, and more.
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Worked-Out Projects: This hands-on course is rich with real-world projects and examples that take you from Data Exploration to Model Development, Optimization, and Evaluation. You'll apply what you've learned through case studies like House Price Prediction and Credit Card Fraud Detection.
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Deep Neural Networks: Get an introductory lesson on Deep Neural Networks with a worked-out example on Image Classification using TensorFlow and Keras.
Course Sections:
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Introduction to Machine Learning 🔍
- Get acquainted with the field of Machine Learning, its history, types, and applications.
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Types of Machine Learning Algorithms 📊
- Dive into supervised, unsupervised, and reinforcement learning algorithms.
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Use Cases of Machine Learning 🌐
- Explore various scenarios where Machine Learning is applied to solve modern problems.
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Role of Data in Machine Learning 💻
- Understand the importance of data quality, processing, and feature engineering.
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Understanding Training or Learning 📈
- Learn how models learn from data and the nuances of different learning strategies.
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Understanding Validation and Testing ✅
- Discover the best practices for validating and testing your Machine Learning models to ensure they generalize well.
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Introduction to Python 🐍
- Get up to speed with Python essentials, syntax, and structure.
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Setting up your ML Development Environment 🛠️
- Learn how to set up a development environment for Machine Learning using Jupyter Notebooks, Anaconda, etc.
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Python Internal Data Structures ⚙️
- Explore Python's data structures such as lists, tuples, sets, and dictionaries.
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Pandas Data Structure – Series and DataFrames 📊
- Master data manipulation with Pandas and its powerful DataFrame structure.
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Exploratory Data Analysis (EDA) 🔎
- Perform EDA to gain insights into the dataset's behavior and characteristics.
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Learning Linear Regression Model 📈
- Implement and understand linear regression modeling using a practical case study on House Price Prediction.
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Logistic Model for Credit Card Fraud Detection 🚀
- Build a logistic regression model to predict credit card fraud.
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Evaluating Model Performance 🚀
- Learn how to measure the performance of your Machine Learning models using various metrics such as accuracy, precision, recall, and F1-score.
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Fine Tuning Your Model 🔧
- Uncover the process of fine-tuning your model to improve its performance.
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Hyperparameter Tuning 🔄
- Master the art of hyperparameter tuning to optimize your models for better performance.
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Cross Validation 🔍
- Learn about the different methods of cross-validation and their importance in model evaluation.
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Learning SVM through Image Classification Project 📸
- Apply Support Vector Machines (SVM) to classify images with the help of a practical project.
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Understanding Decision Trees 🎲
- Discover how decision trees work, how they can be easily overfitted, and strategies to prevent this.
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Deep Neural Networks (Introductory Lesson) 🧠
- Get a basic understanding of neural networks, including forward propagation, backpropagation, cost functions, and optimization methods, with TensorFlow and Keras.
Enroll now to embark on a journey of Machine Learning with Python! Whether you're starting as a beginner or looking to expand your skills, this course is designed to equip you with the knowledge and hands-on experience to excel in the field of Data Science and Machine Learning. Let's dive into the fascinating world of AI together! 🚀✨
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