329 Algorithms courses

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Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning? . Machine learning classes in pune The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

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Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data 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 Experimental Design / ML in 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 Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Machine Learning Essentials with Python (TTML5506-P) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

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Introduction Kurt Anderson - Promo FREE 00:02:00 Kurt Anderson - 1 Introduction 00:01:00 Kurt Anderson - 2 Binary System 00:11:00 Analyzing Algorithms Kurt Anderson - 3 Complexity Introduction 00:02:00 Kurt Anderson - 4 Math Refresher Logarithmic Functions 00:11:00 Kurt Anderson - 5 Math Refresher Factorial Functions.TS 00:03:00 Kurt Anderson - 6 Math Refresher Algebraic Expressions.TS 00:03:00 Kurt Anderson - 7 n-notation 00:18:00 Kurt Anderson - 8 Big O 00:13:00 Kurt Anderson - 9 Big O Real World Example 00:10:00 Arrays Kurt Anderson - 10 How is Data Stored 00:09:00 Kurt Anderson - 11 Fixed Arrays 00:20:00 Kurt Anderson - 12 Circular Arrays 00:08:00 Kurt Anderson - 13 Dynamic Arrays 00:16:00 Kurt Anderson - 14 Array Review 00:08:00 Kurt Anderson - 15 Array Real World Examples 00:06:00 Linked Lists Kurt Anderson - 16 Nodes 00:04:00 Kurt Anderson - 16 Linked List 00:14:00 Kurt Anderson - 17 Linked List Run Times 00:15:00 Kurt Anderson - 18 Doubly Linked Lists 00:08:00 Kurt Anderson - 19 Tail Pointer 00:05:00 Kurt Anderson - 20 Linked List Real World Examples 00:03:00 Kurt Anderson - 21 Linked List Review 00:04:00 Stacks and Queues Kurt Anderson - 22 Stacks 00:10:00 Kurt Anderson - 20 Stack Example 00:11:00 Kurt Anderson - 23 Queues 00:09:00 Kurt Anderson - 24 Queue Examples 00:10:00 Kurt Anderson - 25 Queue and Stack Run Times 00:06:00 Kurt Anderson - 26 Stack and Queues Real World Examples 00:07:00 Sorting Algorithms Kurt Anderson - 27 Sorting Algorithm Introdcution 00:02:00 Kurt Anderson - 28 Bubble Sort 00:10:00 Kurt Anderson - 29 Selection Sort 00:10:00 Kurt Anderson - 30 Insertion Sort 00:09:00 Kurt Anderson - 31 Quick Sort 00:15:00 Kurt Anderson - 32 Quick Sort Run Times 00:10:00 Kurt Anderson - 33 Merge Sort 00:12:00 Kurt Anderson - 34 Merge Sort Run Times 00:08:00 Kurt Anderson - 35 Stable vs Nonstable 00:07:00 Kurt Anderson - 36 Sorting Algorithm Real World Examples 00:04:00 Trees Kurt Anderson - 37 Basics of Trees 00:08:00 Kurt Anderson - 38 Binary Search Tree 00:09:00 Kurt Anderson - 39 BST Run Times 00:08:00 Kurt Anderson - 40 Tree Traversals 00:13:00 Kurt Anderson - 41 Tree Real World Examples 00:05:00 Heaps Kurt Anderson - 42 Heap Introduction 00:04:00 Kurt Anderson - 43 Heap Step by Step 00:12:00 Kurt Anderson - 44 Heap Real World Examples 00:07:00 Conclusion Kurt Anderson - 45 Thank You 00:01:00 Course Certification Order Your Certificates and Transcripts 00:00:00

