Machine Learning Made Easy: Beginner To Expert Journey

Machine Learning, Python Basics, Data Analysis, Exploratory Data Analysis (EDA), Supervised Learning, Unsupervised Learn

What you'll learn

Understand core ML concepts and apply them using Python.

Master key ML algorithms using Python and essential libraries.

Master key ML libraries like NumPy, Pandas, Scikit-learn, and Matplotlib

Learn to preprocess and analyze data for effective model building.

Build practical ML models using real-world datasets.

Requirements

Foundational math skills – Understanding basic algebra

Basic computer literacy – Ability to navigate files, install software

A laptop – Required for coding, running ML models, and practicing hands-on exercises.

Internet connection – Needed for accessing course materials, datasets, and online resources.

Description

This course is designed for individuals with foundational math and computer skills, as well as those at an intermediate and advanced level who want to build a strong understanding of machine learning. You don’t need a high-end laptop—just a willingness to learn! The course includes a crash course on Python basics, covering essential libraries like NumPy, Pandas, Matplotlib, and Scikit-Learn. By the end, you’ll master data analysis, exploratory data analysis, supervised and unsupervised learning, and applying machine learning algorithms to real-world problems. It covers key algorithms such as linear regression, polynomial regression, logistic regression, K-Nearest Neighbors (KNN), K-Means clustering, DBScan, Support Vector Machines (SVM), and anomaly detection.Additionally, this course will help you develop strong problem-solving skills and understand how to interpret data effectively. You will gain hands-on experience through practical examples and real-world case studies, ensuring you can confidently apply machine learning techniques in various domains. Whether you're working on business applications, finance, healthcare, or AI-driven innovations, this course will equip you with the necessary skills to succeed. With step-by-step guidance and interactive exercises, you will build a strong foundation, allowing you to transition into advanced concepts effortlessly. By the end of this training, you'll be able to create, optimize, and evaluate machine learning models with confidence

Overview

Section 1: Introduction

Lecture 1 1_01_Introduction_to_ByteLumina

Lecture 2 1_02_How_to_access_resource

Section 2: 2_Crash_Course_on_Python_and_Google_Colab

Lecture 3 2_01_Introduction_to_Python

Lecture 4 2_02_Getting_Started_with_Google_Colab

Lecture 5 2_03_Basic_operations_in_Google_Colab

Lecture 6 2_04_Basics_of_Python_programming

Lecture 7 2_05_Double_and_single_quote_in_Python

Lecture 8 2_06_Basic_Data_types_and_variables

Lecture 9 2_07_Use_variables_with_F_String

Lecture 10 2_08_Comments_in_Python

Lecture 11 2_09_Lists_and_List_indexing

Lecture 12 2_10_Slicing_of_the_Lists

Lecture 13 2_11_Practicing_List_operations

Lecture 14 2_12_Basic_List_Operations_in_Python

Lecture 15 2_13_Tuple_in_Python

Lecture 16 2_14_Dictionary_in_Python

Lecture 17 2_15_Set_in_Python

Lecture 18 2_16_Immutable_and_Mutable_objects_in_Python

Lecture 19 2_17_Arithmetic_Operators_in_Python

Lecture 20 2_18_Comparison_operator_in_Python

Lecture 21 2_19_Logical_operators_in_Python_(and,_or,_not)

