Machine Learning, Data Science and Deep Learning with Python

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!

⚡ 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
• Detecting 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.

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