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[marketingaiblogs]

Top Skills You Need to Master AI Robotics in 2025

As artificial intelligence (AI) and robotics continue to evolve, the demand for skilled professionals in this field has skyrocketed. Whether you're an aspiring AI robotics engineer or looking to enhance your current skill set, mastering the right skills can propel you to the forefront of this cutting-edge industry. Here's a comprehensive guide to the top skills you need to excel in AI robotics in 2025.

1. Programming Proficiency

Programming is the cornerstone of AI and robotics. Poficiency in languages like Python, C++, and Java is essential. Python, in particular, is widely used in AI due to its simplicity and a vast array of libraries like TensorFlow, PyTorch, and OpenCV. Mastering these languages allows you to develop algorithms, manage data, and create machine learning models effectively.

Key Areas to Focus On:

Object-oriented programming

Algorithms and data structures

Real-time programming for robotics

Recommended Courses:

"Python for Everybody" by University of Michigan (Coursera)

"Programming for AI and Robotics" by AI Certs

"C++ for Robotics" by Udemy

2. Machine Learning and Deep Learning

AI robotics relies heavily on machine learning (ML) and deep learning (DL) techniques to enable robots to learn from data and make decisions. Understanding supervised, unsupervised, and reinforcement learning can give you a competitive edge.

Recommended Tools and Frameworks:

TensorFlow

PyTorch

Scikit-learn

Learning Resources:

"Machine Learning Specialization" by Andrew Ng (Coursera)

"Deep Learning with TensorFlow" by AI Certs

"Deep Learning A-Z" by Udemy

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3. Computer Vision

Robots often rely on computer vision to perceive and interpret their surroundings. Skills in computer vision involve working with image processing, feature extraction, and object detection techniques.

Key Technologies:

OpenCV

YOLO (You Only Look Once)

Convolutional Neural Networks (CNNs)

Recommended Courses:

"Computer Vision Basics" by University at Buffalo (Coursera)

"Applied Computer Vision with OpenCV" by AI Certs

"Mastering YOLO for Real-World Applications" by Edureka

4. Robotics Process Automation (RPA)

RPA involves automating repetitive tasks using robots. Proficiency in RPA tools like UiPath, Automation Anywhere, and Blue Prism is a valuable skill for integrating AI with robotics.

Applications:

Automating assembly lines

Streamlining back-office operations

Optimizing logistics

Recommended Courses:

"RPA Developer Training" by UiPath Academy

"Robotic Process Automation Certification" by AI Certs

"RPA for Beginners" by Simplilearn

5. Mathematics and Statistical Analysis

A strong foundation in mathematics is critical for understanding algorithms and optimizing models. Key areas include linear algebra, calculus, and probability.

Practical Use Cases:

Calculating robot kinematics and dynamics

Designing neural network architectures

Analyzing model performance using statistical metrics

Recommended Courses:

"Mathematics for Machine Learning" by Imperial College London (Coursera)

"Statistics and Probability for AI" by AI Certs

"Advanced Math for Data Science" by Udemy

6. Data Engineering and Big Data Analysis

AI robotics thrives on data. The ability to collect, clean, and analyze data is crucial. Understanding big data tools like Hadoop, Spark, and SQL will help you handle large datasets effectively.

Focus Areas:

Data preprocessing

Feature selection and extraction

Handling structured and unstructured data

Recommended Courses:

"Big Data Engineering" by IBM (Coursera)

"Data Science and Big Data Foundations" by AI Certs

"Apache Spark for Data Engineers" by Pluralsight

7. Embedded Systems and Hardware Skills

Robots are not just about software; they also require sophisticated hardware. Knowledge of embedded systems, microcontrollers (like Arduino and Raspberry Pi), and sensors can significantly enhance your skill set.

Hands-On Experience:

Building and programming microcontroller-based robots

Integrating sensors for real-time data collection

Optimizing hardware-software interaction

Recommended Courses:

"Introduction to Embedded Systems" by University of Texas (edX)

"Arduino and Robotics Bootcamp" by AI Certs

"Raspberry Pi for Beginners" by Udemy

8. Natural Language Processing (NLP)

As robots become more interactive, NLP is crucial for enabling machines to understand and process human language. Skills in NLP empower you to work on projects like voice assistants and conversational robots.

