Learn Data Science Machine Learning And Neural Networks

Learn Machine Learning, Data Science, Neural Networks and Artificial Intelligence with Python and libraries

What you'll learn

Visualizing Data

Charts with matplotlib

Linear Algebra

Python Programming Language

Statistics

Probability

Bayes's Theorem, Distributions

Hypothesis and Inference

Gradient Descent

Stochastic Gradient Descent

Working with Data

Machine Learning

k-Nearest Neighbors

Naive Bayes

Simple Linear Regression, Multiple Regression and Logistic Regression

Decision Trees

Neural Networks

Clustering

Natural Language Processing

Network Analysis

Recommender Systems

MapReduce

Requirements

A bit of Python experience will come handy.

Description

Unlock the boundless potential of data by enrolling in our comprehensive course, "Mastering Machine Learning, Data Science, Neural Networks, and Artificial Intelligence with Python and Libraries." This meticulously crafted program is designed to empower individuals with the skills and knowledge needed to navigate the dynamic landscape of modern technology.Course Overview:In this immersive learning journey, participants will delve into the core principles of Machine Learning, Data Science, Neural Networks, and Artificial Intelligence using Python as the primary programming language. The course is structured to cater to both beginners and intermediate learners, ensuring a gradual progression from fundamental concepts to advanced applications.Key Highlights:Foundations of Machine Learning:Gain a solid understanding of machine learning fundamentals, algorithms, and models.Explore supervised and unsupervised learning techniques.Master feature engineering, model evaluation, and hyperparameter tuning.Data Science Essentials:Learn the art of extracting valuable insights from data.Acquire proficiency in data manipulation, cleaning, and exploratory data analysis.Harness the power of statistical analysis for informed decision-making.Neural Networks and Deep Learning:Dive into the realm of neural networks and deep learning architectures.Understand the mechanics of artificial neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).Implement state-of-the-art deep learning models using Python libraries.Artificial Intelligence (AI) Applications:Explore the practical applications of AI in various industries.Work on real-world projects that simulate the challenges faced by AI professionals.Develop skills in natural language processing (NLP) and computer vision.Hands-On Python Programming:Enhance your Python programming skills to effectively implement machine learning algorithms.Leverage popular Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-Learn.Gain proficiency in handling large datasets and deploying machine learning models.Why Choose Our Course?Comprehensive Curriculum: Our curriculum is meticulously curated to cover a wide spectrum of topics, ensuring a holistic understanding of machine learning, data science, neural networks, and artificial intelligence.Practical Applications: The course emphasizes hands-on learning through real-world projects, enabling participants to apply theoretical knowledge to practical scenarios.Expert Guidance: Learn from industry experts and seasoned professionals who bring a wealth of practical experience to the classroom.Career Opportunities: Equip yourself with in-demand skills sought by employers in the rapidly evolving fields of machine learning and artificial intelligence.Community and Networking: Connect with like-minded individuals, share insights, and build a valuable network within the data science and AI community.Embark on a transformative learning experience that will not only equip you with the skills to thrive in the world of machine learning and artificial intelligence but also position you as a proficient practitioner ready to tackle complex challenges in the data-driven era. Join us on this exciting journey to master the intricacies of Python, machine learning, data science, neural networks, and artificial intelligence!

Overview

Lecture 1 Introduction

Lecture 2 Hello from another VP

Lecture 3 Hello from another VP - 2

Lecture 4 Graphical Experience - 2

Section 1: Visualizing Data in Python

Lecture 5 Introduction

Lecture 6 Bar Charts

Lecture 7 Bar Charts - 2

Lecture 8 Bar Charts - 3

Lecture 9 Line Charts

Lecture 10 Scatterplots

Section 2: Linear Algebra

Lecture 11 Vectors

Lecture 12 Vectors - 2

Lecture 13 Vectors - 3

Lecture 14 Matrices

Lecture 15 Matrices - 2

Section 3: Statistics

Lecture 16 Introduction

Lecture 17 Statistics

Lecture 18 Central Tendencies

Lecture 19 Central Tendencies - 2

Lecture 20 Dispersion

Lecture 21 Correlation

Lecture 22 Correlation - 2

Section 4: Probability in Python

Lecture 23 Probability

Lecture 24 Dependence and Independence

Lecture 25 Conditional Probability

Lecture 26 Boy and Girl Probability

Lecture 27 Bayes Theorem

Lecture 28 Random Variables

Lecture 29 Continuous Distributions

Lecture 30 Normal Distribution

Lecture 31 Central Limit Theorem

Lecture 32 Central Limit Theorem - 2

Section 5: Inference and Hypothesis

Lecture 33 Hypothesis Testing and Coin Examples

Lecture 34 Coin Example

Lecture 35 Coin Example - 2

Lecture 36 Coin Example - 3

Lecture 37 Coin Example - 4

Lecture 38 Confidence Interval

Lecture 39 P-Hacking

Lecture 40 A/B Testing

Lecture 41 Bayesian Inference

Section 6: Gradient Descent

Lecture 42 Gradient Descent

Lecture 43 Estimating

Lecture 44 Estimating - 2

Lecture 45 Right Step Size

Lecture 46 Additional Details

Lecture 47 Stochastic Gradient Descent

Section 7: Data Exploration and Working with Data

Lecture 48 Data Exploration and Working with Data

Lecture 49 Two Dimensions

Lecture 50 Plenty of Dimensions

Lecture 51 Cleaning

Lecture 52 Cleaning - 2

Lecture 53 Manipulation

Lecture 54 Manipulation - 2

Lecture 55 Manipulation - 3

Lecture 56 Manipulation - 4

Lecture 57 Rescaling

Lecture 58 Rescaling - 2

Lecture 59 Dimensionality Reduction

Lecture 60 Dimensionality Reduction - 2

Lecture 61 Dimensionality Reduction - 3

Section 8: Introduction to Machine Learning

Lecture 62 Introduction to Machine Learning

Lecture 63 Over-fitting and Under-fitting

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