Complete Guide to Machine Learning

The MachineLearning Speciality course is a comprehensive program designed to equip learners with a deep understanding of data science and machine learning concepts. It is structured in various modules, starting with an Introduction to DataScience & Machine Learning, covering the essentials such as analytics types, project lifecycle, and required skills. The course then delves into practical skills with Python for Data Analysis & PreProcessing, teaching the use of popular libraries and data handling techniques. Subsequent modules focus on Supervised Machine Learning for both regression and classification, where learners gain hands-on experience with models like linear regression, logistic regression, SVMs, decision trees, and more. The course emphasizes the importance of Feature Selection and Dimensionality Reduction, Cross-Validation & Hyperparameter Tuning, and introduces Deep Learning fundamentals. Additionally, learners explore Clustering techniques to uncover patterns in data. By the end of the course, participants will have mastered the key concepts and tools necessary for a career in machine learning, including Python programming, data preprocessing, model evaluation, and advanced algorithms. This course offers a blend of theoretical knowledge and practical application, ensuring learners are well-prepared for real-world data science challenges.

Learning Objectives

In the Machine Learning Speciality course, students will gain comprehensive knowledge and skills in data science and machine learning, from fundamentals to advanced techniques, including hands-on experience with real-world applications.

• Understand the necessity of data science and machine learning in solving complex problems and enhancing decision-making.
• Differentiate between descriptive, predictive, and prescriptive analytics and their applications.
• Master the data science project lifecycle, from conception to deployment.
• Acquire the essential skills required for a data scientist role, including statistical knowledge and programming expertise.
• Explore various types of machine learning such as supervised, unsupervised, and reinforcement learning.
• Gain proficiency in Python and its libraries for data analysis, visualization, and machine learning model building.
• Conduct exploratory data analysis (EDA) and apply various data preprocessing techniques, including handling missing values and categorical data.
• Develop and evaluate machine learning models using regression and classification techniques, understanding key concepts like overfitting and model selection.
• Implement feature selection, and dimensionality reduction, and understand their impact on model performance.
• Apply cross-validation and hyperparameter tuning to optimize model performance, and gain hands-on experience with these techniques.
• Dive into deep learning, construct neural networks using Keras and TensorFlow, and comprehend essential concepts like activation functions and optimization algorithms.

Course Prerequisites