Popular Tools:

NLTK (Natural Language Toolkit)

spaCy

Hugging Face Transformers

Recommended Courses:

"Natural Language Processing Specialization" by deeplearning.ai (Coursera)

"AI-Driven NLP Solutions" by AI Certs

"NLP Fundamentals" by Simplilearn

9. Soft Skills and Collaboration

In addition to technical expertise, soft skills play a pivotal role in AI robotics. Problem-solving, critical thinking, and teamwork are essential when working in multidisciplinary teams.

Why It Matters:

Collaboration with designers, engineers, and business strategists ensures the success of robotics projects.

Recommended Resources:

"Critical Thinking Masterclass" by LinkedIn Learning

"Team Collaboration for AI Projects" by AI Certs

"Problem-Solving Techniques" by edX

10. Ethics and Compliance in AI Robotics

As AI becomes more pervasive, ethical considerations and compliance are gaining importance. Familiarity with AI ethics, data privacy laws, and responsible AI practices can set you apart as a forward-thinking professional.

Key Principles:

Ensuring transparency and fairness in AI models

Addressing bias and discrimination in algorithms

Adhering to regulations like GDPR and AI Act

Recommended Courses:

"AI Ethics and Responsible AI" by University of Helsinki (Elements of AI)

"Ethics in AI Systems" by AI Certs

"AI Compliance Fundamentals" by Coursera

Final Thoughts

Mastering AI robotics in 2025 requires a blend of technical, analytical, and interpersonal skills. By staying updated with the latest tools and technologies and honing your expertise in these areas, you can position yourself as a leader in this transformative field. Start building your skills today, and be part of the future-shaping industries worldwide.

[fromdevcom]

TensorFlow is a powerful machine learning framework for deep learning. This open-source tool has changed the speed of development in the supervised machine learning area. Using TensorFlow for training machines learning model is very common these days. Large machine learning leader companies are already using TensorFlow on a large scale. Learning TensorFlow has also become super convenient these days with a ton of free tutorials and courses online. I have compiled a list of the best free online resources that will help you learn TensorFlow quickly. Note: Foundation Courses TensorFlow is a specialized machine learning environment that focuses on supervised machine learning. In case you are an absolute beginner in ML space, you may find this list of ML tutorials handy  Free YouTube Courses On TensorFlow  YouTube is full of useful videos, however, it gets really hard to find in-depth courses that may be worth your time. Below is a shortlist of best courses to learn TensorFlow on YouTube, some of them are also advertisement free.  TensorFlow in 5 Minutes | Introduction To TensorFlow | Deep Learning Using TensorFlow | Edureka - YouTube TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial - YouTube Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial - YouTube Coding TensorFlow - YouTube TensorFlow 2.0 Crash Course - YouTube TensorFlow Tutorials - YouTube TensorFlow 2.0 Tutorials for Beginners - YouTube Introduction to TensorFlow 2.0: Easier for beginners, and more powerful for experts (TF World '19) - YouTube Coursera Courses On TensorFlow Coursera a popular choice for online courses. Below are some courses available on Coursera.  TensorFlow in Practice | Coursera Machine Learning with TensorFlow on Google Cloud Platform Getting started with TensorFlow 2 Basic Image Classification with TensorFlow Other Courses Online There are many other websites that have good online tutorials. These are not video courses, however can be handy for beginners. Official website documentation - https://www.tensorflow.org/tutorials Datacamp tutorials on TensorFlow - https://www.datacamp.com/community/tutorials/tensorflow-tutorial TensorFlow training by Guru99 - https://www.guru99.com/tensorflow-tutorial.html Python TensorFlow tutorial - https://adventuresinmachinelearning.com/python-tensorflow-tutorial/ Summary I hope you find this list useful. Do you know about more useful courses related to TensorFlow? Please share it with me in the comments section.

