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The 'Complete Python Machine Learning & Data Science Fundamentals' course covers the foundational concepts of machine learning, data science, and Python programming. It includes hands-on exercises, data visualization, algorithm evaluation techniques, feature selection, and performance improvement using ensembles and parameter tuning. LEARNING OUTCOMES: * Understand the fundamental concepts and types of machine learning, data science, and Python programming. * Learn to prepare the system and environment for data analysis and machine learning tasks. * Master the basics of Python, NumPy, Matplotlib, and Pandas for data manipulation and visualization. * Gain insights into dataset summary statistics, data visualization techniques, and data preprocessing. * Explore feature selection methods and evaluation metrics for classification and regression algorithms. * Compare and select the best machine learning model using pipelines and ensembles. * Learn to export, save, load machine learning models, and finalize the chosen models for real-time predictions. Why buy this Complete Python Machine Learning & Data Science Fundamentals? 1. Unlimited access to the course for forever 2. Digital Certificate, Transcript, student ID all included in the price 3. Absolutely no hidden fees 4. Directly receive CPD accredited qualifications after course completion 5. Receive one to one assistance on every weekday from professionals 6. Immediately receive the PDF certificate after passing 7. Receive the original copies of your certificate and transcript on the next working day 8. Easily learn the skills and knowledge from the comfort of your home CERTIFICATION After studying the course materials of the Complete Python Machine Learning & Data Science Fundamentals there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. WHO IS THIS COURSE FOR? This Complete Python Machine Learning & Data Science Fundamentals course is ideal for * Students * Recent graduates * Job Seekers * Anyone interested in this topic * People already working in the relevant fields and want to polish their knowledge and skill. PREREQUISITES This Complete Python Machine Learning & Data Science Fundamentals does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Complete Python Machine Learning & Data Science Fundamentals was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. CAREER PATH As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Complete Python Machine Learning & Data Science Fundamentals is a great way for you to gain multiple skills from the comfort of your home. COURSE CURRICULUM Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:08:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Understanding the CSV data file 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:07:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Python Machine Learning & Data Science Fundamentals 00:00:00

WHAT WILL I LEARN? * Choose a perfect automated trading strategy * Build a portfolio consisting of trading algorithms * Learn what criteria a trading algorithm should match * Learn when to exclude a strategy from portfolio * Learn advantages of using trading algorithms * Learn where to find automated trading strategies * Understand why it's better to trust an algorithm than a human * Learn what markets to trade using trading algorithms REQUIREMENTS * No programming skills required * Just an open mind * Basic Excel DESCRIPTION Updated in June'16! Learn how to make 95% of profit in just 14 months! Learn how to choose automated trading strategies and force them to earn money for you! In this course, I will show you several platforms where you can find a variety of trading robots. But, more importantly, I will give you a list of criteria to choose perfect strategies and teach you how to select them in practice. I will explain what to take into account when you have to choose between two similar strategies. You will also get a basic knowledge of trading algorithms, their advantages as compared to manual trading, which markets it's better to trade using automated trading strategies. I believe by the end of this course you will be able to build your own portfolio and make profits. Take this course now and learn from my 12+ years of experience. This course is for beginners as well as for advanced traders and algorithm developers! All you need is just your aspiration to learn! With this course you also get: * unlimited lifetime access at no extra costs * all future additional lectures, live trading examples * never any questions asked full 30-day money-back-in-full guarantee Do not hesitate to ask me any questions concerning this course or trading financial markets! Viktor WHO IS THE TARGET AUDIENCE? * Anyone interested in trading Financial Markets * Professional traders and algorithm developers * Anyone who wants to create a passive income on Financial Markets, but has no time to spare how to trade * Forex, stocks, options and especially futures traders * Those, who want to trade algorithms, but don't know where to start * And finally, anyone who has bad experience in trading Introduction What this course is about FREE 00:02:00 What do you need to start this course FREE 00:02:00 Live Results 00:03:00 Performance Update (November'15) 00:04:00 Performance Update (January'16) 00:05:00 Performance update (June'16) 00:06:00 Performance Update (October'16) 00:06:00 What instruments do you prefer to invest? What are Financial Markets? 00:06:00 What is algorithmic trading? 00:03:00 Risks from using trading algorithms 00:05:00 What markets to trade (Futures against Forex) 00:06:00 The external side of the robot What criteria to use to choose a strategy 00:09:00 How to create a portfolio 00:02:00 How to exclude a strategy from portfolio 00:03:00 Where to invest 00:05:00 iSystems in details 00:09:00 Learn from experience What strategies to use. Creating a portfolio (part 1) 00:08:00 What strategies to use. Creating a portfolio (part 2) 00:07:00

