Career Direction Ai Engineer
AI Self-Paced Course + Certifications
This Semi Self-Paced Learning + 8hr/Day, 5-Day Doubt Support certification program is designed by AI Engineers from top MNC to meet current market requirements, equipping you with core principles, best practices, and real-world applications.
You’ll receive 12 months of full course access, and your login credentials will be shared within 24 hours of enrollment. Complete the program at your own pace — once you finish, drop us an email at support@careerdirection.co.in to apply for your certification. Your IBM & Career Direction certifications will be delivered within 15–20 days from the date of your email.
- Levels : Beginner | Intermediate | Expert
- Self-Paced
- 12 Months access to courses
- Certifications Career Direction IBM
Why Take a Course With Career Direction?
- Courses: Programs designed by seasoned Subject Matter Experts from top MNC/MANG companies. These courses are built by industry professionals who work closely with real business problems, ensuring the content is practical, up-to-date, and aligned with current industry standards.
- Market Skill: Following the crowd only increases competition. Only skills aligned with company expectations get you opportunities. Generic skills make you one among many, but focused, job-specific skills help you stand out and match what companies are actively looking for in candidates.
- Certification: Earn dual certifications from Career Direction and IBM to enhance your market value. Having certifications from both Career Direction and IBM adds credibility to your profile and increases trust among recruiters during shortlisting.
- Placement: Courses and profiles alone don’t guarantee jobs, as 90% of roles are filled through referrals. Most hiring happens through internal networks and referrals, which means guidance, positioning, and the right connections play a major role in getting job opportunities.
- Practice: Once you’re ready, connect with us via email for certification and referral support. After gaining the required skills and confidence, top performers can reach out to receive certification validation and referral assistance to move forward in their careers.
Curriculum
- Travel Path & Python
- ML - Regression
- ML Classification , Clustering
- DS (Univariate, Bivariate Analysis)
- Adv ML (Feature Selection & Dimensionality Reduction)
- Web Development
- Deep Learning
- Time Series
- NLP + Deep Learning - Gen AI
- ChatGPT API (Generative AI)
- Google Cloud Platform
- AI Agents & AWS SageMaker
- Lang Chain, Lang Graph
Travel Path & Python
- What is AI? How AI Brain Created?
- End Goal of AI, Application of AI
- Comparison Elements AI and HI
- Relationship Between Domains
- Domain Selection – Technique
- Anaconda Installation Process
- Google Colab For Python
- Purpose of Programming Language
- Input Statement Update
- Control Structures – If
- Indentation: If-Else, If-Elif
- If Overview, For Loop
- Range, OOPs, Function, Class
ML - Regression
- Problem Identification in AI
- Supervised | Unsupervised | Semi-
- Classification vs Regression
- Mapping Domains with Learning
- Model Creation – Learning Phase
- Deployment Phase, Algorithms
- Simple & Multi Linear Regression
- SSE/SSR/SST/R-Squared/Adjusted R-Squared
- Train, Test Split, Model Creation
- Support Vector Machine + Standardization
- Decision Tree, Random Forest
- Boosting Algorithm Intro
- GridSearchCV Handson
ML Classification , Clustering
- Introduction to Classification
- Confusion Matrix Explained
- Random Forest Algorithm
- Decision Tree Algorithm
- Support Vector Machine (SVM)
- Logistic Regression,
- K-Nearest Neighbors
- Virtual Environment
- Grid Search / Logistic Regression
- Naive Bayes, Grid Logistic
- K-Means Clustering
- K-Means Code Explanation
- All Clustering Explanations
DS (Univariate, Bivariate Analysis)
- Intro to Data Science
- Types of Analysis, Columns
- Quantitative Univariate Concepts
- Central Tendency, Monte Carlo Tree
- Percentile, Interquartile Range
- Finding, Replacing Outliers
- Frequency, Histogram, Skewness,
- Kurtosis Variance, Normal Distribution
- Cumulative Distribution Function
- Z-Score Application Co-Variance & Correlation
- Multicollinearity, Variance Inflation Factor
- Homo & Heteroscedasticity
- T-Test, Hypothesis, ANOVA
Adv ML (Feature Selection & Dimensionality Reduction)
- Feature Selection, Dimensionality Reduction
- Select-K Algorithm, Select-K Hands-on
- Recursive Feature Elimination
- Advanced ML Flow, Scalar & Vector
- Principal Component Analysis
- Linear Discriminant Analysis
- Adv Techniques: Fit Transform
- Adv Techniques: Fitting Best/Over/Under
- Adv Techniques: Handling Preprocessed Input – Deployment
- Adv Techniques: Preprocessed Input & Output
