AI+ Developer

Get hands-on with the tools and technologies that power the AI ecosystem.

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  • 28 modules
  • 1 Examination
  • 50 multiple choice questions, 90 Minutes
  • Passing Score: 70%

About certification

  • This certification program offers a comprehensive journey through the key domains of artificial intelligence, specifically tailored for developers. From mastering Python fundamentals to advanced concepts, mathematics, statistics, optimization techniques, and deep learning, this program equips developers with indispensable skills.
    The curriculum encompasses data pre-processing, exploratory data analysis, feature engineering, selection, and dimensionality reduction. Additionally, participants can specialize in NLP, computer vision, or reinforcement learning. The program also covers time series analysis, model explainability, and the intricacies of model deployment. Upon successful completion, you'll be awarded a certification acknowledging your proficiency in these pivotal artificial intelligence areas, positioning you as a well-prepared developer ready to tackle real-world AI challenges and innovations.

Prerequisites

  • Experience dabbling in coding, preferably in common languages like Python or Java.
  • Basic understanding of Machine Learning and how computers process data.
  • Introductory knowledge of Neural Networks and their foundational concepts.
  • Familiarity with language processing tools and introductory chatbot concepts.
  • Understanding of how AI processes and interprets images.
  • Insight into AI learning techniques and continuous improvement methods.
  • Experience or knowledge in deploying AI solutions and presenting them to an audience.

There are 28 modules in this course

    • 1.1 Data Types
    • 1.2 Variables and Assignment
    • 1.3 Operators
    • 1.4 Control Flow
    • 1.5 Functions and Arguments
    • 1.6 Strings and Methods
    • 1.7 Data Structures
    • 1.8 Modules and Importing
    • 1.9 File I/O
    • 1.10 Exceptions and Error Handling

    • 2.1 Object-Oriented Programming
    • 2.2 Decorators
    • 2.3 Generators and Iterators
    • 2.4 Lambda Functions
    • 2.5 Regular Expressions
    • 2.6 Debugging and Testing
    • 2.7 Multi-Processing & Multi-Threading
    • 2.8 Essential Libraries for Data Science
    • 2.9 Working with Databases
    • 2.10 API Development
    • 2.11 Package Creation and Distribution
    • 2.12 Performance Optimization and Profiling
    • 2.13 Design Patterns

    • 3.1 Linear Algebra
    • 3.2 Matrix Operations
    • 3.3 Vector Spaces
    • 3.4 Eigenvectors and Eigenvalues
    • 3.5 Linear Transformations in Python
    • 3.6 Matrix Factorization
    • 3.7 Introduction to Tensor Operations in Linear Algebra

    • 4.1 Differential Calculus
    • 4.2 Integration in Python

    • 5.1 Probability Basics
    • 5.2 Calculating Basic Probabilities
    • 5.3 Probability Distributions (Normal, Binomial, Poisson)
    • 5.4 Conditional Probability
    • 5.5 Monte Carlo Simulation
    • 5.6 Central Limit Theorem
    • 5.7 Statistical Inference in Probability
    • 5..8 Probability in Machine Learning Algorithms
    • 5.9 Decision Making Under Uncertainty - 5.10 Real-world Applications of Probability in Data Science

  1. 6.1 Introduction to Statistics for Data Science
  2. 6.2 Descriptive Statistics
    • 6.2.1 Measures of Central Tendency
    • 6.2.2 Measures of Variability
    • 6.2.3 Data Visualizations
  3. 6.3 Probability and Distributions
    • 6.3.1 Normal Distribution
    • 6.3.2 Binomial Distribution
    • 6.3.3 Poisson Distribution
  4. 6.4 Statistical Inference
    • 6.4.1 Hypothesis Testing
    • 6.4.2 Confidence Intervals
    • 6.4.3 Significance Testing

