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Course Description
This comprehensive course provides a thorough introduction to Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning, leveraging the power of Python. Designed for beginners and professionals alike, this course will equip you with the foundational knowledge and practical skills needed to build intelligent systems and data-driven solutions
What you'll learn in this course?
- Understand the fundamentals of AI, ML, and Deep Learning.
- Master the use of Python and its libraries for data analysis, visualization, and model building.
- Learn to implement and evaluate various machine learning algorithms.
- Gain proficiency in constructing and training deep learning models.
- Apply AI and ML techniques to solve real-world problems across various domains.
Course Curriculum
Introduction to Artificial Intelligence & Machine Learning
Introduction to Machine Learning tools and techniques
Introduction to Statistics: Data Distributions, Mean, Variance, Standard Deviation, Probability
Data Visualization & Graphs: Types of Charts, Factors Influencing Chart Selection, Scatter Plot, Mekko, Heat Map, Bubble Chart
Introduction to Python: Software Setup, Data Types, Strings, Variables, Loops, Decision Making
Sequences and File Operations: Python I/O Functions, Lists, Tuples, Functions, OOPs
Working with Modules and Handling Exceptions: Standard Libraries, Modules in Python (OS, Sys, Date and Time, etc.), Errors and Exception Handling
Introduction to NumPy & Pandas: Creating Arrays, Mathematical Operations, Reading/Writing Data, Data Manipulation
Data Visualization using Python modules: Matplotlib Library, Grids, Axes, Plots, Markers, Colors, Fonts, Bar Graphs, Pie Charts, Histograms
Web Scraping using Python Libraries: Beautiful Soup, Scrapy, Hands-on Web Scraping
Data Handling, Data Validation, and Graphs: Packages used in Machine Learning, Data Importing, Working with Datasets, Descriptive Statistics, Central Tendency, Variance, Percentiles, Outlier Detection, Variable Distribution Charts
Introduction to Machine Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning
Regression Analysis: Correlation, Simple Regression Models, R-Square, Multiple Regression, Multicollinearity, Individual Variable Impact
Decision Trees: Segmentation, Entropy, Information Gain, Building and Validating Decision Trees, Pruning, Fine-tuning, Prediction
Sentiment Analysis: Understanding Sentiment Analysis, Hands-on Sentiment Analysis using Twitter Data
Supervised Learning: Naïve Bayes Classifier, Support Vector Machine
Unsupervised Learning & Cluster Analysis: Supervised vs. Unsupervised Learning, Cluster Analysis, K-Means Clustering Algorithm, Building and Interpreting Clusters
Chatbots: Understanding Chatbots, Hands-on Chatbot Session
LEARN AT YOUR OWN PACE
Training Options
Discover our range of training programs and choose the ones that suit you best. Enroll today and begin your learning journey with us!
