Artificial Intelligence & Machine Learning 1 Year (52 Weeks) 100% Placement Support

AI / ML (Artificial Intelligence & Machine Learning) Training Program

8 months of hands-on technical training in Python for Data Science, Statistics, Machine Learning, Deep Learning, NLP, Generative AI, and MLOps, followed by 4 months of English communication & professional skills to prepare for global AI/ML Engineering roles.

5.0 (Verified Reviews)
72 learners trained
Hybrid / Live Labs
The Dual-Track Edge

Why Our 1-Year Program Gets You Hired

Most coaching institutes teach either code or theory. At BE Career Academy, we combine 8 months of deep technical mastery with 4 months of corporate English communication, incident bridge-call handling, and MNC interview defense.

1 Phase 1: 8 Months Technical Rigor
  • Weeks 1–32 hands-on production lab exercises
  • Live cloud sandboxes, CLI admin & IaC
  • End-of-phase capstone architecture defense
2 Phase 2: 4 Months Corporate Communication
  • Weeks 33–52 client bridge & stand-up simulation
  • Root Cause Analysis (RCA) & Spec writing
  • Live HR & Technical MNC mock rounds

Tools & Competencies You Will Master

Python NumPy & Pandas Scikit-Learn PyTorch & TensorFlow Computer Vision (YOLO) NLP & Transformers Generative AI & LLMs LangChain & RAG FastAPI & MLOps
Detailed 52-Week Syllabus

Curriculum Breakdown

Explore each week's specific hands-on modules and deliverables.

Phase 1: Technical Training (8 Months / Weeks 1–32)

Week 1
Python Refresher for AI – Data Structures, List Comprehensions, Lambda, OOP
Week 2
Numerical Computing with NumPy – Arrays, Vectorization, Broadcasting, Matrix Operations
Week 3
Data Manipulation with Pandas – DataFrames, Series, Cleaning, Merging, GroupBy
Week 4
Data Visualization – Matplotlib & Seaborn (Distributions, Correlations, Heatmaps)
Week 5
Linear Algebra & Calculus for ML – Vectors, Matrices, Eigenvalues, Derivatives, Gradients
Week 6
Probability & Descriptive Statistics – Distributions, Central Limit Theorem, Bayes' Theorem
Week 7
Inferential Statistics – Hypothesis Testing, p-values, A/B Testing, ANOVA, Chi-Square
Week 8
Practical Lab – End-to-End Exploratory Data Analysis (EDA) & Feature Engineering on Real Datasets
Week 9
Introduction to Machine Learning – Types of ML, Train-Test Split, Cross-Validation, Overfitting
Week 10
Linear Regression – Simple, Multiple, Polynomial, Gradient Descent, Ridge & Lasso (L1/L2)
Week 11
Classification Part 1 – Logistic Regression, Confusion Matrix, Precision, Recall, F1, ROC-AUC
Week 12
Classification Part 2 – K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Naive Bayes
Week 13
Tree-Based Models & Ensembles – Decision Trees, Random Forests, Bagging
Week 14
Boosting Algorithms – AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost
Week 15
Unsupervised Learning – K-Means, Hierarchical Clustering, DBSCAN & PCA (Dimensionality Reduction)
Week 16
Practical Lab – Building, Tuning (GridSearchCV/Optuna), and Evaluating End-to-End Scikit-Learn Pipelines
Week 17
Neural Network Fundamentals – Perceptrons, Activation Functions, Loss Functions, Backpropagation
Week 18
Deep Learning Frameworks – TensorFlow, Keras & PyTorch Tensors, Autograd, Training Loops
Week 19
Regularization & Optimization – Dropout, Batch Normalization, Adam, RMSprop
Week 20
Computer Vision – Convolutional Neural Networks (CNNs), Transfer Learning (ResNet, YOLO Object Detection)
Week 21
NLP Fundamentals – Text Preprocessing, Tokenization, Stemming, Lemmatization, TF-IDF
Week 22
Word Embeddings – Word2Vec, GloVe, FastText, Semantic Similarity
Week 23
Sequence Models – Recurrent Neural Networks (RNNs), LSTMs, GRUs for Time Series & Text
Week 24
Practical Lab – Building a Sentiment Analysis & Real-Time Object Detection Application
Week 25
Transformer Architecture – Self-Attention Mechanism, Encoder-Decoder, BERT & GPT Internals
Week 26
Working with Hugging Face – Pre-trained Models, Pipelines, Tokenizers, Fine-Tuning Basics (LoRA/PEFT)
Week 27
Generative AI & Prompt Engineering – OpenAI / Gemini / Open-Source LLMs (Ollama), LangChain Basics
Week 28
Retrieval-Augmented Generation (RAG) – Vector Databases (Chroma/FAISS/Pinecone), Embeddings & AI Agents
Week 29
Model Deployment – Wrapping ML/DL Models in REST APIs using FastAPI & Streamlit UI
Week 30
Containerizing AI Apps – Dockerizing ML Models, Model Versioning with MLflow & DVC
Week 31
Cloud AI & Responsible AI – AWS SageMaker Overview, Model Drift, AI Ethics, Bias Mitigation
Week 32
Final Technical Mock Tests – End-to-End GenAI/ML Capstone Project Deployment & Evaluation