  1. 7.1 Introduction to Optimization in Data Science
  2. 7.2 Gradient Descent
    • 7.2.1 Basic Gradient Descent
    • 7.2.2 Gradient Descent in Neural Networks
  3. 7.3 Stochastic Gradient Descent
    • 7.3.1 Basics of Stochastic Gradient Descent
  4. 7.4 Adaptive Learning Rate Methods
    • 7.4.1 Adam

  1. 8.1 Machine Learning Basics

  1. 9.1 Deep Learning Basics

  1. 10.1 Reinforcement Learning Basics

  1. 11.1 Introduction to Evaluation Metrics in Machine Learning
  2. 11.2 Classification Metrics
    • 11.2.1 Accuracy
    • 11.2.2 Precision
    • 11.2.3 Recall
    • 11.2.4 F1 Score
    • 11.2.5 AUC ROC
  3. 11.3 Regression Metrics
    • 11.3.1 Mean Absolute Error (MAE)
    • 11.3.2 Mean Squared Error (MSE)
    • 11.3.3 Root Mean Squared Error (RMSE)
    • 11.3.4 R-squared
  4. 11.4 Importance of Multiple Metrics
  5. 11.5 Choosing Metrics Based on Business Context
  6. 11.6 Evaluating Metrics on Test Set

  1. 12.1 Explanation of the Topics
  2. 12.2 Data Cleaning
  3. 12.3 Data Transformation
  4. 12.4 Feature Engineering
  5. 12.5 Feature Selection
  6. 12.6 Data Reduction

  1. 13.1 Introduction to EDA in Python
  2. 13.2 Importing and Loading Data
  3. 13.3 Data Cleaning
    • 13.3.1 Handling Missing Values
    • 13.3.2 Handling Duplicate Data
    • 13.3.3 Data Formatting
  4. 13.4 Univariate Analysis
    • 13.4.1 Summary Statistics
    • 13.4.2 Visualizations
  5. 13.5 Bivariate and Multivariate Analysis
    • 13.5.1 Scatterplots
    • 13.5.2 Heatmaps
  6. 13.6 Data Transformations and Encodings
  7. 13.7 Identifying Outliers and Anomalies
  8. 13.8 Tools for EDA in Python
    • 13.8.1 Pandas
    • 13.8.2 Matplotlib
    • 13.8.3 Seaborn
  9. 13.9 The Iterative Nature of EDA

  1. 14.1 Introduction to Feature Engineering
  2. 14.2 Feature Creation
    • 14.2.1 Binning
    • 14.2.2 Logarithmic Transforms
    • 14.2.3 Feature Interactions
    • 14.2.4 Aggregations
  3. 14.3 Feature Selection
    • 14.3.1 Correlation Analysis
    • 14.3.2 Recursive Feature Elimination
    • 14.3.3 Principal Component Analysis
  4. 14.4 Feature Extraction
    • 14.4.1 PCA
    • 14.4.2 Autoencoders
    • 14.4.3 Clustering
  5. 14.5 Feature Scaling
    • 14.5.1 Min-Max Scaling
    • 14.5.2 Standardization
    • 14.5.3 Normalization
  6. 14.6 Missing Value Imputation
    • 14.6.1 Value Deletion
    • 14.6.2 Mean/Median/Mode Imputation
    • 14.6.3 End of Distribution Imputation
  7. 14.7 Discretization
  8. 14.8 Feature Encoding
    • 14.8.1 Label Encoding
    • 14.8.2 One-Hot Encoding
    • 14.8.3 Binary Encoding

  1. 15.1 Filter Methods
  2. 15.2 Wrapper Methods
  3. 15.3 Embedded Methods

  1. 16.1 Introduction to Dimensionality Reduction
  2. 16.2 Problems with High-Dimensional Data
    • 16.2.1 Overfitting
    • 16.2.2 Curse of Dimensionality
    • 16.2.3 Computational Bottlenecks
  3. 16.3 Benefits of Dimensionality Reduction
    • 16.3.1 Computational Efficiency
    • 16.3.2 Improved Visualization
    • 16.3.3 Statistical Power
  4. 16.4 Common Techniques
    • 16.4.1 Principal Component Analysis (PCA)
    • 16.4.2 Linear Discriminant Analysis (LDA)
    • 16.4.3 t-SNE
  5. 16.5 Key Takeaways and Best Practices
    • 16.5.1 Balance Between Reduction and Information Loss
    • 16.5.2 Domain Knowledge in Technique Selection

