Artificial Intelligence And Data Science Syllabus Course Syllabus
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Learn about Artificial Intelligence and Data Science with SLA Institute, the top institute for Artificial Intelligence And Data Science Syllabus. Our AI and Data Science syllabus covers essential topics to help you understand and apply AI and machine learning techniques. You’ll explore key concepts such as data analysis, machine learning algorithms, deep learning, natural language processing, and data visualization. Gain hands-on experience working with industry-standard tools and platforms like Python, TensorFlow, and Scikit-learn. SLA Institute provides excellent training and career support to prepare you for roles in AI and Data Science. Join our AI DS course syllabus with 100% Placement Support to gain practical skills, build confidence, and secure top job opportunities in this rapidly growing field. Start your journey to a successful career with the syllabus of artificial intelligence and data science at SLA Institute.
Course Syllabus
Download SyllabusModule 1: Introduction to AI and Data Science
- Overview of Artificial Intelligence (AI) and Data Science
- History and evolution of AI
- Applications of AI in various industries
- Introduction to Data Science and its importance
- Tools and technologies in AI and Data Science
Module 2: Programming for AI and Data Science
- Python programming basics for AI and Data Science
- Libraries: NumPy, Pandas, Matplotlib, Seaborn
- Data structures and algorithms
- Introduction to Jupyter Notebook
Module 3: Data Preprocessing and Exploration
- Data cleaning and handling missing data
- Feature selection and extraction
- Exploratory Data Analysis (EDA)
- Data visualization techniques using Python
- Working with large datasets
Module 4: Machine Learning Fundamentals
- Introduction to Machine Learning (ML)
- Supervised learning: Linear regression, Logistic regression, Decision trees
- Unsupervised learning: K-means clustering, Hierarchical clustering
- Evaluation metrics: Accuracy, Precision, Recall, F1-score
- Cross-validation and model selection
Module 5: Advanced Machine Learning Algorithms
- Support Vector Machines (SVM)
- Random Forests and Ensemble Methods
- K-Nearest Neighbors (KNN)
- Naive Bayes Classifier
- Hyperparameter tuning
Module 6: Deep Learning and Neural Networks
- Introduction to Deep Learning
- Neural Networks and architecture
- Backpropagation and Gradient Descent
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Introduction to TensorFlow and Keras
Module 7: Natural Language Processing (NLP)
- Introduction to NLP and text mining
- Text preprocessing: Tokenization, Lemmatization, and Stopwords removal
- Sentiment analysis and text classification
- Word embeddings: Word2Vec, GloVe
- Introduction to NLTK and spaCy
Module 8: Data Science and AI in Business
- AI in business applications: Marketing, Finance, Healthcare
- Predictive analytics and recommendation systems
- Time series analysis and forecasting
- Case studies of AI and Data Science in real-world scenarios
Module 9: Big Data and AI
- Introduction to Big Data technologies
- Hadoop, Spark, and distributed computing
- Data processing and management with Big Data tools
- Scaling AI models with Big Data
Module 10: AI and Data Science Ethics and Future Trends
- Ethical considerations in AI
- Bias in AI models and fairness
- AI safety and regulations
- Future trends in AI and Data Science
Module 11: Capstone Project
- Hands-on project involving real-world datasets
- End-to-end AI and Data Science solution implementation
- Model building, evaluation, and deployment
- Presenting findings and insights
Module 12: Career Preparation and Job Readiness
- Resume building and interview preparation
- Soft skills and communication for Data Scientists and AI Engineers
- Placement assistance and job opportunities in AI and Data Science
In conclusion, the AI and Data Science syllabus provides students with the key skills needed to succeed in the fast-growing fields of Artificial Intelligence and Data Science. The AI DS course syllabus covers important topics such as data processing, machine learning, deep learning, and natural language processing, giving students practical experience with industry tools. The syllabus of artificial intelligence and data science ensures a complete understanding of both areas, preparing students for exciting career opportunities in AI and Data Science. This comprehensive Artificial Intelligence And Data Science Syllabus is designed to equip learners with the expertise required to excel in these dynamic fields.
