Artificial Intelligence and Machine Learning Mastery
Specialization | 61 Course Series | 32 Mock Tests
This Machine Learning Certification includes 61 courses with 323 hours of video tutorials and One year access. Learn concepts such as Machine Learning in MS EXCEL, Machine learning using PYTHON, Deep learning, Data Science with R, Face Detection in Python, Bayesian Machine Learning, Projects on Machine learning and much more right from the basics to advanced concepts.
Offer ends in:
What you'll get
- 323 Hours
- 61 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Courses: You get access to all 61 courses, in the Projects bundle. You do not need to purchase each course separately.
- Hours: 323 Video Hours
- Core Coverage: Machine learning using Python, Deep Learning, Data Science with R, Face Detection in Python, Bayesian Machine Learning, Business Intelligence, Artificial Intelligence, and Projects on Machine Learning.
- Course Validity: One year access
- Eligibility: Anyone who is serious about learning Machine Learning and wants to make a career in this Field
- Pre-Requisites: Familiarity with at least one programming language is recommended
- What do you get? Certificate of Completion for each of the 61 courses, Projects
- Certification Type: Course Completion Certificates
- Verifiable Certificates? Yes, you get verifiable certificates for each course with a unique link. These links can be included in your Resume/Linkedin profile to showcase your enhanced Machine Learning Skills
- Type of Training: Video Course – Self-Paced Learning
Content
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MODULE 1: ML Essentials Training
Courses No. of Hours Certificates Details Overview of Machine Learning Certification 1m ✔ Artificial Intelligence with Python - Beginner Level 2h 51m ✔ Artificial Intelligence with Python - Intermediate Level 4h 34m ✔ ChatGPT Complete MasterClass - 2024 4h 57m ✔ AI Machine Learning in Python 8h 37m ✔ Microsoft Excel - Beginners 6h 44m ✔ Supervised Machine Learning with R 2024 - Linear Regression 3h 05m ✔ Machine Learning with Python 2024 5h 17m ✔ Test - Machine Learning with Python Minor Test 1 Test - Machine Learning with Python Minor Test 2 Test - Excel Mock Exam Test - Machine Learning Assessment -
MODULE 2: Machine Learning with Python
Courses No. of Hours Certificates Details Machine Learning using Python 3h 26m ✔ Machine Learning with Python Case Study - Covid19 Mask Detector 2h 05m ✔ Deep Learning: Automatic Image Captioning for Social Media with Tensorflow 2h 23m ✔ Develop a Movie Recommendation Engine 51m ✔ Machine Learning Python Case Study - Diabetes Prediction 1h 02m ✔ Predictive Analytics and Modeling with Python 8h 26m ✔ Test - Machine Learning with Python Major Test Test - ML Assessment Exam Test - Mock Exam Machine Learning -
MODULE 3: Machine Learning with R
Courses No. of Hours Certificates Details Machine Learning with R 20h 25m ✔ Time Series Analysis and Forecasting using R 4h 34m ✔ Project - Fraud Analytics using R 2h 34m ✔ Project - Marketing Analytics using R and Microsoft Excel 2h 9m ✔ Case Study - Customer Analytics using Tableau and R 2h 7m ✔ Case Study - Pricing Analytics using Tableau and R 2h 39m ✔ Cluster Analysis and Unsupervised Machine Learning - K-Means Clustering using R 43m ✔ Machine Learning Project using Caret in R 1h 58m ✔ Test - Complete Machine Learning Exam Test - Machine Learning Ultimate Exam Test - R Programming Basic Test Test - Test Series R Programming Test - 2023 R Programming Exam Test - R Programming Complete Exam -
MODULE 4: Machine Learning with MS Excel
Courses No. of Hours Certificates Details Statistical Tools in Microsoft Excel 1h 11m ✔ Microsoft Excel - Advanced 9h 21m ✔ Microsoft Excel Charts and SmartArt Graphics 6h 4m ✔ Power Excel Training 5h 15m ✔ MS Excel Shortcuts 8h 22m ✔ Mastering Microsoft Excel Date and Time 2h 47m ✔ Date and Time Functions Microsoft Excel Training 2h 37m ✔ Shortcuts in Microsoft Excel 24m ✔ Graphs & Charts in Microsoft Excel 2013 2h 6m ✔ Financial Functions In MS Excel 2h 36m ✔ Microsoft Excel Solver Tutorial 48m ✔ Microsoft Excel for Financial Analysis 49m ✔ Microsoft Excel for Data Analyst 2h 35m ✔ Business Intelligence using Microsoft Excel 5h 06m ✔ MS Excel Simulations Training 2h 15m ✔ Microsoft Power BI - Business Intelligence for Beginners to Advance 10h 34m ✔ Power BI: Software for Data Visualization 3h 3m ✔ Test - Excel Mock Exam Test - Excel Assessment Exam Test - Complete Excel Exam Test - Ultimate Excel Test -
MODULE 5: Machine Learning from Projects & Practicals
Courses No. of Hours Certificates Details Project on ML - Shipping and Time Estimation 2h 29m ✔ Project on ML - Supply Chain Demand Trends Analysis 1h 09m ✔ Predicting Prices using Regression Techniques 2h 18m ✔ Project on ML - Fraud Detection in Credit Payments 1h 51m ✔ Project on ML - Banking and Credit Frauds 44m ✔ Project on ML - Churn Prediction Model using R Studio 1h 22m ✔ Random Forest Algorithm using Python 1h 27m ✔ Predictive Analytics and Modeling with Python 8h 26m ✔ Projects and Case Studies on Machine Learning with Python 4h 57m ✔ Machine Learning Python Case Study - Diabetes Prediction 1h 02m ✔ Project - Exploratory Data Analysis EDA using ggplot2, R and Linear Regression 2h 07m ✔ Project on R - HR Attrition and Analytics 2h 4m ✔ Predicting Credit Default using Logistic Regression in Python 3h 3m ✔ House Price Prediction using Linear Regression in Python 3h 2m ✔ Poisson Regression with SAS Stat 2h 21m ✔ Test - Complete Machine Learning Exam Test - Machine Learning Ultimate Exam -
MODULE 6: Machine Learning Hands-On
Courses No. of Hours Certificates Details Machine Learning ZERO to HERO - Hands-on with Tensorflow 13h 03m ✔ Deep Learning ZERO to HERO - Hands-on with Python 11h 17m ✔ Machine Learning with MATLAB 2h 15m ✔ Projects and Case Studies on Machine Learning with Python 4h 57m ✔ Bayesian Statistics & Supervised Machine Learning: A/B Testing 57m ✔ Octave Machine Learning Training - Beginners to Beyond 3h 35m ✔ Artificial Intelligence and Machine Learning Training Course 12h 13m ✔ Test - Machine Learning Assessment Test - ML Assessment Exam Test - Mock Exam Machine Learning -
MODULE 7: Mock Tests & Quizzes
Courses No. of Hours Certificates Details Test - 2023 Excel Exam Test - Assessment Exam 2022 Test - 2022 Excel Mock Exam Test - Excel 2023 Assessment Exam Test - Complete Excel Test 2023 Test - 2023 - Excel Mock Exam Test - Test Series R Programming Test - 2023 R Programming Exam Test - R Programming Complete Exam Test - R Programming Practice Test
Description
The course is designed to provide comprehensive training in machine learning (ML) techniques, catering to both beginners and experienced individuals looking to enhance their skills in this rapidly growing field. Through a series of modules, participants will embark on a journey that covers fundamental concepts, practical implementations, and advanced applications of ML algorithms. Whether you're a novice seeking to understand the basics or a seasoned professional aiming to deepen your expertise, this course offers a structured curriculum that caters to diverse learning needs. With hands-on projects, real-world case studies, and interactive assessments, participants will gain practical experience and proficiency in ML, empowering them to tackle complex challenges and drive innovation in various domains.
MODULE 1: ML Essentials Training This module serves as a foundational course in machine learning (ML), providing a thorough understanding of core concepts and methodologies. Students begin by learning about the fundamental principles of ML, including supervised and unsupervised learning techniques. They delve into various algorithms such as linear regression, decision trees, and k-nearest neighbors, understanding their applications and limitations. Additionally, the module covers essential topics like feature engineering, model evaluation, and cross-validation techniques. By the end of this module, students gain a solid grounding in ML essentials, laying the groundwork for more advanced topics.
MODULE 2: Machine Learning with Python In this module, students dive into the practical implementation of machine learning algorithms using the Python programming language. They start by exploring Python's powerful libraries such as NumPy, Pandas, and Scikit-learn, which provide robust support for data manipulation, preprocessing, and modeling. Through hands-on exercises and projects, students learn to apply various machine learning algorithms for tasks like classification, regression, and clustering. They also gain exposure to advanced topics like deep learning using TensorFlow and Keras. By the end of this module, students develop proficiency in building and deploying machine learning models using Python.
MODULE 3: Machine Learning with R This module focuses on leveraging the R programming language for machine learning tasks. Students learn to preprocess data, perform exploratory data analysis (EDA), and build predictive models using R's extensive ecosystem of packages. They gain hands-on experience with popular libraries such as caret and tidymodels, which offer robust support for model training, evaluation, and visualization. Additionally, students explore advanced topics like ensemble learning, hyperparameter tuning, and model interpretation techniques. By the end of this module, students become proficient in using R for end-to-end machine learning projects.
MODULE 4: Machine Learning with MS Excel In this module, students learn to harness the power of Microsoft Excel for machine learning applications. They explore Excel's built-in functions and tools for data analysis, regression, and classification tasks. Through practical examples and exercises, students learn to preprocess data, build predictive models, and generate insights using Excel's user-friendly interface. They also discover how to create interactive dashboards and visualizations to communicate their findings effectively. By the end of this module, students gain a unique perspective on machine learning and data analysis using Excel as a tool.