Lecture 22 2_20_Assignment_operators_in_Python

Lecture 23 2_21_Characters_and_strings_in_Python

Lecture 24 2_22_F-strings_in_Python

Lecture 25 2_23_Rules_for_variables_naming

Lecture 26 2_24_How_to_read_error_message

Lecture 27 2_25_Conditional_statements_in_Python

Lecture 28 2_26_Loops_in_Python

Lecture 29 2_27_Functions_in_Python

Lecture 30 2_28_Lambda,_map,_zip_functions_in_Python

Lecture 31 2_29_Methods_in_Python

Lecture 32 2_30_Python_Modules,_built-in_package

Lecture 33 2_31_OS_module_and_file_path

Lecture 34 2_32_File_handling_in_Python

Lecture 35 2_33_Json_format_and_Json_files_management_in_Python

Lecture 36 2_34_CSV_Format_and_CSV_file__handling_in_Python

Lecture 37 2_35_Special_characters_in_Python

Lecture 38 2_36_Exception_management_in_Python

Lecture 39 2_37_Objects_in_Python

Lecture 40 2_38_Python_classes_a_simple_introduction

Lecture 41 2_39_Benefits_of_Using_self_in_Python_Classes

Lecture 42 2_40_Constructors_in_Classes

Lecture 43 2_41_Object-Oriented_Programming

Lecture 44 2_42_PIP_in_Python

Lecture 45 2_43_Using_CoPilot_as_companion_to_write_and_troubleshoot_code

Lecture 46 2_44_End_of_section_Introduction_to_Python_and_Google_Colab

Section 3: 3_Numpy_in_Python

Lecture 47 3_01_Introduction_to_NumPy

Lecture 48 3_02_Import_Numpy_as_np

Lecture 49 3_03_Basic_Array_Creation_in_numpy

Lecture 50 3_04_Array_dimensions,_Shape_and_Size_in_Numpy

Lecture 51 3_05_Data_Types_in_Numpy

Lecture 52 3_06_Pseudo_Data_Generation_in_numpy

Lecture 53 3_07_Explanation_of_examples_created_in_previous_video1

Lecture 54 3_08_Explanation_of_examples_created_in_previous_video2

Lecture 55 3_09_Array_Indexing_and_Slicing

Lecture 56 3_10_Array_Shape_and_Reshape

Lecture 57 3_11_Special_dimension_in_Numpy

Lecture 58 3_12_Handling_Missing_Data_in_Numpy

Lecture 59 3_13_Numpy_performance_comparison

Lecture 60 3_14_Universal_Functions_in_Numpy

Lecture 61 3_15_Where_in_Numpy

Lecture 62 NumPy Cheat Sheet

Lecture 63 Rule of thumb for indexing

Lecture 64 Universal Functions in NumPy

Section 4: 4_Pandas_in_Python

Lecture 65 4_01_Introduction_to_Pandas

Lecture 66 4_02_Accessing_Data,_indexing_and_slicing_in_a_Series

Lecture 67 4_03_Accessing_data_by_Col_label_or_position_in_DataFrame.txt

Lecture 68 4_04_Selecting_data_by_label_in_DataFrame

Lecture 69 4_05_Setting_and_resetting_indexes

Lecture 70 4_06_Boolean_indexing

Lecture 71 4_07_Data_Types_in_Pandas

Lecture 72 4_08_Arithmetic_Operations

Lecture 73 4_09_Reindexing_and_Reshaping

Lecture 74 4_10_Statistics_in_Pandas

Lecture 75 4_11_CSV_and_JSON_file_saving_and_loading

Lecture 76 4_12_Working_with_Dates_and_Times

Lecture 77 4_13_Dates_and_Times_Arithmetic_operations

Lecture 78 4_14_Date_and_Time_Indexing

Lecture 79 4_15_Merging_a_DataFrames

Lecture 80 4_16_Joining_DataFrames

Lecture 81 4_17_Concatenation,_stacking_and_unstacking_a_dataframe

Lecture 82 4_18_Grouping_in_Pandas

Lecture 83 4_19_MultiIndexing

Lecture 84 4_20_Pivot_Tables

Lecture 85 4_21_Groupby_vs_Multiindex_vs_Pivot_Table

Lecture 86 4_22_Mastering_Pandas_str_A_powerful_tool_for_string_operations

Lecture 87 4_23_Regex_in_Pandas

Lecture 88 4_24_Data_Categorization_with_pandas_cut

Lecture 89 4_25_Alternatives_to_Pandas_Vectorized_Operations

Lecture 90 4_26_Panda_iteration

Lecture 91 4_27_Window_Functions_in_Pandas

Lecture 92 4_28_Missing_Data_Handling

Lecture 93 4_29_Where_and_Mask_in_pandas

Lecture 94 4_30_Concluding_the_section

Lecture 95 Pandas Cheat Sheet

Lecture 96 Arithmetic Methods in Pandas

Lecture 97 Comparison Operators

Lecture 98 dt accessor in pandas

Lecture 99 Str accessor in Pandas

Lecture 100 regular expressions

Section 5: 5_Managing_Google_Drive

Lecture 101 5_01_Issues_with_Colab

Lecture 102 5_02_Mounting_google_drive

Lecture 103 5_03_Managing_sessions_in_Google_Colab

Section 6: 6_Matplotlib_and_Seaborn_in_Python

Lecture 104 6_01_Plotting_and_Visualization_in_Python

Lecture 105 6_02_Line_Plot

Lecture 106 6_03_Bar,_Scatter,_area_and_pie_plots

Lecture 107 6_04_Histogram,_box,_Kernel_density_Estimate_plot

Lecture 108 6_05_Figure,_subplot_and_subplots

Lecture 109 6_06_Colors,_Markers,_and_Line_Styles_in_Matplotlib

Lecture 110 6_07_Tick,_labels_and_legends

Lecture 111 6_08_Annotation_and_drawings_on_Subplot

Lecture 112 6_09_Saving_plots_to_file

Lecture 113 6_10_Introduction_to_Seaborn

Lecture 114 Matplotlib cheat sheet

Lecture 115 Seaborn Cheat Sheet

Section 7: 7_Exploratory_Data_Analysis_(EDA)

Lecture 116 7_01_EDA_and_Kaggle

Lecture 117 7_02_Getting_First_Dataset

Lecture 118 7_03_Loading_data_in_Colab_and_Sales_Price_Analysis

Lecture 119 7_04_Normal_distribution,_Data_Skewness_and_kurtosis

Lecture 120 7_05_Data_outliers

Lecture 121 7_06_Features_of_a_dataset

Lecture 122 7_07_Helper_function_explained1

Lecture 123 7_08_Helper_function_explained2

Lecture 124 7_09_Preparation_to_analyze_Features

Lecture 125 7_10_Features_analysis_1

Lecture 126 7_11_Features_analysis_Null_value_management

Lecture 127 7_12_Features_correlation

Lecture 128 7_13_Numeric_features_Missing_value_handling

Lecture 129 7_14_The_Limitations_of_EDA_When_Intuition_and_Data_Disagree

Lecture 130 7_15_Analyzing_Datetime_features

Lecture 131 7_16_Ordinal_features_analysis

Lecture 132 7_17_Anova_(Analysis_of_Variance)_for_Nominal_Features

Lecture 133 7_18_Nominal_Features_analysis

Lecture 134 7_19_Data_Analysis_Final_words

Section 8: 8_Introduction_to_Machine_Learning

Lecture 135 8_01_What_is_Machine_learning

Lecture 136 8_2_Types_of_Machine_Learning_Supervised_Unsupervised_and_Reinforcement_Learning

Lecture 137 8_03_Applications_of_Machine_learning

Lecture 138 8_04_Linear_Regression_Introduction

Lecture 139 8_05_Understanding_Scalars,_Vectors,_Matrices,_and_Tensors

Lecture 140 8_06_Linear_regression_in_Machine_Learning

Lecture 141 8_07_Introduction_to_cost_function_Mean_Squared_Error_(MSE)