[naik0909]

Data Science tensorflow

Data Science from Edureka learning center is now in Thane. We will equip you with the tools and necessary skills to become a Data Scientist    You will learn tools, languages and framework like Python, Numpy, Pandas, matplotlib, NLTK, TensorFlow, Tableau. On successful completion of the course, you are ready for job roles of a Data Analyst, Data Visualization Specialist, Data engineer and more. For more details you can contact us

For More Information

Name:- edurekathane

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[andre-the-analyst]

Deep Learning Live - 3 | TensorFlow Tutorial | Deep Learning Using TensorFlow Training | #Edureka #AI DataScience #DeepLearning 🔥Edureka TensorFlow Training - This Edureka TensorFlow Tutorial video (Blog: will help you in understanding various important basics of TensorFlow.

[360digitmg-training]

Data Science Course Training Institute In Hyderabad

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Text bracket, Document vectors, Text bracket using Doc2vec In this module, you will be tutored in more about Text Bracket and Document Vectors using Doc2vec. ADF, Random perambulation and bus Arima In this module, you will study ADF, Random walk, and Auto Arima styles used in the Time Series. Smoothing This module will educate you tips on how to use this fashion for univariate knowledge. 

 The design is the ultimate step in Data Science coaching and will allow you to to point out your moxie in Data Science to employers. The Data Science Course Master's program is a planned studying path that's really helpful by high business experts. You may also develop a proficiency within the newest movie about AI & Deep literacy by using Tensorflow after which Data Visualization using Tableau. Individual courses at Edureka are about the specialisation of the world of one or two specific areas, nevertheless, you need to be a Data Scientist, also that's the stylish route to take. According to Glassdoor, Data scientists are the alternate-stylish job in 2021. Data Scientists are the stylish sought- after in the field of information wisdom still, the demand for information scientists in Hyderabad is scarce which implies there are lots of druthers

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As information becomes increasingly complicated and substantial, companies bear advisers . This boosts the way forward for data wisdom as companies come reliant on data- driven decision- timber. the party should enrol for the StarAgile job assure program and complete the training as described by the placement group. party ought to complete all the conditions to come eligible for interviews. The providers of a data scientist are necessary for healthcare, retail enterprise, telecommunications, automotive, digital Marketing, professional providers, and cyber securities diligence. StarAgile chooses professional preceptors with time and education moxie. 

  The data science training in hyderabad  will train you in lots of data wisdom instruments and programming tools, which will help you work your information to work with a company of your volition. Different enterprises use different tools, so knowledge of the favoured instruments makes you stand out from the job request competition. The instructing workers are nice, they give us day wise assignments which helped me to use the algorithms of machine literacy and likewise give us the backup classes. The entry they give could be veritably useful to pay attention to the assignments constantly. The Deep literacy Course with TensorFlow Certification by Edureka is curated with the backing of educated assiduity professionals as per the newest musts & demands. This Deep studying instrument course will help you to master in style algorithms like CNN, RCNN, RNN, LSTM, RBM exercising the rearmost TensorFlow 2. zero pack in Python. 

360DigiTMG presents basically the most applicable and up- to- date Data Science course in Hyderabad in cooperation with IIT Madras. The course class is designed by high assiduity professionals with expansive moxie in this sphere. I enrolled in 360DigiTMG's course to support my career and it was a fantastic moxie. 

  This Data Science with R Training encompasses an abstract understanding of Statistics, Time Series, Text Mining and a preface to Deep Learning. Throughout the R Programming Course, you'll apply real- life use- cases on Media, Healthcare, Social Media, Aviation and HR. There are fewer job openings available for the data wisdom and analytics positions in India alone. As the preceptors have business experience they can simply juggle between scholars and professionals at Data Science guiding in Hyderabad. 

 Also, you'll have complete access to the IBM Watson Cloud Lab for Chatbots. To become a knowledge scientist, one will need to have a bachelor's degree in a computer- related area or information wisdom. instruments and degrees from honoured universities ore-learning institutes add a structure to your knowledge and wisdom career. Data Science charge camps will help you turn out to be a master in this area. 

