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Deep learning and Machine Learning with Python

Name: Deep learning and Machine Learning with Python
Rating: 4.5 (54 reviews)

Master Artificial Intelligence and Deep Learning with Python

Created bySelfcode Academy

Last updated 6/2024

English

English [Auto],

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.

Course content

17 sections • 30 lectures • 10h 12m total length

Introduction to Deep learning and Introduction to IDE4:02
Start from Python basics and hands-on machine learning, explore an IDE with Jupyter notebooks, learn essential libraries (pandas, matplotlib, seaborn), and study regression, classification, unsupervised learning, and neural networks.

Pandas16:00
Read, clean, explore, and manipulate datasets with pandas, the Python library for working with dataframes. Inspect shapes, data types, and columns; subset, sort, and group by to compute means.
Numpy14:27
Discover NumPy basics, including ndarray structure, array creation, reshaping, and fast numerical computations; explore broadcasting, vectorized operations, random arrays, and simple comparisons to power data analysis.
Scipy21:07
Explore SciPy's powerful numerical tools for integration, interpolation, optimization, linear algebra, signal and image processing, statistics, and differential equations, with practical Python coding examples.
Matplotlib18:08
Explore Matplotlib for visualizing diabetes data with line, scatter, bar, and histogram plots. Learn to load the diabetes dataset from scikit-learn and render these charts to gain insights.
Seaborn17:27
Discover Seaborn's advanced visualizations, including scatter, box, violin plots, and heatmaps, using Python with the diabetes dataset, and compare it to matplotlib for broader machine learning prep.

Introduction to Linear Regression17:50
Discover linear regression in a supervised learning setting, using gradient descent and a cost function to fit a univariate model with features and y hat predictions from training data.
Cost Function24:13
Explore how linear regression models use f(x)=wx+b, fit by minimizing the cost function j(w,b) with squared error, and how gradient descent guides optimization.
Gradient Descent9:57
The lecture explains gradient descent for optimizing weights and bias, using the cost function and learning rate with updates w - alpha ∂J/∂w and b - alpha ∂J/∂b.
Over Fitting4:14
Explain how the learning rate alpha controls gradient descent, showing how too small slows convergence, too large causes overshoot, and how a decreasing rate guides the update toward the optimum.
Gradient Descent for Linear Regression7:58
Explore how gradient descent optimizes linear regression by updating w and b via the cost function. Derive dJ/dw and dJ/db, and examine the possibility of multiple local minima.
Linear Regression (Lab Session)14:10
Learn how to build a linear regression model in Python using sklearn, load a house price dataset, split the data, train, predict, and evaluate with mean squared error.

Introduction to Logistic Regression19:51
Explore logistic regression, predicting yes or no with a sigmoid function modeling probabilities. Understand how w and b define decision boundaries, contrasting with linear regression and its logistic loss.
Cost Function , Gradient Descent for Logistic Regression25:04
Explore the simplified loss and cost functions for logistic regression, derive gradient descent updates for w and b, and address overfitting with regularization and feature selection.
Logistic Regression (Lab Session)20:47
Explore logistic regression in Python with a diabetes dataset in a Jupyter notebook, covering data preparation and training with scikit-learn. Learn how this classification approach leads to decision trees.

Introduction to Decision Trees34:46
Explore decision trees and their extensions, including Random Forest and XGBoost, contrasting with linear and logistic regression, and learn entropy, information gain, and one-hot encoding through cat-versus-dog examples.
Xgboost13:37
Explore XGBoost, a fast gradient boosting algorithm with regularization, pruning, and feature importance. Learn to implement it in Python, handle missing values, and compare to decision trees.
Randomforest21:24
Explore the random forest algorithm, an ensemble of decision trees that performs regression and classification using bagging, random feature subsets, and out-of-bag error, with iris dataset coding.

Requirements

A basic python knowledge required

Description

Master Deep Learning with Python for AI Excellence

Course Description:

This meticulously crafted course is designed to empower you with comprehensive knowledge and practical skills to thrive in the world of artificial intelligence.