Lecture 142 8_08_Key_Parameters_or_coefficient_Slope_w_and_y-intercept_b

Lecture 143 8_09_Introduction_to_Gradient_descent_algorithm

Lecture 144 8_10_What_is_Gradient_Descent

Lecture 145 8_11_Learning_rate

Lecture 146 8_12_Feature_Scaling

Lecture 147 8_13_Data_set_split_between_train,_test_and_validation_data

Lecture 148 8_14_Multi_variable_regression

Lecture 149 8_15_Dot_product

Lecture 150 8_16_Explanation_of_dot_product_functions

Lecture 151 8_17_House_price_prediction_with_full_application

Lecture 152 8_18_Exploring_Feature_Scaling_Methods

Lecture 153 8_19_Polynomial_regression_and_Feature_Engineering

Lecture 154 8_20_Vectorization_of_Equations

Lecture 155 8_21_Saving_numeric_features

Section 9: 9_Intro_to_sklearn_Scikit-Learn

Lecture 156 9_01_Introduction_to_Scikit-learn

Lecture 157 9_02_Dataset_split_in_train_and_test

Lecture 158 9_03_Data_scaling_with_sklearn

Lecture 159 9_04_Arrays_comparison

Lecture 160 9_05_Regression_analysis_for_single_variable

Lecture 161 9_06_Shape_of_the_vector

Lecture 162 9_07_Multivariable_linear_regression

Lecture 163 9_08_Result_analysis

Section 10: 10_Logistic_regression_and_classification

Lecture 164 10_01_Introduction_to_classification

Lecture 165 10_02_Logistic_regression

Lecture 166 10_03_Decision_boundary

Lecture 167 10_04_Loss_function_for_logistic_regression

Lecture 168 10_05_Cost_function_and_Gradient_Descent_for_logistic_regression

Lecture 169 10_06_Titanic_Survival_Insights_Data_download

Lecture 170 10_07_One_Hot_Encoding

Lecture 171 10_08_Data_preparation_for_model

Lecture 172 10_09_Model1_for_binary_classification

Lecture 173 10_10_Accuracy_and_Cost_for_classification

Lecture 174 10_11_Simplifying_Logistic_Regression_with_Scikit-Learn

Section 11: 11_Model_performance_validation

Lecture 175 11_01_Train,_Test_and_Validation_split

Lecture 176 11_02_K-fold_cross_validation

Section 12: 12_Confusion_Matrix

Lecture 177 12_01_Unbalanced_data_problem

Lecture 178 12_02_Confusion_matrix

Lecture 179 12_03_Precision,_Recall_and_F1-Score

Lecture 180 12_04_Classification_report

Section 13: 13_K-Nearest_Neighbors_(KNN)

Lecture 181 13_01_Introduction_to_K-Nearest_Neighbors

Lecture 182 13_02_Euclidean_Distance_formula

Lecture 183 13_03_KNN_algorithm

Section 14: 14_Model_Overfitting_and_Underfitting_problem

Lecture 184 14_01_Overfitting_and_underfitting

Lecture 185 14_02_Addressing_over_or_under_fitting

Lecture 186 14_03_Reguralization

Section 15: 15_SVM_Support_Vector_Machine

Lecture 187 15_01_Introduction_to_Support_vector_machine

Lecture 188 15_02_Key_terms_for_SVM

Lecture 189 15_03_Support_vector_classification_(SVC)

Lecture 190 15_04_Randomized_Search

Lecture 191 15_05_Grid_search

Lecture 192 15_06_Support_vector_regression_(SVR)

Section 16: 16_Decision_Tree_Random_Forest_and_XGBoost

Lecture 193 16_01_Introduction_to_decision_trees

Lecture 194 16_02_Entropy

Lecture 195 16_03_Information_gain

Lecture 196 16_04_Gini_Index

Lecture 197 16_05_Decision_tree_classifier

Lecture 198 16_06_Decision_Tree_Regression

Lecture 199 16_07_Ensemble_multiple_decision_trees

Lecture 200 16_08_Bootstrapping_sampling_with_replacement

Lecture 201 16_09_Random_forest

Lecture 202 16_10_XGBoost

Lecture 203 16_11_Coefficient_of_determination

Lecture 204 16_12_Models_performance_comparison

Section 17: 17_K_mean_clustering_Unsupervised_Learning

Lecture 205 17_01_Introduction_to_clustering

Lecture 206 17_02_K-Mean_Clustering_algorithm

Lecture 207 17_03_K-Mean_Clustering_algorithm_formula

Lecture 208 17_04_Data_Preparation_for_clustering

Lecture 209 17_05_K_mean_clustering_with_sklearn

Section 18: 18_DBScan

Lecture 210 18_01_Introduction_to_DBScan

Lecture 211 18_02_Application_of_DBScan

Section 19: 19_Anomaly_detection_Finding_unusual_events

Lecture 212 19_01_Introduction_to_anomaly_detection

Lecture 213 19_02_Gaussian_normal_distribution_for_anomaly_detection

Lecture 214 19_03_The_dataset_for_anomaly_detection

Lecture 215 19_04_Anomaly_detection_algorithm

Section 20: 20_Final_words

Lecture 216 20_01_Next_steps

Anyone eager to learn machine learning with foundational math and IT skills.,Business executives seeking insights into AI and data-driven decision-making,Beginner developers looking to build a strong foundation in machine learning.,Machine learning enthusiasts eager to explore practical applications.,Technical professionals wanting to enhance their analytical skills.,Project managers aiming to understand ML-driven solutions.,Product managers interested in leveraging ML for business strategy.,Students preparing for careers in data science, AI, or related fields.