- Data Visualization: - Zomato Dataset(Seaborn)
- Data Visualization: - Zomato Dataset(Data Studio)
- Data Visualization: - Zomato Dataset(Tableau)
Web Development
- Intro to Web Development
- Create Project & App
- Setup - Must Watch
- HTML Page, GET,POST
- Backend View, Processed Output
- Output Page, App URLs
- Project URLs, Database Comment
- Classification Walkthrough
- User-Based Intuition
- User-Based Hands-on
- Item-Based Recommendation
- Content-Based Recommendation
- Popularity-Based
Deep Learning
- How Computer Understands Images
- Artificial Neural Network
- ANN Visual (3Blue1Brown)
- Gradient Descent
- Backpropagation Calculus
- Convolutional Neural Network
- Explore More Codes
- Pre-Trained Models
- Transfer Learning Matrix
- Deep Learning Flow
- Face Mask Detection-5
- AI App using Pre-Trained Models
- How to Collect Dataset
Time Series
- Intro to Time Series
- Share Market Basics
- Time Series vs Regression
- Components, Assumptions
- Handling Non-Stationarity
- EDA-Stock Data
- Stationarity Check
- Auto-Correlation
- Auto Regression
- Model Creation & Forecasting
- UDF, Moving Average
- MA, ARMA/ARIMA
- SARIMAX, VAR
NLP + Deep Learning - Gen AI
- Intro to NLP + Deep Learning
- WhatsApp Chat Preprocessing
- TF-IDF Vector, Sentiment Analysis
- Topic Modeling, Why Word Embedding
- Word2Vec CBOW, Word2Vec Skip Gram
- Word Embedding Hands-on, Intro to RNN
- Why RNN Fails, Sigmoid/Tanh
- LSTM, Seq2Seq, Transformer Intro
- Input Embedding, Positional Encoding
- Single Head QKV, Single Head Attention
- Multi-Head Attention, Residual Connection
- Problem Statement, Decoder Output
ChatGPT API (Generative AI)
- ChatGPT API Introduction
- OpenAI API & Level-1 Code
- Custom Chatgpt Understanding
- End-User ChatGPT with Shareable Link
- Building Systems with ChatGPT
- BSWC Problem Statement
- BSWC Classification, Moderation
- BSWC Chaining Prompt, Output Check
- Using Gemini & Ollama Installation
- How to Generate Gemini, ChatGPT API
- Real-Time Demo, Realtime Flow
- How to Use OpenAPI Key in Replit
Google Cloud Platform
- Deployment Intro
- Deployment Platforms
- Deployment Procedures
- Flask ML App Localhost
- Environment File
- GCP App Engine Deployment
- Docker Deployment Localhost
- GCP through Docker
- Kubernetes Deployment
- GCP Cloud Build CI/CD
- How CI/CD Changes Deployment
- YAML Explanation
AI Agents (AutoGen)
- Intro to AI Agents
- Agent Types, Why AI Agents
- LLM Models, AgentOps Two-Agent
- Two Agents Using Gemini
- Ollama Local Models
- How to Use Ollama Model
- Two Agents Retrieve User Proxy
- Two Agents – Gemini + Ollama
- AutoGen Documentation Overview
- Multi-Agents – Sequential Chat
- Sequential Chat Hands-on
- Nested Agents Problem Statement
AWS SageMaker AI -I
- AWS Services Intro
- SageMaker AI Intro
- Create S3 Bucket
- Types of Training Jobs
- Training Model via Script
- Deploy Endpoint
- Predict via Endpoint
- Install VS Code
- Predict Outside AWS
- IAM User, Serverless Endpoint
- Deploy Local Model
- Deploy RAG Chatbot
AWS SageMaker AI -II
- S3 Model Deployment Overview
- S3 Model (Hardcoded)
- S3 Model with Secret Key
- S3 Model GitHub EC2 Hosting
- Deployment Architecture
- Lambda Functions
- Replit to Lambda
- RAG Code Explanation
- Configure AWS CLI
- Docker file,Image
- Invoke URL Frontend
- CI/CD,YML GitHub Explanation
Lang Chain, Graph -I
- Purpose of LangChain
- Ecosystem, Version
- LLM Models, Prompt Template
- Multiple Tools
- Web Search Tools
- Initialize Agent, Model with Tools
- Two Agents Retrieve User Proxy
- Agent Intro & Hands-on
- Memory Types First , Last
- LangChain RAG as Tool
- Types of RAG Overview
- Types of Chains
Lang Chain, Graph-II
- Types of Output Parsers
- LangGraph Intro
- Nodes & Edges Hands-on
- Code Explanation
- Simple Flow Execution
- Conditional Edges
- Double Conditional Loop
- Loop, One-Skip Loop
- Career: Resume & Profile Building
- Career: Role & Project Clarity
- Career: Interview & Negotiation
- Career: Placement & Referral
Who is this course for
This course is designed for students, job seekers, and working professionals across all domains who want a clear direction and a strong pathway to elevate their career in the IT industry.
Ideal for anyone looking to build solid tech foundations, improve their job readiness, or validate their skills with guidance from seasoned experts from top MNCs and MANG companies such as IBM, HCL, Kyndryl, and TCS.
Individuals who enroll in our AI-Infused Training Program with Career Counseling sessions gain a unique edge in the market. They benefit from enhanced skills, improved profile visibility, direct company referrals, and massive growth opportunities — making job offers and career advancement just one step away.
Gain Dual Certifications