  Misclassification, Probability, AUC, R- Square This module will give everyone tips on how to work with Misclassification, Probability, AUC, and R- Forecourt. AIC, BIC, Model Fitting, Training and Test Data In this module, you'll learn every little thing you want to know about a number of models similar as AIC, BIC, Model Fitting, Training, and Test Data. The posterior module is Machine literacy that will teach us all the Machine Learning strategies from scratch, and the popularly used Classical ML algorithms that fall in each of the classes. In this module, you will learn the forms of joins and learn how to mix information. Chi- Square is a thesis testing methodology employed in Statistics, which is used to measure how a model compares to precise information. One Sample T- Test One- Sample T- Test is a thesis testing methodology employed in Statistics. 

 Also, in India, there was a 20 enhancement for the Data Scientist function in comparison with the final time on the report submitted byPayScale.com. Since Data Science is a multidisciplinary area the job openings for this subject are immense. Listed below are the colourful aqueducts of businesses that rent Data Scientist campaigners. 

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 Data wisdom is a subject that is known among massive companies for helping them make income. Data collected over a long term will occasionally present patterns that could presumably be used to prognosticate the conduct of the request and thus make a profit using that knowledge. The area has lots of reaches as several pots and associations are counting on data to make a profit. This should indicate ample jobs for someone who is well clued in understanding and working with a large quantum of delicate knowledge. The campaigners are awarded a Data Science course completion instrument as soon as they complete the data wisdom course in Hyderabad successfully. 

For more information

360DigiTMG - Data Analytics, Data Science Course Training Hyderabad  

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[aiinspireddotcom]