MEF CARRIER ETHERNET TRAINING COURSE DESCRIPTION The course progresses from a overview of the Carrier Ethernet service and how it works onto looking at the concepts in depth. Service attributes and management follow with the course finishing with studies of practical Carrier Ethernet. WHAT WILL YOU LEARN * Discuss and understand key Carrier Ethernet Concepts. * Understand tasks related to designing, deploying and maintaining a Carrier Ethernet network. * Offer effective solutions to implementing a Carrier Ethernet enterprise network given available customer resources and requirements. * Carry out informed discussions using industry Carrier Ethernet 'vocabulary. * Pass the MEF CECP 2.0 professional accreditation exam. MEF CARRIER ETHERNET TRAINING COURSE DETAILS * Who will benefit: Anyone working with Carrier Ethernet * Prerequisites: The course attendees need to be conversant with data networks, as well as Ethernet and IP technologies. * Duration 5 days MEF CARRIER ETHERNET TRAINING COURSE CONTENTS * Section One: Introduction to Carrier Ethernet * Introduction to Carrier Ethernet: What is Carrier Ethernet? Evolution, advantages, The MEF, MEF specifications; UNI, EVC, OVC, EPL/EVPL, EP-LAN/ EVP-LAN, EP-Tree/EVP-Tree, etc, overview. * How Carrier Ethernet Works: Service Frame Handling. Carrier Ethernet at Customer Premises, metro and core. Carrier Ethernet Workings, UNI attributes, Service Attributes (EVC and EVC per UNI attributes), Bandwidth Profiles, service multiplexing, L2 protocol processing; Carrier Ethernet equipment, CPE, aggregation and homing nodes, core equipment; management systems. * The Setting Up of a Carrier Ethernet Service: Step 1: Choose service type, EPL/EVPL, EP-LAN/EVP-LAN, EPTree/EVP-Tree, EVLine...; Step 2: CPE tasks, UNI-C tasks (UNI attributes, service attributes (EVC and EVC per UNI) and bandwidth profiles), UNI-N tasks (L2 protocol handling). Step 3: Non-CPE tasks, Access, metro and core connections set up. * Section Two: Carrier Ethernet Concepts in depth * Carrier Ethernet Definitions in Depth: UNI, UNI I & II, UNI-N and UNI-C, etc.; NNI/ENNI; EVC; OVC, OVC type (P2P, M2M, Rooted MP), OVC end point (root, leaf, trunk), OVC end point map, OVC end point bundling; Service types in detail, EPL/EVPL, EP-LAN/EVP-LAN, EP-Tree/EVP-Tree, EVLine, Access EPL, Access EVPL . * Carrier Ethernet Service Frame Handling: Unicast, multicast and broadcast frame delivery, Tagged, untagged and priority; Tagging, C and S-Tags, 802.3, 802.1d, 802.1q, 802.1ad, 802.1ah evolution, VLAN ID translation/preservation. CoS preservation. * Other Key Carrier Ethernet Concepts: MTU, MTU at UNI, MTU at ENNI; Physical Layer Attributes, FE, GbE and 10GbE, Service Multiplexing and Bundling Concept and detail, rules and implications; Hairpin Switching * Managing Bandwidth in a Carrier Ethernet Network: Token Bucket Algorithm, EIR, CIR, CBS, EBS, Coupling Flag; Frame Colors, recoloring, Color Awareness attribute, Color Forwarding; Bandwidth Profiles, rules and concepts. MEF CoS identifiers, DEI bit (in S-Tag), PCP bit (in C-Tag or S-Tag), or DSCP (in IP header), Multiflow bandwidth concepts; CoS Label/Color Identification. * Section Three: Carrier Ethernet Service Attributes Overview: Carrier Ethernet 2.0; Blueprint C Service Attributes: Per UNI, Physical interfaces, Frame format, Ingress/egress Bandwidth Profiles, CEVLAN ID/EVC Map, UNI protection. EVC per UNI, Ingress/egress Bandwidth Profiles, etc.; Per EVC, CEVLAN ID Preservation, CoS ID Preservation, Relationship between SLA and SLP, Class of Service, etc. OVC, ENNI, OVC End Point per UNI and OVC End Point per ENNI, Ingress/egress bandwidth profiles, etc. * Section Four: Managing Carrier Ethernet Networks Overview: MEF Service Lifecycle. Carrier Ethernet maintenance: Port, Link & NE failure, Service Protection Technologies, Fault Identification and Recovery, LAG, Active/Standby EVC, Single EVC with transport protection, G.8031, G.8032, MPLS FRR. * SOAMs: Connectivity fault management, connectivity Monitoring, Loopback, Linktrace; Performance Management, Frame Delay, Inter Frame Delay Variation, Availability, Frame Loss Ratio, Resiliency, HLI, DMM, DMR, SLM, SLR; Key Concepts, Single vs dual ended, ordered UNI pair calculations. * LOAMs: Link discovery, link monitoring, etc. * Terminology and Concepts: MEG levels, MIPs. * Section Five: Practical Carrier Ethernet Carrier Ethernet Transport Technologies: Layer 1: SDH. Layer 2: Bridging, provider bridging, PBB, PBBTE. Layer 2.5: MPLS VPWS, MPLS VPLS, MPLS-TP. * Carrier Ethernet Access Technologies: fiber, SDH, active fiber, PON, GPON, 10G PON, OTN, WDM; copper, PDH, G-SDSL, 10Pass-TS, HFC; packet radio. * Optimising mobile backhaul with Carrier Ethernet Key challenges solutions: Market pressure, LTE evolution, elements and architecture (RAN BS, NC, GWIF.), synchronization, bandwidth management. * Circuit Emulation over Ethernet: Purpose, needs and applications. Synchronization: Phased, ToD, External Reference source, SynchE ,NTP, IEEE-1588 v2/ PTP, ACR; MEF Service Definitions for emulated circuits. * Applying what you know: Practical examples and scenarios, Carrier Ethernet solutions; Practice Scenarios, Given a scenario, determine appropriate Ethernet services

