Description

Price: 8.00 USD | Size: 7.71 GB  | Duration :  14 Hours  | 104 Video Lessons | ⭐️⭐️⭐️⭐️⭐️ 4.9

BRAND : Expert TRAINING | ENGLISH |  Bonus :  Machine Learning, Data Science PDF Guides | INSTANT DOWNLOAD 

 

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

 

What you’ll learn

 

Build artificial neural networks with Tensorflow and Keras

Implement machine learning at massive scale with Apache Spark’s MLLib

Classify images, data, and sentiments using deep learning

Make predictions using linear regression, polynomial regression, and multivariate regression

Data Visualization with MatPlotLib and Seaborn

Understand reinforcement learning – and how to build a Pac-Man bot

Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA

Use train/test and K-Fold cross validation to choose and tune your models

Build a movie recommender system using item-based and user-based collaborative filtering

Clean your input data to remove outliers

Design and evaluate A/B tests using T-Tests and P-Values

 

Requirements

You’ll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software.

Some prior coding or scripting experience is required.

At least high school level math skills will be required.

Description

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We’ll cover the A-Z of machine learning, AI, and data mining techniques real employers are looking for, including:

 

Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and Keras

Creating synthetic images with Variational Auto-Encoders (VAE’s) and Generative Adversarial Networks (GAN’s)

Data Visualization in Python with MatPlotLib and Seaborn

Transfer Learning

Sentiment analysis

Image recognition and classification

Regression analysis

K-Means Clustering

Principal Component Analysis

Train/Test and cross validation

Bayesian Methods

Decision Trees and Random Forests

Multiple Regression

Multi-Level Models

Support Vector Machines

Reinforcement Learning

Collaborative Filtering

K-Nearest Neighbor

Bias/Variance Tradeoff

Ensemble Learning

Term Frequency / Inverse Document Frequency

Experimental Design and A/B Tests

Feature Engineering

Hyperparameter Tuning

 

 

Who this course is for:

 

Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.

Technologists curious about how deep learning really works

Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you’ll need some prior experience in coding or scripting to be successful.

If you have no prior coding or scripting experience, you should NOT take this course – yet. Go take an introductory Python course first.