7 Free Complete Artificial Intelligence Video Tutorials (53+ Hours)

7 Free Artificial Intelligence Video Tutorials In this post, We list out 7 handpicked Artificial Intelligence video tutorials from YouTube where each AI video tutorial is more than 4 hours in duration and having millions of views. We always encourage learners to utilize video tutorials that are thorough and cover the fundamentals completely. The collection contains tutorials worth of more than 53 hours. Why AI is Important in 2021 In this collection, we have listed video tutorials based on the contents in such a way that if you to start from the beginning, you can start in the below sequential order to have comprehensive knowledge in the field of artificial intelligence. These AI tutorials cover core concepts of Artificial Intelligence, Machine Learning, Deep Learning, Python and other libraries and frameworks with hands on practical's. #1 – Artificial Intelligence Tutorial for Beginners by Edureka This video tutorial will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples. Following topics are covered in this AI tutorial - 00:00 Introduction - 02:27 History Of AI - 06:45 Demand For AI - 08:46 What Is Artificial Intelligence? - 09:50 AI Applications - 16:49 Types Of AI - 20:24 Programming Languages For AI - 27:12 Introduction To Machine Learning - 28:08 Need For Machine Learning - 31:48 What Is Machine Learning? - 34:13 Machine Learning Definitions - 37:26 Machine Learning Process - 49:13 Types Of Machine Learning - 49:21 Supervised Learning - 52:00 Unsupervised Learning - 53:44 Reinforcement Learning - 55:29 Supervised vs Unsupervised vs Reinforcement Learning - 58:23 Types Of Problems Solved Using Machine Learning - 1:04:49 Supervised Learning Algorithms - 1:05:17 Linear Regression - 1:11:20 Linear Regression Demo - 1:26:36 Logistic Regression - 1:35:36 Decision Tree - 1:55:18 Random Forest - 2:07:31 Naive Bayes - 2:14:37 K Nearest Neighbour (KNN) - 2:20:31 Support Vector Machine (SVM) - 2:26:40 Demo (Classification Algorithms) - 2:42:36 Unsupervised Learning Algorithms - 2:42:45 K-means Clustering - 2:50:49 Demo (Unsupervised Learning) - 2:56:40 Reinforcement Learning - 3:24:36 Demo (Reinforcement Learning) - 3:31:41 AI vs Machine Learning vs Deep Learning - 3:33:08 Limitations Of Machine Learning - 3:36:32 Introduction To Deep Learning - 3:38:36 How Deep Learning Works? - 3:40:48 What Is Deep Learning? - 3:41:50 Deep Learning Use Case - 3:43:14 Single Layer Perceptron - 3:50:56 Multi Layer Perceptron (ANN) - 3:52:55 Backpropagation - 3:54:39 Training A Neural Network - 4:01:02 Limitations Of Feed Forward Network - 4:03:18 Recurrent Neural Networks - 4:05:36 Convolutional Neural Networks - 4:09:00 Demo (Deep Learning) - 4:29:02 Natural Language Processing - 4:30:53 What Is Text Mining? - 4:32:43 What Is NLP? - 4:33:26 Applications Of NLP - 4:35:53 Terminologies In NLP - 4:41:19 NLP Demo - 4:47:21 Machine Learning Masters Program https://www.youtube.com/watch?v=JMUxmLyrhSk Tutorial Length: 04 Hours & 53 Minutes #2 - Artificial Intelligence Full Course by SimpleLearn This video tutorial on Artificial Intelligence Course helps you understand the basics of artificial intelligence. This also covers the future of AI, some of the industry experts view on AI and what they have to say about AI. You will see the top 10 applications of AI in 2021. Then, you will understand about Machine Learning and Deep Learning and the different algorithms used to build AI models. Finally, you will learn the Top 10 Artificial Intelligence Technologies In 2021 Following topics are covered in this AI video tutorial - 00:00:00 Artificial Intelligence  in 5 min - 00:05:59 Future Of Artificial Intelligence - 00:13:20 Artificial Intelligence Application 2021 - 00:25:38 Should we be afraid of Artificial Intelligence - 00:38:21 What is Artificial Intelligence - 00:48:13 Machine Learning Part 1 - 01:21:57 Linear Regression Analysis - 01:41:34 Decision Tree - 01:58:12 Machine Learning Part 2 - 02:51:14 KNN algorithm Using Python - 03:17:40 Mathematics For Machine Learning - 05:07:53 Deep Learning Tutorial - 05:52:46 TensorFlow 2.0 Tutorial for Beginners - 07:18:44 Top 10 Artificial Intelligence Technologies in 2021 https://www.youtube.com/watch?v=PXwUEJVSAeA Tutorial Length: 07 Hours & 28 Minutes #3 - AI Full Course for Beginners in 9 Hours by Clever Programmer In this tutorial, following projects are covered - 00:03:02 - Face Detector App - 02:08:42 - Self Driving Car App - 04:11:47 - Smile Detector App - 06:47:03 - TensorFlow Image Classifier App https://www.youtube.com/watch?v=XIrOM9oP3pA Tutorial Length: 08 Hours & 58 Minutes #4 - Artificial Intelligence Course by Intellipaat In this artificial intelligence tutorial