  1. 17.1 Introduction to Data Visualization
  2. 17.2 Types of Data Visualization
    • 17.2.1 Exploratory Data Visualization
  3. 17.3 Categories of Visualizations
    • 17.3.1 Temporal Visualizations
    • 17.3.2 Multidimensional Visualizations
  4. 17.4 Popular Types of Visualizations
    • 17.4.1 Bar Charts and Histograms
    • 17.4.2 Line Graphs
    • 17.4.3 Scatter Plots
    • 17.4.4 Pie Charts
    • 17.4.5 Heat Maps
  5. 17.5 Key Takeaways and Best Practices
    • 17.5.1 Storytelling Through Visualization
    • 17.5.2 Audience and Context Consideration
    • 17.5.3 Simplicity and Focus
    • 17.5.4 Tool Selection
    • 17.5.5 The Art of Data Visualization
    • 17.5.6 Pushing Design Boundaries

  1. 18.1 Introduction to Supervised Learning Algorithms
  2. 18.2 Common Tasks in Supervised Learning
  3. 18.2.1 Classification
  4. 18.2.2 Regression
  5. 18.3 Popular Algorithms
    • 18.3.1 Linear Regression
    • 18.3.2 Logistic Regression
    • 18.3.3 Decision Trees
    • 18.3.4 Random Forests
    • 18.3.5 Support Vector Machines (SVMs)
    • 18.3.6 Naive Bayes Classifier
    • 18.3.6 KNN

  1. 19.1 Introduction to Unsupervised Learning Algorithms
  2. 19.2 Types of Unsupervised Learning Algorithms
  3. 19.2.1 Clustering Algorithms
    • 19.2.1.1 K-means Clustering
    • 19.2.1.2 Hierarchical Clustering
    • 19.2.1.3 Density-based Clustering (DBSCAN)
  4. 19.2.2 Association Algorithms
    • 19.2.2.1 Apriori Algorithm
    • 19.2.2.2 FP-growth Algorithm
  5. 19.2.3 Dimensionality Reduction Algorithms
    • 19.2.3.1 Principal Component Analysis (PCA)
    • 19.2.3.2 t-SNE

  1. 20.1 AdaBoost Algorithm Explanation
  2. 20.2 XGBoost Algorithm Explanation
  3. 20.3 CatBoost Algorithm Explanation
  4. 20.4 GradiendBoost Algorithm Explanation

  1. 21.1 Sampling Methods
    • 21.1.1 Undersampling
    • 21.1.2 Oversampling
    • 21.1.3 Synthetic Minority Over-sampling Technique (SMOTE)
  2. 21.2 Algorithm Modifications
    • 21.2.1 Class Weighting
    • 21.2.2 Asymmetric Misclassification Costs
    • 21.2.3 Decision Thresholds

  1. 22.1 Introduction to Hyperparameters
    • 22.1.1 What are Hyperparameters?
    • 22.1.2 Importance in Model Training
  2. 22.2 Hyperparameter Tuning Techniques
    • 22.2.1 Grid Search
    • 22.2.2 Random Search
    • 22.2.3 Bayesian Optimization
    • 22.2.4 Evolutionary Algorithms
  3. 22.3 Challenges in Hyperparameter Tuning
    • 22.3.1 Selecting Hyperparameters to Tune
    • 22.3.2 Choosing Performance Metrics
    • 22.3.3 Computational Complexity
    • 22.3.4 Overfitting
  4. 22.4 Strategies for Efficient Tuning
    • 22.4.1 Early Stopping
    • 22.4.2 Parallelization
  5. 22.5 Tools for Hyperparameter Tuning
    • 22.5.1 Optuna
    • 22.5.2 Hyperopt
    • 22.5.3 Scikit-Optimize