Phase 2: English Communication & Professional Skills (4 Months / Weeks 33–52)

Week 33
Grammar & Sentence Structure for Professional Writing
Week 34
Vocabulary Building for Data Science, AI Research & Business Analytics Contexts
Week 35
Active Listening & Translating Business Problems into AI/ML Objectives
Week 36
Writing Practice – Model Cards, Experiment Logs, Executive Summaries
Week 37
PowerPoint Skills – Data Storytelling & Visualizing Model Insights for Stakeholders
Week 38
Presentation Delivery Techniques – Voice, Body Language, Confidence
Week 39
Interpersonal Communication – Collaborating with Domain Experts, Data Engineers & Product Teams
Week 40
Handling Difficult Conversations – Explaining Model Limitations, False Positives & ROI
Week 41
Professional Grooming – Dress Code, Email Signatures, Corporate Culture
Week 42
Consulting & Stakeholder Etiquette – Managing AI Expectations, Clear Non-Jargon Explanations
Week 43
Project Handover & MLOps Pipeline Documentation Communication
Week 44
Professional Writing Lab – Whitepapers, Technical Blogs & Research Summaries
Week 45
Resume Writing – AI/ML Engineer & Data Scientist Resume Templates
Week 46
Cover Letters, LinkedIn, GitHub, Kaggle & Hugging Face Profile Optimization
Week 49
Group Discussions – Generative AI Ethics, Automation Impact & Industry Case Studies
Week 50
Live Scenario Tests – Timed Dataset Modeling, Debugging Neural Networks & RAG Pipelines
Week 51
Final Review – Technical + Soft Skills Assessment
Week 52
Career Guidance & Job Placement Support
Pedagogy & Methodology

Teaching & Learning Approach

Interactive Learning

Role-plays, debates, case studies, real-world simulations

Assessment

Weekly quizzes, monthly progress tests, mid-term & final comprehensive exams

Digital Tools

Podcasts, online articles, business reports, pronunciation tools

Feedback

Regular one-on-one coaching & progress reports

Graduate Readiness

End-of-Course Outcomes

By the end of 52 weeks, learners will have built the competence and confidence to:

Perform advanced data wrangling, statistical analysis, and feature engineering using Python.
Train, tune, and evaluate supervised, unsupervised, and deep learning models using Scikit-Learn and PyTorch/TensorFlow.
Build modern Generative AI applications, RAG pipelines, and LLM workflows using LangChain and Vector Databases.
Deploy production-grade AI models as scalable APIs using FastAPI, Docker, and MLOps tools.
Communicate complex data insights clearly through compelling storytelling to business stakeholders.
Successfully clear MNC interviews and excel as AI/ML Engineers or Data Scientists.

Who Should Enroll?

  • Final year B.Tech, BCA, MCA, B.Sc students
  • Recent graduates seeking their first IT job
  • IT support & non-tech professionals transitioning roles
  • Professionals aiming for MNC & remote global roles

Academy Career Support

  • 100% Placement assistance with hiring partners
  • Dedicated resume, LinkedIn & GitHub optimization
  • Unlimited 1-on-1 mock interviews (HR & Technical)
  • Lifetime alumni community & lab sandbox access
Full 1-Year Program Tuition
Contact for Fees

Custom installment plans, merit scholarships & flexible fee structures available upon consultation.

Talk to our admissions counselor for fee breakdown
Total Duration 52 Weeks (1 Year)
Mode of Study Hybrid / Online Interactive
Batch Options Weekday & Weekend Batches
Certification Industry Recognised Certificate
Placement Support 100% Dedicated Assistance
Have questions about eligibility or fees? Call Senior Counselor: +91 8282 061 414

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