32 PCA courses

Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R

This is an introductory course on machine learning. The course covers a wide range of topics, from handling a dataset to model delivery. Some prior training in Python programming and basic calculus knowledge will help you get the best out of this course.

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Experienced tutors and mentors will be there for you whenever you need them, and solve all your queries through email and chat boxes. Benefits you'll get choosing Apex Learning for this Course: * One payment, but lifetime access to 11 CPD courses * Certificates, 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 * Gain valuable knowledge without leaving your home What other courses are included with this Course? 1. Level 2 Microsoft Office Essentials 2. Microsoft Teams 3. Leadership & Management Diploma 4. Working from Home Essentials 5. Mental Health and Working from Home 6. Online Meeting Management 7. Effective Communication Skills 8. Time Management 9. Report Writing 10. Emotional Intelligence and Human Behaviour Curriculum ***Data Science Course Bundle 2022*** Welcome, Course Introduction & overview, and Environment set-up * Welcome & Course Overview * Set-up the Environment for the Course (lecture 1) * Set-up the Environment for the Course (lecture 2) * Two other options to setup environment Python Essentials * Python data types Part 1 * Python Data Types Part 2 * Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) * Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) * Python Essentials Exercises Overview * Python Essentials Exercises Solutions Python for Data Analysis using NumPy * What is Numpy? A brief introduction and installation instructions. * NumPy arrays, built-in methods, array methods and attributes. * Indexing, slicing, broadcasting & boolean masking * Arithmetic Operations & Universal Functions * Exercises Overview * Exercises Solutions Python for Data Analysis using Pandas * What is pandas? A brief introduction and installation instructions. * Pandas Introduction * Pandas Data Structures - Series * Pandas Data Structures - DataFrame * Handling Missing Data * Data Wrangling - Combining, merging, joining * Groupby * Useful Methods and Operations * Project 1 (Overview) Customer Purchases Data * Project 1 (Solutions) Customer Purchases Data * Project 2 (Overview) Chicago Payroll Data * Project 2 (Solutions Part 1) Chicago Payroll Data Python for Data Visualization using matplotlib * Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach * Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach * Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach * Matplotlib Essentials - Exercises Overview * Matplotlib Essentials - Exercises Solutions Python for Data Visualization using Seaborn * Introduction & Installation * Distribution Plots * Categorical Plots (Part 1) * Categorical Plots (Part 2) * Axis Grids * Matrix Plots * Regression Plots * Controlling Figure Aesthetics * Exercises Overview * Exercise Solutions Python for Data Visualization using pandas * Pandas Built-in Data Visualization * Pandas Data Visualization Exercises Overview * Panda Data Visualization Exercises Solutions Python for interactive & geographical plotting using Plotly and Cufflinks * Interactive & Geographical Plotting (Part 1) * Interactive & Geographical Plotting (Part 2) * Interactive & Geographical Plotting Exercises (Overview) * Interactive & Geographical Plotting Exercises (Solutions) Capstone Project - Python for Data Analysis & Visualization * Project 1 - Oil vs Banks Stock Price during recession (Overview) * Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) * Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) * Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) * Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) Python for Machine Learning (ML) - scikit-learn - Linear Regression Model * Introduction to ML - What, Why and Types….. * Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff * Linear Regression Model - Hands-on (Part 1) * Linear Regression Model Hands-on (Part 2) * Good to know! How to save and load your trained Machine Learning Model! * Linear Regression Model (Insurance Data Project Overview) * Linear Regression Model (Insurance Data Project Solutions) Python for Machine Learning - scikit-learn - Logistic Regression Model * Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. * Logistic Regression Model - Hands-on (Part 1) * Logistic Regression Model - Hands-on (Part 2) * Logistic Regression Model - Hands-on (Part 3) * Logistic Regression Model - Hands-on (Project Overview) * Logistic Regression Model - Hands-on (Project Solutions) Python for Machine Learning - scikit-learn - K Nearest Neighbors * Theory: K Nearest Neighbors, Curse of dimensionality …. * K Nearest Neighbors - Hands-on * K Nearest Neighbors (Project Overview) * K Nearest Neighbors (Project Solutions) Python for Machine Learning - scikit-learn - Decision Tree and Random Forests * Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. * Decision Tree and Random Forests - Hands-on (Part 1) * Decision Tree and Random Forests (Project Overview) * Decision Tree and Random Forests (Project Solutions) Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) * Support Vector Machines (SVMs) - (Theory Lecture) * Support Vector Machines - Hands-on (SVMs) * Support Vector Machines (Project 1 Overview) * Support Vector Machines (Project 1 Solutions) * Support Vector Machines (Optional Project 2 - Overview) Python for Machine Learning - scikit-learn - K Means Clustering * Theory: K Means Clustering, Elbow method ….. * K Means Clustering - Hands-on * K Means Clustering (Project Overview) * K Means Clustering (Project Solutions) Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) * Theory: Principal Component Analysis (PCA) * Principal Component Analysis (PCA) - Hands-on * Principal Component Analysis (PCA) - (Project Overview) * Principal Component Analysis (PCA) - (Project Solutions) Recommender Systems with Python - (Additional Topic) * Theory: Recommender Systems their Types and Importance * Python for Recommender Systems - Hands-on (Part 1) * Python for Recommender Systems - - Hands-on (Part 2) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) * Natural Language Processing (NLP) - (Theory Lecture) * NLP-Challenges, Data Sources, Data Processing ….. * Feature Engineering and Text Preprocessing in Natural Language Processing * NLP - Tokenization, Text Normalization, Vectorization, BoW…. * BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … * Pipeline feature to assemble several steps for cross-validation… 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. CPD 20 CPD hours / points Accredited by CPD Quality Standards WHO IS THIS COURSE FOR? There is no experience or previous qualifications required for enrolment on this Data Science Course Bundle 2022. It is available to all students, of all academic backgrounds. REQUIREMENTS Our Data Science Course Bundle 2022 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible on tablets and smartphones so you can access your course on wifi, 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 this CPD certificate will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. CERTIFICATES CERTIFICATE OF COMPLETION Digital certificate - Included

OVERVIEW This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis comes with accredited certification, 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 Python for Data Analysis. It is available to all students, of all academic backgrounds. REQUIREMENTS Our Python for Data Analysis 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 qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. COURSE CURRICULUM 19 sections • 99 lectures • 00:08:00 total length •Welcome & Course Overview: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:37:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method ..: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00 •Resources- Python for Data Analysis: 00:00:00

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.

OVERVIEW In this age of technology, data science and machine learning skills have become highly demanding skill sets. In the UK a skilled data scientist can earn around £62,000 per year. If you are aspiring for a career in the IT industry, secure these skills before you start your journey. The Complete Machine Learning & Data Science Bootcamp 2023 course can help you out. This course will introduce you to the essentials of Python. From the highly informative modules, you will learn about NumPy, Pandas and matplotlib. The course will help you grasp the skills required for using python for data analysis and visualisation. After that, you will receive step-by-step guidance on Python for machine learning. The course will then focus on the concepts of Natural Language Processing.  Upon successful completion of the course, you will receive a certificate of achievement. This certificate will help you elevate your resume. So enrol 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? Anyone with an interest in learning about data science can enrol in this course. It will help aspiring professionals develop the basic skills to build a promising career. Professionals already working in this can take the course to improve their skill sets. 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 COURSE CURRICULUM 18 sections • 98 lectures • 23:48:00 total length •Welcome & Course Overview6: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:17:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method.: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Get involved in a learning adventure, mastering R from foundational basics to advanced techniques. This course is a gateway to the realm of data science. Explore statistical machine learning models and intricacies of deep learning and create interactive Shiny apps. Unleash the power of R and elevate your proficiency in data-driven decision-making.