Machine Learning, Data Science and Deep Learning with Python

Published on: June 19, 2023

Comprehensive free course on machine studying, knowledge science, and deep studying with our arms-on additionally you may free obtain. ✍ Learn TensorFlow, AI, neural networks & sensible expertise to reach a quick-paced business. Start your journey now!

Machine Learning, Data Science and Deep Learning with Python

⚡ Get began with machine studying, knowledge science, and Python at no cost! In this course, you’ll learn to:

Build synthetic neural networks with TensorFlow and Keras.
Implement machine studying at a large scale with Apache Spark’s MLLib.
Classify photos, knowledge, and sentiments utilizing deep studying.
Make predictions utilizing linear regression, polynomial regression, and multivariate regression.
Visualize knowledge with matplotlib and Seaborn.
Utilize reinforcement studying strategies and construct a Pac-Man bot.
Classify knowledge utilizing Okay-Means clustering, help vector machines (SVM), KNN, resolution bushes, naive Bayes, and PCA.
Use prepare/take a look at and Okay-Fold cross-validation to decide on and tune your fashions.
Design and consider A/B assessments utilizing T-assessments and P-values.
And far more! Enroll now in our free machine studying and knowledge science course with Python.

Description

Get began with machine studying and knowledge science at no cost! Our complete course covers the basics you want to succeed on this scorching profession path, together with over 100 lectures spanning 15 hours of video. With arms-on Python code examples and actual-world purposes, you’ll achieve a sensible understanding and utility of key ideas utilized by actual knowledge scientists and machine-studying practitioners at high-tech firms like Google and Amazon. Whether you’re a newbie or have some programming expertise, our course will put together you for a transfer into this thrilling discipline, with subjects starting from A-Z of machine studying, AI, and knowledge mining strategies. Enroll now in our free machine studying and knowledge science course with Python.

Ready to grasp the world of machine studying and deep studying with Python? Our complete course covers all the important thing subjects you want to know, together with:

Deep Learning and Neural Networks (MLPs, CNNs, RNNs) with TensorFlow and Keras
Creating artificial photos with variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs)
Data visualization in Python with Matplotlib and Seaborn
Transfer Learning
Sentiment evaluation
Image recognition and classification
Regression evaluation
Okay-Means Clustering
Principal Component Analysis
Train, take a look at, and cross-validation
Bayesian Methods
Decision Trees and Random Forests
Multiple Regression
Multi-Level Models
Support Vector Machines
Reinforcement Learning
Collaborative Filtering
Okay-Nearest Neighbor
Bias/Variance Tradeoff
Ensemble Learning
Term Frequency/Inverse Document Frequency
Experimental Design and A/B Tests
Feature Engineering
Hyperparameter Tuning

With arms-on coaching and sensible examples, you’ll achieve the talents you want to reach as we speak of quick-paced business. Enroll now in our free machine studying and knowledge science course with Python and begin your journey in the direction of turning into a professional!

Take your machine studying expertise to the following degree with our complete course – and far more! We additionally cowl machine studying with Apache Spark, permitting you to scale up your strategies to investigate “big data” on a computing cluster. If you’re new to Python, don’t fear, we begin with a crash course. Whether you’re a programmer trying to change careers or a knowledge analyst transitioning to the tech business, our course covers the fundamental strategies utilized by actual-world business knowledge scientists. Enroll now and be taught the subjects you want to reach as we speak quick-paced business!

Course Content of machine studying and knowledge science free course:

Getting Started

Introduction
Udemy 101: Getting the Most From This Course
Important be aware
Installation: Getting Started
WINDOWS: Installing and utilizing Anaconda & Course Materials
MAC: Installing and utilizing Anaconda & Course Materials
LINUX: Installing and Using Anaconda & Course Materials
Python Basics
Introducing the Pandas Library

Statistics and Probability Refresher, and Python Practice

Ordinal
Mean, Median, Mode
Using imply, median, and mode in Python
Variation and Standard Deviation
Probability Density Function; Probability Mass Function
Common Data Distributions (Normal, Binomial, Poisson, and so on)
Percentiles and Moments
A Crash Course in Matplotlib
Advanced Visualization with Seaborn
Covariance and Correlation
Conditional Probability
Exercise Solution: Conditional Probability of Purchase by Age
Bayes’ Theorem

Predictive Models

Linear Regression
Polynomial Regression
Multiple Regression, and Predicting Car Prices
Multi-Level Models