  1. 23.1 Introduction to Time Series Data
    • 23.1.1 What is Time Series Data?
    • 23.1.2 Examples and Applications
  2. 23.2 Key Aspects of Time Series Analysis
    • 23.2.1 Trends
    • 23.2.2 Seasonality
    • 23.2.3 Cycles
    • 23.2.4 Noise
  3. 23.3 Stationarity and Autocorrelation
    • 23.3.1 Understanding Stationarity
    • 23.3.2 Autocorrelation in Time Series
  4. 23.4 Time Series Forecasting
    • 23.4.1 Forecasting Techniques
    • 23.4.2 Exponential Smoothing
  5. 23.5 Time Series Models
    • 23.5.1 ARIMA Models
    • 23.5.2 Neural Networks in Time Series
  6. 23.6 Visualization in Time Series Analysis
    • 23.6.1 Importance of Visualization
    • 23.6.2 Tools and Techniques
  7. 23.7 Key Takeaways and Best Practices
    • 23.7.1 Importance of Domain Knowledge
    • 23.7.2 Advanced Techniques and Innovations
    • 23.7.3 Feature Engineering in Time Series

  1. 24.1 Neural Networks
  2. 24.2 Activation Function
  3. 24.3 Loss Functions
  4. 24.4 Optimizers
  5. 24.5 Regularization
  6. 24.6 Forward Propagation
  7. 24.7 Backward Propagation
  8. 24.8 Hyperparameter Tuning in Neural Networks

  1. 25.1 NLP
  2. 25.2 Computer Vision
  3. 25.3 Reinforcement Learning

  1. 26.1 LLMs-Text
  2. 26.2 LLMs - Text to Image

  1. 27.1 Explanation of the Topics
  2. 27.2 Explainable Modeling
  3. 27.3 Model-Agnostic Methods
  4. 27.4 Interactive Explanations
  5. 27.5 Explainable Deep Learning
  6. 27.6 Visual Explanations
  7. 27.7 Natural Language Explanations

  1. 28.1 What is Model Deployment
  2. 28.2 Key Steps in Deploying a Model
  3. 28.3 Challenges with Model Deployment
  4. 28.4 Best Practices

Certification Outcome

  • This certification program offers a comprehensive journey through the key domains of artificial intelligence, specifically tailored for developers. From mastering Python fundamentals to advanced concepts, mathematics, statistics, optimization techniques, and deep learning, this program equips developers with indispensable skills.
    The curriculum encompasses data pre-processing, exploratory data analysis, feature engineering, selection, and dimensionality reduction. Additionally, participants can specialize in NLP, computer vision, or reinforcement learning. The program also covers time series analysis, model explainability, and the intricacies of model deployment. Upon successful completion, you'll be awarded a certification acknowledging your proficiency in these pivotal artificial intelligence areas, positioning you as a well-prepared developer ready to tackle real-world AI challenges and innovations.

Market insight
As artificial intelligence becomes increasingly pervasive across various industries, there is a growing demand for AI developers who possess the skills to harness it's transformative potential. This specialized AI developer certification program has been meticulously crafted to align with the evolving needs of established tech giants and emerging AI startups in this dynamic field.
Value proposition
Participants in this program will not only gain proficiency in fundamental AI concepts but also acquire advanced technical skills essential for the successful implementation of AI solutions in real-world applications. This certification empowers AI developers with the knowledge and expertise needed to excel in the competitive world of AI development.
Additional features
Interactive Sessions: Engage in discussions with industry experts and peers. Hands-on Exercises: Practical tasks to apply learned concepts in real-world scenarios. Case Studies: Dive deep into real business challenges and AI-driven solutions. Post-Certification Support: Access to a community of AI experts and enthusiasts for continuous learning and networking