Get a head start with our AIML training program
Fast Track Your Career with Artificial Intelligence Certification Course
At AIML Consultants, we understand the significance and complexity of emerging technologies and see a need to train our staff so they can be successful in their careers.
Our state-of-the-art rigorous training program is an eight-week paid program devoted to preparing candidates with job-oriented skills for a rewarding career in IT industry.
Select candidates will be offered credentials to access the World's Leading Online Learning Platform where they can build a successful career in AI with an Artificial Intelligence Course & Certification.
Opportunity to master industry-valued skills like Python, NLP, Machine learning, Generative AI, prompt engineering, explainable AI, and more.
Once you make the payment, course materials will be available to you.
Artificial Intelligence Course Curriculum Breakdown
8 Weeks • 150 Lectures • 20 hrs Total Length
Week 1 & 2: AI Intro, Python for AI/ML, SQL
- What is AI, Why AI?
- Programming for Problem Solving
- Artificial intelligence fundamentals
- Components of AI
- Python Overview
- Important Python features
- Python installation, Anaconda Python distribution
- Python Functions and Packages
- Scikit-Learn for Machine Learning & LNP
- Working with Data Structures, Arrays,
- Math Operators and Expressions
- Vectors & Data Frames
- Pandas, NumPy, Matplotlib
- Numpy for Statistical Analysis
- Matplotlib & Seaborn for Data Visualization
- Descriptive Statistics
- Probability & Conditional Probability
- Probability Distributions
- Working with Databases
- How to create a Database instance on Cloud
- CREATE Table Statement
- SELECT Statement
- COUNT, DISTINCT, LIMIT
- INSERT, UPDATE and DELETE Statements
- Information and Data Models
- Types of Relationships
- Sub-Queries and Nested Selects
Week 2 & 3: Machine Learning, Supervised Learning
- Introduction to Machine Learning
- Understanding Supervised Learning.
- Linear Regression Theory
- Supervised Learning Regression
- Linear Regression
- Multiple Linear Regression
- Bias-Variance Trade-Off
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Simple Vector Machine (SVM)
- Decision Trees
- Bagging
- Boosting, AdaBoost & XGBoost
- Naïve Bayes Classifier
Week 4 & 5: Machine Learning, Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Dimensionality Reduction
- Linear Discriminant Analysis
- Time Series Modelling
- Principal Component Analysis (PCA)
- Reinforcement Learning
- Model Comparisons
- Analysis Considerations
- Clustering Animals
- Customer Segmentation
- Optimal Number of Clusters
- Cluster Based Incentivization
- Image Segmentation
Week 5 & 6: Deep Learning w Tensorflow
- What is Neural Networks
- Gradient Descent
- Perceptron & Neural Networks
- Batch Normalization
- Activation and Loss functions
- Hyper parameter tuning
- Deep Neural Networks
- Tensor Flow & Keras for Neural Networks & Deep Learning
- Understand Neural Networks in Detail
- PyTorch Tensors
- PyTorch Autograd
- Illustrate Multi-Layer Perceptron
- Backpropagation – Learning Algorithm
- Understand Backpropagation – Using Neural Network Example
- MLP Digit-Classifier using TensorFlow
- Building a multi-layered perceptron for classification
Week 7: Neural Networks
- Artificial neural networks in Deep Learning
- Linear & Logistic Regression With Tensorflow
- Activation Functions
- Illustrate Perceptron
- Important Parameters of Perceptron
- Shallow, Deep neural networks
- Optimization algorithms
- Hyper-parameter tuning
- Batch Normalization and Programming Frameworks
- Single layer NN & Multilayer NN
- Back propagation, Dynamic Programming
- Mathematical Take on NN
- Function Approximator
- Kernels, Padding & Strides
- Convolutional Layer, Max-Pooling
- CNN Architectures – VGGNet, AlexNet, Inception Network
- Rnns with back propagation
- Long short-term memory (lstm)
- Link with Linear Regression
- Recurrent Neural Network
- Backpropagation in RNN.
- Types of RNN, Architecture of RNN
Week 8: Natural Language Processing
- Understanding NLP
- Stop Words, Tokenization
- Stemming and lemmatization
- Bag of Words Model
- Word Vectorizer
- POS Tagging
- Named Entity Recognition
- Text Pre-processing
- Topic Modelling
- Noise Removal
- Lexicon Normalization, Lemmatization, Stemming
- Object Standardization
- Text Classification, Text Matching
- Levenshtein Distance
- Phonetic Matching, Flexible String Matching