MODULE 5: Machine Learning from Projects & Practicals This module focuses on applying machine learning techniques to real-world projects and case studies. Students work on hands-on assignments that simulate industry scenarios, such as customer segmentation, sentiment analysis, and recommendation systems. They apply their knowledge gained from previous modules to solve these practical challenges, gaining valuable experience in model development, evaluation, and deployment. Additionally, students learn best practices for project management, collaboration, and presentation skills, preparing them for real-world machine learning projects in diverse domains.
MODULE 6: Machine Learning Hands-On This module provides an immersive, hands-on experience in machine learning, allowing students to deepen their practical skills and expertise. Through a series of guided projects and coding exercises, students tackle complex machine learning problems from start to finish. They explore advanced topics such as natural language processing (NLP), computer vision, and reinforcement learning, gaining proficiency in cutting-edge ML techniques. Additionally, students have the opportunity to work on industry-relevant projects, collaborate with peers, and receive personalized feedback from instructors to enhance their learning experience.
MODULE 7: Mock Tests & Quizzes In this module, students have the opportunity to assess their understanding and reinforce their learning through mock tests and quizzes. These assessments cover the material from each module, helping students gauge their progress and identify areas for improvement. By simulating real-world exam conditions, students can build confidence and test their knowledge across a range of machine learning topics. Moreover, the feedback provided on their performance allows students to focus their efforts on areas that require further study, ensuring a well-rounded understanding of machine learning concepts and techniques.
Sample Certificate
Requirements
- Basic Knowledge of Programming: Familiarity with programming languages such as Python, R, or MATLAB will be advantageous as many examples and exercises will involve coding in these languages.
- Understanding of Mathematics and Statistics: A foundational understanding of mathematical concepts like algebra, calculus, and statistics will aid in comprehending the underlying principles of machine learning algorithms.
- Familiarity with Data Analysis: Prior experience with data analysis tools and techniques will be helpful, as machine learning often involves working with large datasets, preprocessing, and analyzing data to derive insights.
- While not mandatory, having these prerequisites will enhance the learning experience and ensure participants can grasp the concepts more effectively.
- Knowledge of Machine Learning Concepts: While not mandatory, having a basic understanding of machine learning concepts such as supervised learning, unsupervised learning, and model evaluation metrics can help in grasping the course content more quickly.
- Experience with Data Visualization Tools: Familiarity with data visualization tools like Matplotlib, Seaborn, or ggplot2 can aid in visualizing the results of machine learning models and communicating insights effectively.
- Access to Machine Learning Libraries: Having access to machine learning libraries such as scikit-learn for Python or caret for R will enable participants to implement machine learning algorithms and practice coding exercises more efficiently.
- Problem-Solving Skills: Strong problem-solving skills and the ability to think critically are essential for understanding and applying machine learning algorithms to real-world problems.
Target Audience
- Beginners: Individuals who are new to the field of machine learning and wish to build a strong foundation in both theory and practical implementation.
- Data Enthusiasts: Professionals working with data who want to expand their skill set and learn how to apply machine learning techniques to analyze and derive insights from data.
- Students: Undergraduate or graduate students studying computer science, data science, statistics, or related fields who want to supplement their academic learning with practical machine learning experience.
- Professionals Seeking Career Advancement: Individuals looking to transition into roles such as data analyst, data scientist, machine learning engineer, or artificial intelligence specialist, where knowledge of machine learning is highly valued.
- Business Owners and Managers: Entrepreneurs or business leaders who want to understand how machine learning can be leveraged to improve decision-making, optimize processes, and gain a competitive edge in their industry.
- Anyone Interested in AI: Individuals with a general interest in artificial intelligence and its applications across various domains, seeking to gain hands-on experience with machine learning algorithms and techniques.
Course Ratings
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Great course for someone like me who's been using Excel everyday but didn't know about data analysis tool available in it. I've always have to open JMP for analysis even just for simple charting and correlation plotting. Now I know I can do that in Excel too. Thanks! 🙂
Michelle Esber
This course was great to attend and learn about Excel's "Data Analytics" add-in function!
Sumari Hattingh Van Niekerk
Overall, I found the course to be incredibly informative and beneficial to my understanding of Excel. One aspect of the course that stood out to me was the clarity of instruction. The instructor did an excellent job of breaking down complex concepts into easy-to-understand explanations. This made learning Excel much more manageable, especially for someone like me who was relatively new to the software. I also appreciated the practical approach taken throughout the course. The hands-on exercises and real-world examples helped me grasp the material more effectively. By applying what I learned in a practical context, I felt more confident in my abilities to use Excel in various scenarios. Additionally, I found the pacing of the course to be just right. It covered a wide range of topics without feeling rushed, allowing me to absorb the information at a comfortable pace. The supplemental materials provided were also a valuable resource, offering additional support and reinforcement
Akshit Raj Choudhary
This tutorial was really helpful in understanding forecasting using R. The explanation was really easy to understand and the examples were really useful. The coverage of topics was good starting with the basics then going deep into the topics. they have covered simple forecasting methods, transformations, and adjustments, time series regressions and arima models
SHUSHANTH T