Immerse yourself in engaging lectures and hands-on lab sessions that cover fundamental concepts, cutting-edge methodologies, and real-world applications of deep learning. Gain expertise in essential Python libraries, machine learning algorithms, and advanced techniques, setting a solid foundation for your AI career.

Course Highlights:

In-Demand Skills: Acquire the highly sought-after skills demanded by today's AI-centric job market, opening doors to data science, machine learning, and AI development roles.

Hands-On Learning: Learn by doing! Our interactive lab sessions ensure you gain practical experience, from data preprocessing to model evaluation, making you a proficient deep learning practitioner.

Comprehensive Curriculum: From foundational Python libraries like Pandas and NumPy to cutting-edge neural network architectures like CNNs and RNNs, this course covers it all. Explore linear regression, logistic regression, decision trees, clustering, anomaly detection, and more.

Expert Guidance: Our experienced instructors are committed to your success. Receive expert guidance, personalized feedback, and valuable insights to accelerate your learning journey.

Project-Based Learning: Strengthen your skills with real-world projects that showcase your deep learning capabilities, building a compelling portfolio.

Practical Applications: Understand how deep learning powers real-world applications, including image recognition, natural language processing, recommendation systems, and autonomous vehicles.

Who Should Enroll:

Aspiring Data Scientists: Start your journey into data science and AI with the skills and knowledge needed to excel.

Machine Learning Enthusiasts: Deepen your understanding of machine learning and take it to the next level with deep learning applications.

AI Developers: Enhance your proficiency in deep learning to stay ahead in this rapidly evolving field.

Whether you're new to AI or an experienced professional, this course empowers you to harness the full potential of deep learning and Python, opening doors to limitless opportunities. Don't miss this chance to shape your future in artificial intelligence.

Course Curriculum

Section 1: Introduction

Understand the significance of deep learning and its implications.

Get familiar with essential Integrated Development Environments (IDEs).

Section 2: Python Libraries

Master data manipulation with Pandas.

Explore numerical operations with NumPy.

Dive into scientific analysis using Scipy.

Create visually appealing graphics with Matplotlib.

Craft elegant visualizations with Seaborn.

Section 3: Introduction to Deep Learning

Uncover the fundamental principles of deep learning.

Grasp the pivotal role of neural networks.

Section 4: Supervised vs. Unsupervised Learning

Demystify supervised and unsupervised learning.

Section 5: Linear Regression

Master linear regression for prediction.

Section 6: Multiple Linear Regression

Predict multiple outcomes using advanced techniques.

Section 7: Logistic Regression

Equip computers with decision-making capabilities.

Section 8: Decision Trees

Explore decision trees and essential companions like Xgboost and Random Forest.

Section 9: Clustering

Organize data through clustering.

Section 10: Anomaly Detection

Identify anomalies in data.

Section 11: Collaborative and Content-Based Filtering

Deliver personalized recommendations.

Section 12: Reinforcement Learning

Immerse in dynamic reinforcement learning.

Section 13: Neural Networks

Delve into the core of AI with neural networks.

Section 14: TensorFlow

Master the acclaimed deep learning library.

Section 15: Keras

Build and train deep learning models with ease.

Section 16: PyTorch

Explore the dynamic and versatile deep-learning library.

Section 17: RNN and CNN

Unlock specialized architectures for sequential data and image processing.

Upon course completion, you'll possess a profound understanding of deep learning, ready to tackle diverse AI and machine learning challenges using Python's robust toolkit.

This course equips you to confidently step into the realm of AI mastery. Experience the magic of AI and command your computer to achieve remarkable feats!

Enroll now and unlock the magic of Deep Learning and Python!"

Who this course is for:

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.

Deep learning and Machine Learning with Python

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 4min

Python Libraries5 lectures • 1hr 27min

Introduction to Deep Learning1 lecture • 21min

Super vised vs Unsupervised1 lecture • 36min

Linear Regression6 lectures • 1hr 18min

Multiple Linear Regression1 lecture • 16min

Logistic Regression3 lectures • 1hr 6min

Decision Trees3 lectures • 1hr 10min

Clustering1 lecture • 35min

Anomaly Detection1 lecture • 24min

Requirements

Description

Who this course is for: