Artificial Intelligence and Machine Learning Mastery
Specialization | 61 Course Series | 32 Mock Tests

This comprehensive Machine Learning (ML) course helps beginners and experienced professionals enhance their skills in this fast-growing field. The program provides a structured curriculum covering basic concepts, hands-on practice, and advanced uses of ML algorithms. Participants will work on real-world projects, case studies, and interactive exercises to practice developing, evaluating, and deploying machine learning models across different domains.

The course includes the following modules:

Module 1: ML Essentials Training

Learn the basics of machine learning, including types of learning, linear regression, decision trees, k-nearest neighbors, feature engineering, and how to evaluate models.

Module 2: Machine Learning with Python

Learn to implement ML algorithms using Python libraries such as NumPy, Pandas, and Scikit-learn. Work on classification, regression, clustering, and advanced topics like deep learning with TensorFlow and Keras.

Module 3: Machine Learning with R

Leverage R for ML projects, covering data preprocessing, exploratory data analysis (EDA), predictive modeling, ensemble learning, hyperparameter tuning, and model interpretation using libraries like caret and tidymodels.

Module 4: Machine Learning with MS Excel

Apply machine learning techniques using Excel's built-in functions and tools. Preprocess data, perform regression and classification, and create interactive dashboards and visualizations to deliver effective insights.

Module 5: Machine Learning from Projects & Practicals

Work on industry-relevant projects, including customer segmentation, sentiment analysis, and recommendation systems. Gain experience in model development, evaluation, and deployment while learning project management and collaboration skills.

Module 6: Machine Learning Hands-On

Deepen practical ML skills with guided coding exercises and projects. Learn advanced topics like natural language processing (NLP), computer vision, and reinforcement learning. Receive personalized feedback to refine your expertise.

Module 7: Mock Tests & Quizzes

Assess your understanding of machine learning concepts through quizzes and mock tests. Identify areas for improvement and reinforce learning in a structured, exam-like environment.

By the end of this course, participants will understand machine learning techniques, gain hands-on experience with various programming tools, and be confident applying ML solutions in real-world situations.

• Basic understanding of programming concepts (Python or R preferred)

• Familiarity with mathematics and statistics (linear algebra, probability, and basic calculus)

• Basic knowledge of Excel for data analysis

• Curiosity and willingness to work on hands-on projects and coding exercises.

• Beginners looking to start a career in Machine Learning or Data Science

• Students pursuing degrees in Computer Science, Data Science, Statistics, or related fields

• Professionals aiming to upskill in ML for career growth

• Analysts, developers, or engineers interested in applying ML techniques to real-world projects

• Anyone keen on understanding and implementing machine learning models across different tools (Python, R, Excel).