Introduction to Python
- Concepts of Python Programming
- Configuration of Development Environment
- Variable and Strings
- Functions, Control Flow and Loops
- Tuple, Lists and Dictionaries
- Standard Libraries
Data Science Fundamentals
- Introduction to Data Science
- Real World Use-Cases of Data Science
- Walkthrough of Data Types
- Data Science Project Lifecycle
Introduction to NumPy
- Basics of NumPy Arrays
- Mathematical Operations in NumPy
- NumPy Array Manipulation
- NumPy Array Broadcasting
Data Manipulation with Pandas
- Data Structures in Pandas-Series and Data Frames
- Data Cleaning in Pandas
- Data Manipulation in Pandas
- Handling Missing Values in Datasets
- Hands-on: Implement NumPy Arrays and Pandas Data Frames
Data Visualization in Python
- Plotting Basic Charts in Python
- Data Visualization with Matplotlib
- Statistical Data Visualization with Seaborn
- Hands-on: Coding Sessions Using Matplotlib, Seaborn Package
Exploratory Data Analysis
- Introduction to Exploratory Data Analysis (EDA) Steps
- Plots to Explore Relationship Between Two Variables
- Histograms, Box plots to Explore a Single Variable
- Heat Maps, Pair plots to Explore Correlations
Introduction to Machine Learning
- What is Machine Learning?
- Use Cases of Machine Learning
- Types of Machine Learning - Supervised to Unsupervised methods
- Machine Learning Workflow
- Introduction to Linear Regression
- Use Cases of Linear Regression
- How to Fit a Linear Regression Model?
- Evaluating and Interpreting Results from Linear Regression Models
- Predict Bike Sharing Demand
- Introduction to Logistic Regression
- Logistic Regression Use Cases
- Understand Use of odds & Logic Function to Perform Logistic Regression
- Predicting Credit card Default Cases
Decision Trees & Random Forest
- Introduction to Decision Trees & Random Forest
- Understanding Criterion (Entropy & Information Gain) used in Decision Trees
- Using Ensemble Methods in Decision Trees
- Applications of Random Forest
Model Evaluation Techniques
- Introduction to Evaluation Metrics and Model Selection in Machine Learning
- Importance of Confusion Matrix for Predictions
- Measures of Model Evaluation - Sensitivity, Specificity, Precision, Recall & f-score
- Use AUC-ROC Curve to Decide Best Model
Dimensionality Reduction using PCA
- Introduction to Curse of Dimensionality
- What is Dimensionality Reduction?
- Technique Used in PCA to Reduce Dimensions
- Applications of Principle Component Analysis (PCA)
- Optimize Model Performance using PCA on SPECTF heartdata
- Introduction to K-NN
- Calculate Neighbours using Distance Measures
- Find Optimal Value of K in K-NN Method
- Advantage & Disadvantages of K-NN
Naive Bayes Classifier
- Introduction to Naïve Bayes Classification
- Refresher on Probability Theory
- Applications of Naive Bayes Algorithm in Machine Learning
- Classify Spam Emails Based on Probability
- Introduction to K-Means Clustering
- Decide Clusters by Adjusting Centroids
- Understand Applications of Clustering in Machine Learning
- Segment Hands in Pokerdata
Support Vector Machines
- Introduction to SVM
- Figure Decision Boundaries Using Support Vectors
- Identify Hyperplane in SVM
- Applications of SVM in Machine Learning
Time Series Forecasting
- Components of Time Series Data
- Interpreting Autocorrelation & Partial Autocorrelation Functions
- Introduction to Time Series Analysis
- Stationary Vs Non Stationary Data
- Stationary data and Implement ARIMA model
- Applications of Apriori algorithm
- Understand Association rule
- Developing product Recommendations using Association Rules
- Analyse Online Retail Data using Association Rules
- Introduction to Recommender Systems
- Types of Recommender Systems - Collaborative, Content Based & Hybrid
- Types of Similarity Matrix (Cosine, Jaccard, Pearson Correlation)
- Segment Hands in Poker DataBuild Recommender systems on Movie data using K-NN Basics
Linear Discriminant Analysis
- Recap of Dimensionality Reduction Concepts
- Types of Dimensionality Reduction
- Dimensionality Reduction Using LDA
- Apply LDA to Determine Wine Quality
- Introduction to Anomaly Detection
- How Anomaly Detection Works?
- Types of Anomaly Detection: Density Based, Clustering etc. NET Based Commands
- Detect Anomalies on Electrocardiogram Data
- Introduction to Ensemble Learning
- What are Bagging and Boosting techniques?
- What is Bias Variance Trade Off?
- Predict Wage (annual income) Classes from Adult Census Data
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Python is an interpreted, high-level, general-purpose programming language.
Project Jupyter is a nonprofit organization created to "develop open-source software, open-standards, and services for interactive computing across dozens of programming languages"
Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.
Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy
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Frequently Asked Questions
What is artificial intelligence?
AI can be described as an area of computer science that simulates human intelligence in machines. It’s about smart algorithms making decisions based on the available data. Whether it’s Amazon’s Alexa or a self-driving car, the goal is to mimic human intelligence at lightning speed (and with a reduced rate of error).
What are intelligent agents?
An intelligent agent is an autonomous entity that leverages sensors to understand a situation and make decisions. It can also use actuators to perform both simple and complex tasks. In the beginning, it might not be so great at performing a task, but it will improve over time. The Roomba vacuum cleaner is an excellent example of this.
What’s the most popular programming language used in AI?
The open-source modular programming language Python leads the AI industry because of its simplicity and predictable coding behavior. It's popularity can be attributed to open-source libraries like Matplotlib and NumPy, efficient frameworks such as Scikit-learn, and practical version libraries like Tensorflow and VTK.
What are AI neural networks?
Neural networks in AI mathematically model how the human brain works. This approach enables the machine to think and learn as humans do. This is how smart technology today recognizes speech, objects, and more.
What’s a Turing test?
The Turing test, named after Alan Turing, is a method of testing a machine’s human-level intelligence. For example, in a human-versus-machine scenario, a judge will be tasked with identifying which terminal was occupied by a human and which was occupied by a computer based on individual performance. Whenever a computer can pass off as a human, it’s deemed intelligent. The game has since evolved, but the premise remains the same.
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