Machine Learning with Python

Supervised vs. Unsupervised Learning, and Train/Test
Bayesian Methods: Concepts
Implementing a Spam Classifier with Naive Bayes
Okay-Means Clustering
Clustering individuals primarily based on revenue and age
Measuring Entropy
WINDOWS: Installing Graphviz
MAC: Installing Graphviz
LINUX: Installing Graphviz
Decision Trees: Concepts
Decision Trees: Predicting Hiring Decisions
Ensemble Learning
XGBoost
Support Vector Machines (SVM) Overview
Using SVM to cluster individuals utilizing sci-kit-be taught

Recommender Systems

User-Based Collaborative Filtering
Item-based Collaborative Filtering
Finding Movie Similarities Utilizing Cosine Similarity
Improving the Results of Movie Similarities
Making Movie Recommendations with Item-Based Collaborative Filtering
Improve the recommender’s outcomes

More Data Mining and Machine Learning Techniques

Okay-Nearest-Neighbors: Concepts
Using KNN to foretell a score for a film
Dimensionality Reduction; Principal Component Analysis (PCA)
PCA Example With the Iris knowledge set
Data Warehousing Overview: ETL and ELT
Reinforcement Learning
Reinforcement Learning & Q-Learning with Gym
Understanding a Confusion Matrix
Measuring Classifiers (Precision, Recall, Fl, ROC, AUC)

Dealing with Real-World Data

Bias/Variance Tradeoff
Okay-Fold Cross-Validation to keep away from overfitting
Data Cleaning and Normalization
Cleaning net log knowledge
Normalizing numerical knowledge
Detect
ing outliers
Feature Engineering and the Curse of Dimensionality
Imputation Techniques for Missing Data
Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
Binning, Transforming, Encoding, Scaling, and Shuffling

Apache Spark: Machine Learning on Big Data

Warning about Java 11 and Spark 3!
Spark set up notes for MacOS and Linux customers
Installing Spark – Part I
Installing Spark – Part 2
Spark Introduction
Spark and the Resilient Distributed Dataset (RDD)
Introducing MLlib
Introduction to Decision Trees in Spark
Okay-Means Clustering in Spark
TF / IDF
Searching Wikipedia with Spark
Using the Spark DataBody API for MLLib

Experimental Design / ML within the Real World

Deploying Models to Real-Time Systems
A/B Testing Concepts
T-Tests and P-Values
Hands-on With T-Tests
Determining How Long to Run an Experiment
A/B Test Gotchas

Deep Learning and Neural Networks

Deep Learning Pre-Requisites
The History of Artificial Neural Networks
Deep Learning within the Tensorflow Playground
Deep Learning Details
Introducing TensorFlow
Using TensorFlow
Introducing Keras
Using Keras to Predict Political Affiliations
Convolutional Neural Networks (CNNs)
Using CNNs for handwriting recognition
Recurrent Neural Networks (RNNs)
Using an RNN for sentiment evaluation
Transfer Learning
Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
Deep Learning Regularization with Dropout and Early Stopping
The Ethics of Deep Learning

Generative Models

Variational Auto-Encoders work
Variational Auto-Encoders (VAE) – Hands-on with Fashion MNIST
Generative Adversarial Networks (GANs) – How they work
Generative Adversarial Networks (GANs) – Playing With some demos
Generative Adversarial Networks (GANs) – Hands-on with Fashion MNIST
Learning More about Deep Learning

Final Project

Assignment: Mammogram-Classification
Final venture evaluate
You made it!
More to Explore

Who this course is for:

Software builders or programmers looking for to transition into knowledge science and machine studying careers.
Technologists are interested in deep studying and its purposes.
Data analysts in non-tech industries (like finance) looking for to transition into the tech business and learn to analyze knowledge utilizing code.
Individuals with prior coding or scripting expertise who wish to achieve sensible expertise in knowledge science and machine studying.

Note: If you haven’t any prior coding or scripting expertise, it’s beneficial that you simply take an introductory Python course earlier than taking this machine studying the course.

To get began with our machine studying the course, you’ll want:

A desktop pc (Windows, Mac, or Linux) able to operate Anaconda 3 or newer Don’t fear; we’ll stroll you thru the method of putting in the mandatory free software program.
Prior coding or scripting expertise is beneficial to get essentially the most out of this course.
At least a high school degree in math expertise might be required to grasp key ideas.