LOOKING FOR: ADULT FICTION, ADULT NON-FICTION Laetitia Rutherford is a literary agent at Watson, Little, where she recently passed her ten year anniversary. She began as an agent at Toby Eady Associates, following three years as a Marketing Executive for HarperCollins Fiction. Watson, Little is a longstanding agency representing writers across all tastes and genres and with a strong reputation for extending book rights into Translation and into Film and TV. Laetitia is looking for upmarket and literary Fiction, and for creative Non-Fiction. She welcomes writers from all walks of life and is keen to hear from a variety of voices and life experiences. Laetitia is looking for novels with a strong concept, question or emotional scenario, fresh perspectives, well worked out narratives, and arresting style. She is more often looking for contemporary settings, and loves to be immersed in a distinctive place, but also likes historical novels, as long as they speak lucidly for today. Contemporary issues like housing, money, and the experience of different workplaces appeal, as does unconventional family life and relationships. Laetitia is also open to novels with surreal or fantastical aspects, that throw universal storylines into the challenges of today (and tomorrow)’s complex world. It is always exciting to see a story so relatable, or as yet unheard, or resonant that it speaks to different countries in different languages. Laetitia is experienced in unique literary work, seen in her author Hannah Silva’s My Child, the Algorithm, a Granta Best Books of 2023; gripping and intelligent upmarket fiction (see Anika Scott); as well as highly popular Crime fiction like Ajay Chowdhury’s Detective Kamil Rahman series and Jenny Blackhurst’s locked room refresh Three Card Murder, both in TV development. She loves plot and the puzzle of a really plotty edit, as much as dazzling writing, whether spare or lush. Her current focus is on taking forward Watson, Little’s Literary talent, and less on mainstream Crime. But Laetitia would be excited to see a novel that plays with crime elements – and is looking forward to reading, for example, Jennifer Croft’s The Extinction of Irena Rey.   Among the most compelling novels recently read are Fates and Furies by Lauren Groff, No One Is Talking About This by Patricia Lockwood, Girl, Woman, Other by Bernardine Evaristo, The Vanishing Half by Brit Bennett, An American Marriage by Tayeri Jones, Lullaby by Leila Slimani, Lanny by Max Porter, and the William novels by Elizabeth Strout. In Non-Fiction, recent favourites include In the Dream House by Carmen Maria Machado, Maggie Nelson’s The Argonauts, Amy Liptrot’s The Outrun, Jennifer Croft’s Homesick, and Noreen Masud’s A Flat Place. Laetitia would like you to submit a covering letter, 1 page synopsis and the first three chapters or 5,000 words of your manuscript in a single word document. (In addition to the paid sessions, Laetitia is kindly offering one free session for low income/under-represented writers. Please email agent121@iaminprint.co.uk [agent121@iaminprint.co.uk] to apply, outlining your case for this option which is offered at the discretion of I Am In Print).  By booking you understand you need to conduct an internet connection test with I Am In Print prior to the event. You also agree to email your material in one document to reach I Am In Print by the stated submission deadline and note that I Am In Print take no responsibility for the advice received during your agent meeting. The submission deadline is: Monday 15 July 2024

