What you'll be learning
Introduces Python for Artificial Intelligence, focusing on AI techniques such as neural networks, natural language processing, and deep learning using TensorFlow and Keras. Best suited for those familiar with Python who wish to delve into AI technologies
- Understand the basicsof Python programming language.
- Gain knowledge of artificial intelligence and its applications.
- Utilize Python libraries for AI development.
- Set up a Python environment for AI projects.
- Upskill on NLP with LSTM
- Apply machine learning techniques using Python, including
supervised and unsupervised learning, data manipulation,
preprocessing, feature selection, and model evaluation. - Learn about deep learningconcepts, including neuralnetworks,
activation functions, training, optimization, and convolutional neural
networks (CNN). - Use TensorFlow and Keras for deep learning projects in Python.
Module 1: Introduction to Python
This Python syllabus provides a comprehensive introduction to programming with Python, covering its history and general-purpose capabilities. Key topics include the differences between compilers and interpreters, error types, and how Python executes code. It emphasizes applications in artificial intelligence, including neural networks and natural language processing.
Core concepts include syntax, data types, control statements, and data structures such as strings, lists, tuples, sets, and dictionaries. The course also explores functions, recursion, and lambda functions. Overall, it equips learners with the skills to effectively apply Python in various fields, particularly in AI and data science.
Module 2: Advanced Python
This training covers advanced Python skills, including exception handling, regular expressions, and file operations. It dives into data analysis with NumPy and Pandas, exploring data manipulation, indexing, grouping, and merging. It also teaches data visualization with Matplotlib and Seaborn, guiding learners to create plots, analyze distributions, and refine data insights effectively.
Module 3: Introduction to Artificial Intelligence
This module provides an in-depth introduction to machine learning, covering both supervised and unsupervised learning techniques. It explores key statistical concepts like central tendency, probability, and correlation, followed by error metrics and outlier analysis. Learners will dive into machine learning algorithms, including regression models, decision trees, SVMs, and ensemble methods (Random Forest, Gradient Boosting, XGBoost). It also includes unsupervised learning topics like clustering (K-means, DBSCAN) and dimensionality reduction through PCA. Hands-on labs using Scikit-Learn and real-world datasets help learners build, evaluate, and optimize models effectively.
Module 4: Time Series Modeling
This module focuses on time series modeling, beginning with an overview of key concepts and objectives. Learners will explore various time series pattern types in two parts, distinguishing between different behaviors in data over time. The module covers essential topics such as white noise and the importance of stationarity in time series analysis. Additionally, it addresses techniques for removing non-stationarity, equipping learners with the skills to effectively analyze and model time-dependent data.
Module 5: NLP(Natural Language Processing) & Deep Learning
Module 6: Industry Projects
This module focuses on industry projects that apply advanced data science and machine learning techniques. The first project involves sentiment analysis for customer feedback, aimed at improving customer experience for a global e-commerce giant. The second project focuses on image recognition for product quality control, enhancing manufacturing efficiency at a leading tech startup. These projects provide practical insights into how data-driven approaches can optimize operations and enhance user satisfaction in real-world applications.
- A stand-out portfolio with new projects &a 93% better resume
- 3x higher chance of getting hired with amazing skills & certification
