About This Course
Dive into the comprehensive world of Data Science with our meticulously designed course. This program equips you with the essential knowledge and skills to analyze, interpret, and visualize data, as well as implement machine learning algorithms and artificial intelligence techniques. From fundamental statistical methods to advanced machine learning algorithms and reinforcement learning, this course covers a broad spectrum of topics. With practical projects in each area, you’ll gain hands-on experience applying theoretical concepts to real-world scenarios.
Learning Objectives
Master measures of central tendency, dispersion, skewness, and kurtosis to effectively describe and summarize data. Utilize probability and probability distributions to make informed decisions.
Learn to use techniques such as Principal Component Analysis, Hierarchical and Non-Hierarchical Clustering, Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Random Forests, Decision Trees, Gradient Boosting, and XGBoost.
Gain insights into Markov Chains, Extended Markov Chains, and Bellman’s Equation to understand reinforcement learning concepts.
Analyze relationships between variables and predict outcomes using various regression techniques.
Develop and apply Artificial Neural Networks, Recurrent Neural Networks, and Convolutional Neural Networks for tasks like image detection and analysis.
Employ techniques such as Bag of Words, TF-IDF, Word Embedding, and Sentiment Analysis to analyze and interpret text data.
Learn to build and deploy machine learning applications using frameworks such as Django, Flask, BentoML, and FastAPI.
Design and implement recommendation systems using Next Best Offer Product Recommendations, Collaborative Filtering, and Content-Based Filtering.
Requirements
- Basic knowledge of programming, preferably in Python.
- Familiarity with fundamental mathematical concepts and statistics.
- A passion for data and a willingness to learn new technologies.
- No prior experience in Data Science or Machine Learning is required, though it may be beneficial
Target Audience
- Aspiring Data Scientists and Analysts looking to build a solid foundation in data analysis and machine learning.
- Professionals seeking to enhance their skills in data-driven decision making and predictive modeling.
- Students and graduates in fields such as computer science, engineering, mathematics, or related disciplines.
- Individuals interested in applying data science techniques in their current roles or pursuing a career in artificial intelligence and machine learning.
Your Instructors
Free
