Course Overview

Machine Learning (ML) is the backbone of Artificial Intelligence, powering everything from recommendation engines to fraud detection, self-driving cars, and speech recognition. With industries adopting AI and automation, Machine Learning skills are in high demand worldwide.

At Techxeeria Technologies Pvt. Ltd., our Machine Learning Training with Python is designed to help students and professionals master the foundations of ML, advanced algorithms, and real-world applications in just 6 months. The training covers Python programming, Math for ML, Supervised & Unsupervised learning, Deep Learning basics, and deployment, with hands-on projects and capstone assignments.

Why Choose Machine Learning Training?

• Learn Python for Machine Learning (from basics to advanced) 
• Build strong foundations in Math, Probability & Statistics for ML 
• Master Supervised & Unsupervised ML algorithms 
• Explore Deep Learning with TensorFlow & Keras 
• Work on real-world ML projects and case studies 
• Gain skills for roles like ML Engineer, Data Scientist, AI Engineer, and Python Developer 

6-Month Machine Learning Syllabus

Month 1 – Python for Machine Learning + Math Basics

Week 1–2: Python Basics for ML

• Day 1: Introduction to ML & AI (Primary Keyword: Machine Learning Training with Python) 
• Day 2: Python Environment Setup (Jupyter, Anaconda) 
• Day 3: Data Types, Variables, Operators 
• Day 4: Control Flow (Loops, Conditions) 
• Day 5: Functions & Modules 
• Day 6: Lists, Tuples, Dictionaries, Sets 
• Day 7: Practice + Mini Project 

Week 3–4: Math & Probability Basics

• Day 8: Linear Algebra (Vectors, Matrices, Matrix Multiplication) 
• Day 9: Probability & Distributions 
• Day 10: Statistics (Mean, Median, Variance, SD) 
• Day 11: Correlation & Covariance 
• Day 12: Hypothesis Testing 
• Day 13: Bayes Theorem & Probability in ML 
• Day 14: Mini Project – Basic Stats on Dataset 

Month 2 – Data Handling & Visualization

Week 5–6: NumPy & Pandas

• Day 15: NumPy Basics – Arrays, Operations 
• Day 16: Indexing, Slicing, Broadcasting 
• Day 17: Pandas Series & DataFrames 
• Day 18: Data Cleaning – Missing Values 
• Day 19: Data Transformation – Merge, GroupBy, Pivot 
• Day 20: Feature Engineering Basics 
• Day 21: Mini Project – Sales Dataset Cleaning 

Week 7–8: Data Visualization

• Day 22: Matplotlib Basics 
• Day 23: Histograms, Pie, Line, Bar Charts 
• Day 24: Seaborn – Pairplots, Heatmaps 
• Day 25: Advanced Seaborn Plots 
• Day 26: Plotly for Interactive Graphs 
• Day 27: Case Study – Exploratory Data Analysis (EDA) 
• Day 28: Mini Project – COVID Data Visualization 

Month 3 – Supervised Machine Learning

Week 9–10: Regression Models

• Day 29: Introduction to ML – Supervised vs Unsupervised 
• Day 30: Linear Regression (Simple & Multiple) 
• Day 31: Polynomial Regression 
• Day 32: Ridge & Lasso Regression 
• Day 33: Logistic Regression (Binary Classification) 
• Day 34: Multi-class Logistic Regression 
• Day 35: Mini Project – House Price Prediction 

Week 11–12: Classification Models

• Day 36: Decision Trees 
• Day 37: Random Forests 
• Day 38: K-Nearest Neighbors (KNN) 
• Day 39: Naïve Bayes Classifier 
• Day 40: Support Vector Machine (SVM) 
• Day 41: Model Evaluation Metrics (Accuracy, Precision, Recall, F1, AUC) 
• Day 42: Mini Project – Customer Churn Prediction 

Month 4 – Unsupervised ML & Feature Engineering

Week 13–14: Clustering Models

• Day 43: Introduction to Unsupervised Learning 
• Day 44: K-Means Clustering 
• Day 45: Hierarchical Clustering 
• Day 46: DBSCAN Clustering 
• Day 47: Evaluation of Clusters (Silhouette Score) 
• Day 48: Case Study – Market Segmentation 
• Day 49: Mini Project – Mall Customer Segmentation 

Week 15–16: Dimensionality Reduction & Feature Handling

• Day 50: Feature Scaling (Standardization & Normalization) 
• Day 51: PCA (Principal Component Analysis) 
• Day 52: LDA (Linear Discriminant Analysis) 
• Day 53: Feature Selection Techniques 
• Day 54: Outlier Detection & Handling 
• Day 55: Imbalanced Data Handling (SMOTE, Undersampling) 
• Day 56: Mini Project – Credit Card Fraud Detection 

Month 5 – Advanced ML & Ensemble Learning

Week 17–18: Ensemble Methods

• Day 57: Bagging & Random Forests 
• Day 58: Boosting Techniques (AdaBoost, Gradient Boosting) 
• Day 59: XGBoost 
• Day 60: LightGBM & CatBoost 
• Day 61: Stacking Models 
• Day 62: Model Tuning – GridSearchCV, RandomSearchCV 
• Day 63: Mini Project – Loan Default Prediction 

Week 19–20: Model Deployment & Real-World Use Cases

• Day 64: Cross Validation & K-Fold CV 
• Day 65: Overfitting vs Underfitting 
• Day 66: Regularization in Models 
• Day 67: Saving Models (Pickle, Joblib) 
• Day 68: Model Deployment with Flask 
• Day 69: Model Deployment on Streamlit 
• Day 70: Mini Project – ML Model Web App 

Month 6 – Deep Learning Basics + Final Projects

Week 21–22: Deep Learning with Python

• Day 71: Introduction to Neural Networks (Secondary Keyword: Deep Learning with Python) 
• Day 72: Perceptron & Activation Functions 
• Day 73: Forward & Backpropagation 
• Day 74: Building Neural Network with TensorFlow/Keras 
• Day 75: CNN – Convolutional Neural Networks (Image Classification) 
• Day 76: RNN – Recurrent Neural Networks (Text/Time Series) 
• Day 77: Mini Project – Handwritten Digit Recognition 

Week 23–24: Capstone Projects & Interview Prep

• Day 78–82: Capstone Project 1 – EDA + ML Model (Supervised) 
• Day 83–87: Capstone Project 2 – ML + Deep Learning Project 
• Day 88–89: Resume Building + GitHub Portfolio Setup 
• Day 90: Mock Interviews & Final Assessment 

Hands-On Training

• Mini Projects in every module (Python, ML, DL) 
• 2 Major Capstone Projects for portfolio building 
• Resume & GitHub Portfolio setup 
• Mock Interviews & Job Preparation 

Who Can Join?

• Beginners aspiring to become Machine Learning Engineers 
• Python learners looking to master ML & AI 
• Data Science enthusiasts seeking ML project experience 
• Students preparing for placements in AI/ML companies 

Career Opportunities After Course

• Machine Learning Engineer 
• Data Scientist 
• AI Engineer 
• Data Analyst 
• Research Engineer