you will learn end to end about ai and it's vast domain. So this artificial intelligence course is an exhaustive tutorial for you to get started with AI Following topics are covered in this video - 0:00 - Artificial Intelligence tutorial - 01:41 - Dartmouth conference - 04:23 - What is Artificial Intelligence - 07:30 - Timeline of Artificial Intelligence - 19:00 - Types of Artificial Intelligence - 21:47 - What makes Artificial Intelligence, Intelligent? - 24:42 - Fun fact about Artificial Intelligence - 26:03 - Dark Side of Artificial Intelligence - 27:15 - Myths vs Facts of Artificial Intelligence - 30:30 - Domains of Artificial Intelligence - 32:50 - Final thoughts on Artificial Intelligence - 33:26 - What is Intelligence? - 34:35 - What makes Humans Intelligent? - 35:45 - Difference between AI & Ml & Deep Learning - 38:05 - Machine Learning real time Applications - 41:05 - What is Machine Learning - 41:40 - How does Machine Learn? - 42:45 - Types of Machine Learning - 49:33 - Machine Learning Algorithms - 50:04 - Limitations of Machine Learning - 51:40 - Introduction to Deep Learning - 54:05 - Application of Deep Learning - 55:15 - How deep Learning Works? - 56:34 - What is a Neural Network? - 57:25 - Artificial Neural Network(ANN) - 58:35 - Topology of a Neural Network - 59:28 - How do Neurons work? - 01:00:03 - Artificial Neurons in detail - 01:01:07 - How does the Perceptron work? - 01:02:00 - Concept of weights - 01:02:25 - Why do we need Activation Functions? - 01:04:00 - Types of Activation Functions - 01:06:47 - Training a Perceptron - 01:07:49 - Perceptron Training Algorithm - 01:08:38 - Benefits of using Artificial Neural Network - 01:10:24 - Deep Learning Frameworks - 01:13:55 - What are Tensors? - 01:14:53 - Computational Graph - 01:15:51 - Program Elements in TensorFlow - 01:16:15 - Working on constants in Jupiter note book - 01:24:10 - Working on Placeholders in Jupiter note book - 01:30:12 - Working on Variables in Jupiter note book - 01:36:34 - Introduction to Neural Networks in Jupiter notebook - 02:23:55 - Multi layer Perceptron Architecture - 03:09:40 - Working in TensorFlow - 04:21:18 - Convolutional Neural Network (CNN) - 05:08:23 - Demo on CNN - 06:19:34 - Face Recognition Project in Artificial Intelligence https://www.youtube.com/watch?v=UkZzM-jxLv4 Tutorial Length: 06 Hours & 29 Minutes #5 - Artificial Intelligence Course For Working Professionals by Intellipaat In this Artificial Intelligence Full Course video, you will learn end to end about AI and ML its vast domain. So this Artificial Intelligence video covers all the topics of AI and ML along with Machine Learning projects, Artificial Intelligence projects, and interview questions is an exhaustive tutorial for you to get started with AI. This AI & ML Course for Beginners is a must-watch video for everyone who wishes to learn AI and make a career in it. https://www.youtube.com/watch?v=c5M5m62IhjU Tutorial Length: 07 Hours & 29 Minutes #6 - Artificial Intelligence and Machine Learning For Beginners by Intellipaat In this Artificial Intelligence and Machine Learning For Beginners video, you will learn end to end about AI and ML its vast domain. So this Artificial Intelligence Machine Learning video covers all the topics of AI and ML along with Machine Learning projects, Artificial Intelligence projects and interview questions is an exhaustive tutorial for you to get started with AI. This AI & ML Course for Beginners is a must-watch video for everyone who wishes to learn AI and make a career in it. The following topics are covered in this video - 0:00 - Introduction - 01:31 - Why Artificial Intelligence? - 07:06 - What is Intelligence? - 09:10 - Difference between AI, ML, & DL - 11:38 - Machine Learning around You! - 14:39 - Introduction to Machine Learning - 16:24 - Machine Learning Types - 24:12 - Machine Learning Algorithms - 25:34 - Limitations of Machine Learning - 26:12 - Introduction to Deep Learning - 29:06 - Applications of Deep Learning - 29:43 - How Does Deep Learning Work? - 33:03 - What is Neural Network? - 38:47 - Artificial Neurons - 02:05:43 - Why do we Need Activation Functions? - 02:06:16 - Types of Activation Functions - 02:40:24 - Benefits of Using Artificial Neural Networks - 02:41:41 - Perceptron Training Algorithm - 02:42:35 - Deep Learning Frameworks - 02:45:21 - TensorFlow - 02:48:04 - Keras - 02:52:49 - PyTorch - 02:55:01 - DL4J - 02:58:00 - MXNet - 03:00:00 - What are Tensors? - 03:02:59 - Computational Graph - 03:05:38 - Program Elements in TensorFlow - 03:23:43 - Use Case 1 - 03:31:30 - Multi-layer Perceptron - 03:33:04 - Use Case 1: Solution - 03:43:39 - Backpropagation Algorithm - 04:27:23 - Problem Statement - 05:54:25 - What is Fraud? - 05:55:12 - Types of