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Guide To Ai & Ml Developer Tools
Last updated 1/2020
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.70 GB | Duration: 7h 27m
A complete guide to master the most important tools used in machine learning by developers around the globe
What you'll learn
Introduction to Machine Learning
Pandas
Scipy
Data Visualization using Matplotlib
Tensorflow
Keras
Requirements
Basic knowledge of Python
Description
With self-driving cars, smart robots, to even your coffee machines, AI has become a prominent technology that cannot be overlooked. The integration of Artificial Intelligence is growing and businesses from multiple sectors are now looking to build technologies including AI to empower their strategies. This has increased the demands of AI & ML skilled experts. Considering this, we have curated this course that revolve around all the essential tools that are required by developers to build real-world AI & ML models.Why you should take this course?With machine learning as the future of technology, getting your hands on this type of development is crucial. However, it isn't easy. This is why we have designed this course that covers a variety of different machine learning tools used by developer around the world. It includes in-depth information on libraries like Pandas, Matplotlib, Scipy and so much more.This course includes :How to get started with Anacondas and JupyterBrief introduction to Tensorflow and KerasData ExplorationDecision making algorithmsClustering TechniquesNeural NetworksPractical projects and much more!So, what are you waiting for? Enroll now into this course and we'll see you on the other side!
Overview
Section 1: Anaconda and Jupyter
Lecture 1 Introduction
Lecture 2 Strategies for Learning Packages
Lecture 3 Useful Features with Jupyter and Anaconda
Section 2: Pandas
Lecture 4 Loading and Describing Data
Lecture 5 Dataframe Indexing
Lecture 6 Exploring Real Data
Lecture 7 Other Important Logic
Lecture 8 Problem
Lecture 9 Answer
Section 3: Matplotlib
Lecture 10 Introduction to matplotlib
Lecture 11 Bar and Line Plots
Lecture 12 Box Plots and Subplotting
Lecture 13 Plotting with Real Data
Lecture 14 Problem
Lecture 15 Answer
Section 4: Sklearn
Lecture 16 Introduction to Machine Learning
Lecture 17 Machine Learning Workflow
Lecture 18 Cross-validation and Scoring
Lecture 19 SVM and Visualizing Boundaries
Lecture 20 Random Forest and Decision Trees
Lecture 21 KNN and K-Means Clustering
Lecture 22 GMMs and Visualizing Clusters
Lecture 23 Problem
Lecture 24 Answer
Section 5: Scipy
Lecture 25 Introduction to scipy Features
Lecture 26 Audio Signals and Spectrograms
Lecture 27 Image Processing and Fourier Transforms
Lecture 28 Problem
Lecture 29 Answer
Section 6: TensorflowKeras
Lecture 30 Introduction to Neural Networks
Lecture 31 Introduction to Tensorflow and Keras
Lecture 32 Building a Simple Neural Network in Keras
Lecture 33 Problem
Lecture 34 Answer
Section 7: Project
Lecture 35 Problem Statement
Lecture 36 Solution
Those who want to understand the use of Python libraries like Pandas,Scipy etc,Those who want to get started with Tensorflow and Keras
Homepage
https://www.udemy.com/course/ai-machine-learning-tools/
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