Deep Learning With Python | Essential Deep Learning Concepts

A deep Understanding of Deep Learning

What you'll learn

Data visualization libraries such as Pandas, Matplotlib, Seaborn, and NumPy.

Key concepts of machine learning, including supervised and unsupervised learning, and understand the differences between them.

Implementation of linear regression models.

Understanding the concept of cost functions.

Employing gradient descent for optimization.

Decision tree algorithms, including XGBoost and Random Forests.

Understand how ensemble methods work and their applications in predictive modeling, enabling them to construct more accurate and robust models.

They will also be able to extend their skills to logistic regression, including cost functions and gradient descent specific to classification problems.

Requirements

A basic python knowledge required

Description

*  Course Description  *Welcome to the world of Deep Learning with Python! This comprehensive course is designed to take you through the fundamental concepts and practical applications of deep learning, enabling you to develop a strong foundation in artificial intelligence. Through a series of engaging lectures and hands-on lab sessions, you will gain a deep understanding of various Python libraries, machine learning algorithms, and advanced techniques used in deep learning.* Course Curriculum *Section 1: Introduction Let's start by understanding what deep learning is and why it's so important. We'll also get familiar with special tools called Integrated Development Environments (IDEs) that will make your learning journey smooth.Section 2: Python Libraries Learn how to use Python for some cool stuff! We'll teach you how to play with data using Pandas, do math with Numpy, explore science with Scipy, make fancy charts with Matplotlib, and create beautiful visualizations with Seaborn.Section 3: Introduction to Deep Learning Say hello to deep learning! We'll show you what it's all about and how it can help solve big problems. We'll even introduce you to the fancy thing called a neural network.Section 4: Supervised vs. Unsupervised Learning Discover two ways computers learn: one where they're told exactly what to do (that's supervised learning) and another where they learn on their own by finding patterns (that's unsupervised learning).Section 5: Linear Regression Ever thought of predicting the future? We'll teach you how to predict stuff using lines! We'll show you how to draw these lines, how to know if they're right, and how to stop them from guessing too much.Section 6: Multiple Linear Regression Let's take things up a notch! We'll teach you how to predict even more stuff using more lines. It's like magic, but with math!Section 7: Logistic Regression Sometimes, computers need to make decisions. We'll show you how computers can make decisions like a detective, and we'll even introduce you to some cool math called logistic regression.Section 8: Decision Trees Discover the magic of decision trees. They're like those "Choose Your Own Adventure" books, but for computers. We'll also introduce you to two special helpers: Xgboost and Random Forest.Section 9: Clustering Imagine you have a lot of data and you want to group similar things. That's clustering! We'll show you how to do it, and you'll feel like a detective sorting clues.Section 10: Anomaly Detection Have you ever wanted to find the odd one out? Computers can help with that! We'll teach you how to spot weird stuff in data using cool tricks.Section 11: Collaborative and Content-Based FilteringImagine having a robot friend who knows exactly what you like. We'll show you how computers can be like that friend by recommending things you'll love.Section 12: Reinforcement LearningEver played a game and learned how to win by trying different things? That's what reinforcement learning is about! We'll explore how computers can learn by trying and failing, just like you do.Section 13: Neural NetworksDiscover the brain of AI: the neural network. It's like a web of tiny decision-makers that help computers understand the world. Section 14: TensorFlow One of the most popular deep learning libraries, a special tool that makes building and teaching neural networks easier. You'll be like a wizard creating AI spells!Section 15: Keras A high-level neural networks API that simplifies the process of building and training deep learning models. It's like building with blocks - easy and fun!Section 16: PyTorchAnother widely used deep learning library, and learn how to build, train, and deploy neural networks with its flexible and dynamic computation graph. It's like creating art with digital brushes, giving you lots of freedom.Section 17: RNN and CNN Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two specialized architectures that excel in processing sequential and image data, respectively.By the end of this course, you will have gained a comprehensive understanding of deep learning concepts and techniques, and you'll be equipped to tackle a variety of AI and machine learning challenges using Python and its powerful libraries. Whether you're a beginner in the field or looking to solidify your knowledge, this course will empower you to take your first steps toward becoming an AI expert. So, get ready to unlock the magic of AI and make your computer do amazing things!

Overview

Section 1: Introduction

Lecture 1 Introduction to Deep learning and Introduction to IDE

Section 2: Python Libraries

Lecture 2 Pandas

Lecture 3 Numpy

Lecture 4 Scipy

Lecture 5 Matplotlib

Lecture 6 Seaborn

Section 3: Introduction to Deep Learning

Lecture 7 Introduction to Deep Learning

Section 4: Super vised vs Unsupervised

Lecture 8 Super vised vs Unsupervised

Section 5: Linear Regression

Lecture 9 Introduction to Linear Regression

Lecture 10 Cost Function

Lecture 11 Gradient Descent

Lecture 12 Over Fitting

Lecture 13 Gradient Descent for Linear Regression

Lecture 14 Linear Regression (Lab Session)

Section 6: Multiple Linear Regression

Lecture 15 Multiple Linear Regression

Section 7: Logistic Regression

Lecture 16 Introduction to Logistic Regression

Lecture 17 Cost Function , Gradient Descent for Logistic Regression

Lecture 18 V18. Logistic Regression (Lab Session)

Section 8: Decision Trees

Lecture 19 Introduction to Decision Trees

Lecture 20 Xgboost

Lecture 21 Randomforest

Section 9: Clustering

Lecture 22 Clustering

Section 10: Anomaly Detection

Lecture 23 Anomaly Detection

Section 11: Collaborative and Content Based Filtering

Lecture 24 Collaborative and Content Based Filtering

Section 12: Reinforcement Learning

Lecture 25 V25. Reinforcement Learning

Section 13: Neural Networks

Lecture 26 Neural Networks

Section 14: TensorFlow

Lecture 27 V27. TensorFlow

Section 15: Keras

Lecture 28 Keras

Section 16: Pytorch

Lecture 29 Pytorch

Section 17: RNNs and CNNs

Lecture 30 V30. RNN and CNN

Beginner Python Developers enthusiastic about Learning Deep Learning and Data Science,Students who have at least high school knowledge in math and who want to start learning Machine Learning.,Any people who are not that comfortable with coding but who are interested in Deep Learning and want to apply it easily on datasets.,Any data analysts who want to level up in Deep Learning.,Anyone interested in Deep Learning.