OVERVIEW This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. HOW WILL I GET MY CERTIFICATE? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. WHO IS THIS COURSE FOR? There is no experience or previous qualifications required for enrolment on this Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. REQUIREMENTS Our Data Science & Machine Learning with Python 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- * Open doors of opportunities * Increase your adaptability * Keep you relevant * Boost confidence And much more! COURSE CURRICULUM 2 sections • 90 lectures • 10:24:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:08:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:07:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

OVERVIEW Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! HOW WILL I GET MY CERTIFICATE? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. WHO IS THIS COURSE FOR? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. REQUIREMENTS The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. CAREER PATH This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as * Data Analyst * Data Scientist * Data Manager * Business Analyst And much more! COURSE CURRICULUM 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

This course is intended for beginner-level individuals who are fascinated about quantum computing and want to learn more about it. It uses Jupyter notebook and IBM Qiskit tool to execute your learning into the actual computation.

With this course, you will learn the bare-bone basics of C# by building console applications from scratch. You will first develop the application and then test it to gain a solid understanding of C# fundamentals. You will also explore the latest features released in C# 7.

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.

Boost Your Career with Apex Learning and Get Noticed By Recruiters in this Hiring Season! Save Up To £4,169 and get Hard Copy + PDF Certificates + Transcript + Student ID Card worth £160 as a Gift - Enrol Now Give a compliment to your career and take it to the next level. This Machine Learning will provide you with the essential knowledge and skills required to shine in your professional career. Whether you want to develop skills for your next job or want to elevate skills for your next promotion, this Machine Learning will help you keep ahead of the pack. The Machine Learning incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can reinforce your professional skills and essential knowledge, reaching out to the level of expertise required for your position. Further, this Machine Learning will add extra value to your resume to stand out to potential employers. Throughout the programme, it stresses how to improve your competency as a person in your profession while at the same time it outlines essential career insights in this job sector. Consequently, you'll strengthen your knowledge and skills; on the other hand, see a clearer picture of your career growth in future. By the end of the Machine Learning, you can equip yourself with the essentials to keep you afloat into the competition. Along with this Machine Learning course, you will get 10 other premium courses. Also, you will get an original Hardcopy and PDF certificate for the title course and a student ID card absolutely free. This Bundle Consists of the following Premium courses: * Course 01: Machine Learning with Python * Course 02: Advanced Diploma in User Experience UI/UX Design * Course 03: Data Science & Machine Learning with R * Course 04: Python Programming for Everybody * Course 05: Data Structures Complete Course * Course 06: Data Science with Python * Course 07: Computer Science: Graph Theory Algorithms * Course 08: Higher Order Functions in Python - Level 03 * Course 09: AWS Essentials * Course 10: Cloud Computing / CompTIA Cloud+ (CV0-002) * Course 11: Introduction to Data Analysis So, enrol now to advance your career! 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