The integration of Artificial Intelligence is growing and businesses from multiple sectors are now looking to build technologies including AI to empower their strats. 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 courseWith 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 Strats 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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