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Basic Concept

  • Train,Test & Validation Distribution
  • ML Strategy
  • Computation Graph
  • Evaluation Metric
  • Human Level Performance


  • Linear Regression
  • Logistic Regression
  • Gradient Descent
  • Decision Tree
  • Random Forest
  • Bagging & Boosting
  • KNN


  • K-Means
  • Hierarichal Clustering


  • Basic Programming
  • NLP Libraries
  • OpenCV

Basic Statistics

  • Sampling & Sampling Statistics
  • Hypothesis Testing


  • Derivatives
  • Optimization

Linear Algebra

  • Function
  • Scalar-Vector-Matrix
  • Vector Operation


  • Space
  • Probability
  • Distribution


  • Intro
  • Deep Learning Importance [Strength & Limitation]
  • SP | MLP

Feed Forward & Backward Propagation

  • Neural Network Overview
  • Neural Network Representation
  • Activation Function
  • Loss Function
  • Importance of Non-linear Activation Function
  • Gradient Descent for Neural Network

Practical Aspect

  • Train, Test & Validation Set
  • Vanishing & Exploding Gradient
  • Dropout
  • Regularization


  • Bias Correction
  • RMS Prop
  • Adam, Ada, AdaBoost
  • Learning Rate
  • Tuning
  • Softmax


  • Scikit Learn
  • NLTK
  • Spacy & Gensim
  • OpenCV
  • Tensorflow
  • Keras

Text Processing

  • Representation
  • Data Cleaning
  • Data Preprocessing
  • Similarity

Image Processing

  • Image
  • Image Transformation
  • Filters
  • Noise Removal
  • Correlation & Convolution
  • Edge Detection
  • Non Maximum Suppression & Hysteresis
  • Fourier Domain
  • Video Processing
Speech Data Analytics
Feature Extraction

  • What is Face Recognition
  • One Shot Learning
  • Siamese Network
  • Triplet Loss
  • Face Verification
  • Neural Style Transfer
  • Deep Conv Net Learning

Object Detection

  • Image Feature
  • Descriptors
  • Detection & Classification


  • Computer Vision
  • Padding
  • Convolution
  • Pooling
  • Why Convolution

Deep Convolution Model

  • Case Studies
  • Classic Networks
  • Inception
  • Open Source Implementation
  • Transfer Learning

Detection Algorithm

  • Object Localization
  • Landmark Detection
  • Object Detection
  • Bounding Box Prediction
  • Yolo

Face Recognition

  • What is Face Recognition
  • One Shot Learning
  • Siamese Network
  • Triplet Loss
  • Face Verification
  • Neural Style Transfer
  • Deep Conv Net Learning


  • Why Sequence Model
  • RNN Model
  • Backpropogation through time
  • Different Type of RNNs
  • GRU
  • LSTM
  • Biderectional LSTM
  • Deep RNN
  • Word Embedding
  • Debiasing
  • Negative Sampling
  • Elmo & Bert
  • Beam Search
  • Attention Model


  • Autoencoders & Decoders
  • Adversial Network
  • Active Learning


  • Q Learning
  • Exploration & Exploitation

Introduction to Machine Learning

  • Business Case evaluation
  • Data requirements and collection
  • Evaluation metrics

Machine Learning

  • Profit of 50_startups data prediction
  • Extra marital affair prediction
  • Fraud data analytics
  • Fabric sales analysis
  • Classification of animals data
  • Crime data analysis using clustering method and airlines data to obtain optimum number of clusters.

Python Programming

  • Resource Information Analysis
  • Text Cleaning of Customer reviews using NLP
  • Image Manipulation (Loading, Rotation etc.)

Mathematics Foundation

  • Sampling & Sampling Statistics
  • Hypothesis Testing
  • Calculus Problems
  • Linear Algebra Problems
  • Probability Problems
Intro to Neural Network & Deep Learning
Parameter & Hyperparameter

  • Risk Evaluation
  • Prediction of claim amount
  • Emotor temp prediction
  • User Behavioural Pattern
  • Probability Problems

(2 ANN assignments+ 2 Parameter and hyperparameters)

Data Processing

  • User review data load and familiarity with data and environment
  • E commerce Product Similarity
  • Sentiment classification of movie reviews
  • Emotion Mining of user reviews
  • Cleaning of hand-written digits data
  • Image data Augumentation
  • Facial feature detection
  • Video Analysis of a short film
  • Speech data Analysis w.r.t emotion


  • Ecommerce product image classification
  • Disease prediction based on images

(2 CNN algorithms)

  • Vehicle identification(Object Detection)
  • Animal Classification(Object Classification)
  • Spatial Image classification (Image segmentation)
  • Face detection
  • Face recognition (Attendance using facial recognition)


  • Next word prediction (Vanilla RNN)
  • Twitter data analysis using Named Entity Recognition(NER)
  • Retail data – Word2vec
  • NER and Forecasting of Oil price prediction
  • Auto text composer (NER language model)
  • Auto text composer (NER language model)
  • Q and A Chatbot
  • Real life voice Recognition


  • Machine Translation
  • New Image generation based on existing images

Reinforcement Learning

  • Game Intelligence


This AI Certification Course is helpful for the skill development and improving knowledge & AI engineer course offers to become a successful AI Engineer. AI Course makes find a solution for complex problems, by creating deep learning, machine learning, computer vision.

In this Artificial Intelligence (AI) course, you will learn

  • Understand the basics of AI and how these technologies are re-defining the AI industry
  • Learn the major terminology used in AI space
  • Learn major applications of AI through a different case study

Artificial Intelligence (AI) is becoming more astute step by step in all business capacities to lift performances. AI is widely used in gaming, media, finance, advanced mechanics, quantum science, self-governing vehicles, and clinical diagnosis. Artificial intelligence innovation is a significant essential in a large part of the digital transformation occurring today as organizations position themselves to profit by the always developing measure of information being generated and collected.

AchieversIT familiarize you with the basic terminologies, problem-solving, and learning methods of Artificial Intelligence and also discuss the impact of AI

AchieversIT training on Artificial Intelligence (AI) gives you the basic knowledge of Artificial Intelligence:-

  • Management and Non-technical participants
  • Students who want to learn Artificial Intelligence
  • Professionals in the field of analytics, who wish to make a career switch to AI.
  • Junior/Senior Analysts and Team Leaders.

few prerequisites for pursuing the AI Engineer course are:-

  • Basic Knowledge of statistical tools and techniques
  • Basics of programming languages such as Python
  • Problem-solving skills.
  • Curious mind


  • A system with an Intel i3 processor or above
  • A minimum of 3GB RAM (4GB or above recommended for faster processing)
  • Operating system: 32bit or 64 bits

You will do your assignments/case studies using Jupyter Notebook that is already installed on your Cloud LAB environment (access it from a browser). The access credentials are available on your LMS. Should you have any queries, the 24*7 Support Team will promptly assist you.