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Benefits you'll get choosing Apex Learning for this Machine Learning: One payment, but lifetime access to 11 CPD courses Certificate, student ID for the title course included in a one-time fee Full tutor support available from Monday to Friday Free up your time - don't waste time and money travelling for classes Accessible, informative modules taught by expert instructors Learn at your ease - anytime, from anywhere Study the course from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills How will I get my Certificate? After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (Previously it was £10 * 11 = £110) Hard Copy Certificate: Free (For The Title Course) If you want to get hardcopy certificates for other courses, generally you have to pay £20 for each. But this Fall, Apex Learning is offering a Flat 50% discount on hard copy certificates, and you can get each for just £10! P.S. The delivery charge inside the U.K. is £3.99 and the international students have to pay £9.99. Curriculum of the Bundle Course 01: Machine Learning with Python Module 01: Introduction to Algorithms Module 02: Preprocessing Module 03: Regression Module 04: Classification Course 02: Data Science & Machine Learning with R Data Science and Machine Learning Course Intro Data Types and Structures in R Data Types and Structures in R Intermediate R Data Manipulation in R Data Visualization in R Creating Reports with R Markdown Building Webapps with R Shiny Introduction to Machine Learning Starting A Career in Data Science Course 03: Python Programming for Everybody Module 01 A Installing Python Documentation Command Line Variables Simple Python Syntax Keywords Import Module Module 02 Additional Topics If Elif Else Iterable For Loops Execute Exceptions Module 03 Data Types Number Types More Number Types Strings More Strings Files Lists Dictionaries Tuples Sets Module 04 Comprehensions Definitions Functions Default Arguments Doc Strings Variadic Functions Factorial Module 05 Function Objects Lambda Generators Closures Classes Object Initialization Class Static Members Classic Inheritance Data Hiding Course 04: Advanced Diploma in User Experience UI/UX Design UX/UI Course Introduction Introduction To The Web Industry Foundations of Graphic Design UX Design (User Experience Design) UI Design (User Interface Design) Optimization Starting a Career in UX/UI Design Course 05: Data Structures Complete Course Unit 01: Introduction Unit 02: Arrays Unit 03: Liked List Unit 04: Stack Unit 05: Queues Unit 06: Priority Queues (PQs) Unit 07: Union Find Unit 08: Binary Search Trees Unit 09: Fenwick Tree Unit 10: Hash Tables Unit 11: Suffix Array Unit 12: AVL Trees Unit 13: Indexed Priority Queue Unit 14: Sparse Tables Course 06: Data Science with Python Unit 01: Introduction to Python Data Science Unit 02: Data Cleaning Packages Unit 03: Data Visualization packages Course 07: Computer Science: Graph Theory Algorithms Module 00: Promo Module 01: Introduction Module 02: Common Problem Module 03: Depth First Search Module 04: Breadth First Search Module 05: Breadth First Search Shortest Path on a Grid Module 06: Trees Module 07: Topological Sort Module 08: Dijkstra Module 09: Bellman-Ford Algorithm Module 10: Floyd-Warshall Algorithm Module 11: Bridge and Algorithm Points Module 12: Tarjan Algorithm Module 13: Travelling Salesman Problem (TSP) Module 14: Eulerian Paths and Circuits Module 15: Prim's Minimum Spanning Tree Algorithm Module 16: Network Flow Course 08: Higher Order Functions in Python - Level 03 Module 01: Course Introduction Module 02: Simple Higher Order Functions Module 03: Sorting with Keys Module 04: Map Function Module 05: Filter Function Module 06: List Comprehension Alternative Module 07: Recursion Introduction Course 09: AWS Essentials Section 01: AWS Foundations and Services Section 02: AWS Security and Costs Course 10: Cloud Computing / CompTIA Cloud+ (CV0-002) Section 01: What You Need to Know Section 02: Introducing the Cloud Section 03: System Requirements for Cloud Deployments Section 04: Cloud Storage Section 05: Cloud Compute Section 06: Cloud Networking Section 07: Cloud Security Section 08: Migrating to the Cloud Section 09: Maintaining Cloud Solutions Section 10: Troubleshooting Cloud Solutions Course 11: Introduction to Data Analysis Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding CPD 125 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this Machine Learning bundle. Persons with similar professions can also refresh or strengthen their skills by enrolling in this course. Students can take this course to gather professional knowledge besides their study or for the future. Requirements Our Machine Learning is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various expertise will increase the value in your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included