The course highlights essential AI/ML tools and frameworks such as Python libraries (TensorFlow, Keras, Scikit-learn, NumPy) and explains why Python is preferred for AI development due to its simplicity, extensive libraries, and community support. It also emphasizes practical considerations like data preprocessing, feature extraction, hyperparameter tuning, and overfitting avoidance methods including cross-validation, dropout, and ensemble learning.
Finally, the course covers AI’s expanding impact across industries such as healthcare, finance, marketing, risk detection, and robotics, underscoring the increasing demand for AI/ML skills in the job market, along with advice on career development and educational pathways.
