Machine Learning Bootcam: Hand-On Python in Data Science
Learn Complete hands-on guide to implementing Supervised Machine Learning Algorithm in Python including ANN, CNN & RNN
Ratings 2.93 / 5.00
What You Will Learn!
- Basics of Python (Introduction to Spyder & Jupyter Notebook)
- Numpy (•Introduction to the Library •Nd-array Object •Data Types •Array Attributes •Indexing and Slicing •Array Manipulation)
- Pandas (Introduction to the Library •Series Data Structures •Pandas Data Frame •Pandas Basic Functionality •Crash Course – Data Visualization & ScikitLearn)
- Tensorflow (•Introduction to the Library •Basic Syntax •Tensorflow Graphs •Variable Place Holders •Neural Network •Tensorboard)
- Seaborn (•Distribution Plots •Categorical Plots •Regression Plots •Style and Color)
- Plotly and Cufflinks
- Regression (• Simple Linear Regression •Multiple Linear Regression •Polynomial Regression •Support Vector Regression • Decision Tree & Forest Regression
- Classification (•Logistic Regression •K-Nearest Neighbors • Support Vector Machine •Kernel SVM •Naïve Bayes •Decision Tree Classification •Random Forest)
- Deep Learning (•Artificial Neural Networks •Convolutional Neural Networks •Recurrent Neural Networks)
Description
This comprehensive course delves into the essential realm of Supervised Learning in Python, a pivotal branch of Machine Learning. Whether you are a Python novice or an experienced programmer, fear not, as the initial lectures devoted to Python and its integral libraries, including Numpy, Pandas, Seaborn, Scikit-Learn, and Tensorflow, are designed to equip you with the necessary skills and familiarity with the programming language.
The course is thoughtfully structured into two distinct sections. The first section focuses on Python basics and fundamental libraries, providing a solid foundation crucial for delving into the intricacies of Supervised Machine Learning. It serves as a preparatory phase, ensuring participants are well-versed in the tools required for effective engagement with the subsequent material.
The second section delves into the core of Supervised Learning, spanning three main chapters: Regression, Classification, and Deep Learning. Each chapter is meticulously dissected, offering a dual approach of theoretical understanding and hands-on experimentation. This method not only enhances conceptual comprehension but also ensures practical proficiency in implementing algorithms.
Throughout the course, emphasis is placed on the practical application of various machine learning algorithms. Participants will learn to harness these algorithms to construct impressive modules of Machine Learning. By the course's culmination, you will have acquired the expertise to independently develop Recognition Systems, Prediction Models, and various other applications.
Embark on this learning journey, and by the course's conclusion, you will be well-equipped to tackle real-world challenges using Supervised Learning techniques in Python. Let's get started on this exciting exploration of the world of machine learning!
Who Should Attend!
- Those who are interested in AI and Machine Learning
- Those who have basic knowledge of any programming language
- Those who want to be create awesome Machine Learning and AI modules
- And those who want to earn some handsome amount of money from Machine Learning Field in Future
Tags
- Data Science
- Machine Learning
- Python
Subscribers
2937
Lectures
86
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