Frauds - 05:55:55 - Rule-Based Approach for Fraud Detection - 05:57:23 - Data Science for Fraud Detection - 06:01:17 - Challenges of Fraud Detection Model - 06:56:50 - Comparison AI vs ML. - 07:05:03 - When to use: AI or ML? - 07:06:38 - Why do we need Artificial Intelligence? - 07:08:57 - Artificial Intelligence Developers - 07:17:17 - Step by Step Guid: AI Developer - 07:22:55 - AI Developer: Job Descriptions - 07:24:34 - AI Developer: Average Salary - 07:25:21 - Artificial Intelligence Interview Questions https://www.youtube.com/watch?v=PoTarJp7Pxk Tutorial Length: 08 Hours & 22 Minutes #7 - Machine Learning Full Course - Learn Machine Learning 10 Hours by Edureka This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms Below are the topics covered in this Machine Learning Tutorial for Beginners video - 00:00 Introduction - 2:47 What is Machine Learning? - 4:08 AI vs ML vs Deep Learning - 5:43 How does Machine Learning works? - 6:18 Types of Machine Learning - 6:43 Supervised Learning - 8:38 Supervised Learning Examples - 11:49 Unsupervised Learning - 13:54 Unsupervised Learning Examples - 16:09 Reinforcement Learning - 18:39 Reinforcement Learning Examples - 19:34 AI vs Machine Learning vs Deep Learning - 22:09 Examples of AI - 23:39 Examples of Machine Learning - 25:04 What is Deep Learning? - 25:54 Example of Deep Learning - 27:29 Machine Learning vs Deep Learning - 33:49 Jupyter Notebook Tutorial - 34:49 Installation - 50:24 Machine Learning Tutorial - 51:04 Classification Algorithm - 51:39 Anomaly Detection Algorithm - 52:14 Clustering Algorithm - 53:34 Regression Algorithm - 54:14 Demo: Iris Dataset - 1:12:11 Stats & Probability for Machine Learning - 1:16:16 Categories of Data - 1:16:36 Qualitative Data - 1:17:51 Quantitative Data - 1:20:55 What is Statistics? - 1:23:25 Statistics Terminologies - 1:24:30 Sampling Techniques - 1:27:15 Random Sampling - 1:28:05 Systematic Sampling - 1:28:35 Stratified Sampling - 1:29:35 Types of Statistics - 1:32:21 Descriptive Statistics - 1:37:36 Measures of Spread - 1:44:01 Information Gain & Entropy - 1:56:08 Confusion Matrix - 2:00:53 Probability - 2:03:19 Probability Terminologies - 2:04:55 Types of Events - 2:05:35 Probability of Distribution - 2:10:45 Types of Probability - 2:11:10 Marginal Probability - 2:11:40 Joint Probability - 2:12:35 Conditional Probability - 2:13:30 Use-Case - 2:17:25 Bayes Theorem - 2:23:40 Inferential Statistics - 2:24:00 Point Estimation - 2:26:50 Interval Estimate - 2:30:10 Margin of Error - 2:34:20 Hypothesis Testing - 2:41:25 Supervised Learning Algorithms - 2:42:40 Regression - 2:44:05 Linear vs Logistic Regression - 2:49:55 Understanding Linear Regression Algorithm - 3:11:10 Logistic Regression Curve - 3:18:34 Titanic Data Analysis - 3:58:39 Decision Tree - 3:58:59 what is Classification? - 4:01:24 Types of Classification - 4:08:35 Decision Tree - 4:14:20 Decision Tree Terminologies - 4:18:05 Entropy - 4:44:05 Credit Risk Detection Use-case - 4:51:45 Random Forest - 5:00:40 Random Forest Use-Cases - 5:04:29 Random Forest Algorithm - 5:16:44 KNN Algorithm - 5:20:09 KNN Algorithm Working - 5:27:24 KNN Demo - 5:35:05 Naive Bayes - 5:40:55 Naive Bayes Working - 5:44:25Industrial Use of Naive Bayes - 5:50:25 Types of Naive Bayes - 5:51:25 Steps involved in Naive Bayes - 5:52:05 PIMA Diabetic Test Use Case - 6:04:55 Support Vector Machine - 6:10:20 Non-Linear SVM - 6:12:05 SVM Use-case - 6:13:30 k Means Clustering & Association Rule Mining  - 6:16:33 Types of Clustering - 6:17:34 K-Means Clustering - 6:17:59 K-Means Working - 6:21:54 Pros & Cons of K-Means Clustering - 6:23:44 K-Means Demo - 6:28:44 Hierarchical Clustering - 6:31:14 Association Rule Mining - 6:34:04 Apriori Algorithm - 6:39:19 Apriori Algorithm Demo - 6:43:29 Reinforcement Learning - 6:46:39 Reinforcement Learning: Counter-Strike Example - 6:53:59 Markov's Decision Process - 6:58:04 Q-Learning - 7:02:39 The Bellman Equation - 7:12:14 Transitioning to Q-Learning - 7:17:29 Implementing Q-Learning - 7:23:33 Machine Learning Projects - 7:38:53 Who is a ML Engineer? - 7:39:28 ML Engineer Job Trends - 7:40:43 ML Engineer Salary Trends - 7:42:33 ML Engineer Skills - 7:44:08 ML Engineer Job Description - 7:45:53 ML Engineer Resume - 7:54:48 Machine Learning Interview Questions https://www.youtube.com/watch?v=GwIo3gDZCVQ Tutorial Length: 09 Hours & 38 Minutes Below, we have shared a carefully crafted 11 Complete Free Programming Video Tutorials in Tamil for the students and anyone who would like to learn programming or start a career in Programming. 11 Free Programming Video Tutorials in Tamil (65+ Hours) Share with everyone and show them you care for them. 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[anytutor37-com]

TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | Deep Learning & TensorFlow | Edureka ##learnonline #tutors #classroom #professionals #onlinestudents #coronavirus #training #learnenglish #distancelearning #onlineeducation #lockdown #career #graduation #homeschool contentstudio.page.link/vrft

[omarkhanca]

tensorflow > TensorBoard Tutorial | Graph Visualization Using TensorBoard | TensorFlow Tutorial | Edureka | 2018-12-10T05:34:33.000Z

[andre-the-analyst]

TensorFlow 2.0 Tutorial - Part 1 | Introduction To TensorFlow 2.0 | #TensorFlow #Edureka #tensorflowtutorial #tensorflow2 #pythonprojects #pythontutorial #PythonTraining #TensorFlowTraining #Python #Datalytical 🔥Edureka TensorFlow Training: Edureka TensorFlow 2.0 Tutorial - Part 1 ( Part 2 - ) covers the basics of TensorFlow with various new features and applications with respect to AI and Deep Learning.

[andre-the-analyst]

Deep Learning Live - 2 | Neural Network Tutorial | Deep Learning Training | #Edureka #AI DataScience #DeepLearning 🔥Edureka TensorFlow Training - This video will provide you with a brief and crisp knowledge of Neural Networks, how they work, the various parameters involved in the whole Deep Learning Process.

[andre-the-analyst]

Stock Prediction using Machine Learning and Python | Machine Learning Training | Edureka #datascience #MachineLearning 🔥NIT Warangal Post Graduate Program in AI & Machine Learning with Edureka: This Edureka "Stock Prediction using Machine Learning" takes you through the basic process of predicting the trends of stock prices using machine learning architecture of LSTM while also making use of prominent Python Libraries such as Tensorflow, Keras, etc.

[omarkhanca]

tensorflow > TensorBoard Tutorial | Graph Visualization Using TensorBoard | TensorFlow Tutorial | Edureka | 2018-12-10T05:34:33.000Z

[omarkhanca]

pytorch > Keras vs Tensorflow vs PyTorch | Deep Learning Frameworks Comparison | Edureka | 2018-11-26T05:34:49.000Z